{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from neo import Spike2IO\n", "\n", "import numpy as np\n", "import scipy \n", "import h5py\n", "from scipy import stats\n", "import matplotlib.pyplot as plt\n", "import matplotlib.axes as ax\n", "from pathlib import Path\n", "import pandas as pd\n", "import seaborn as sns\n", "import seaborn.distributions as sd\n", "from seaborn.palettes import color_palette, blend_palette, dark_palette, light_palette\n", "import statsmodels.api as sm\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "def get_range_bounds(win_times,trigger):\n", " start = np.min(np.where(trigger>win_times[0])[0])\n", " stop= np.max(np.where(triggert1)&(analevent= np.min(tin)) & (x <= np.max(tin))]\n", " dx = np.sort(np.diff(np.sort(x)))\n", " dt_samp = dx[np.nonzero(dx)][0]\n", " if dt_samp > np.min(np.diff(tin)):\n", " t = np.linspace(np.min(tin), np.max(tin), np.min([int(np.ceil(T / dt_samp)), int(1e3)]))\n", " else:\n", " t = tin\n", "\n", " # calculate delta t\n", " dt = min(np.diff(t))\n", "\n", " # create the finest histogram\n", " thist = np.concatenate((t, (t[-1]+dt)[np.newaxis]))\n", " y_hist = np.histogram(x_ab, thist-dt/2)[0]\n", " N = sum(y_hist).astype(np.float)\n", " y_hist = y_hist / N / dt\n", "\n", " # always provide W\n", " C = np.zeros((1, len(W)))[0]\n", " C_min = np.Inf\n", " ymat=[]\n", " for k, w in enumerate(W):\n", " C[k], yh = CostFunction(y_hist, N, w, dt)\n", " ymat.append(yh)\n", " if((C[k] < C_min).any()):\n", " C_min = C[k]\n", " optw = w\n", " y = yh\n", " ymat = np.asarray(ymat) \n", " \n", " return y, t, optw, C, C_min\n", "\n", "def spiketimes_align(trigger_times,data_times,trigger,trial_duration):\n", " aligned_times = []\n", " for t in trigger_times:\n", " thesetimes = data_times[\n", " np.where((data_times>(trigger[t]))&(data_times<(trigger[t]+trial_duration)))[0]\n", " ]-(trigger[t])\n", " aligned_times = np.concatenate((aligned_times,thesetimes)) \n", "\n", " return np.asarray(aligned_times)\n", "\n", "def get_optw(these_trials,spikes,xtime,trigger,trial_duration):\n", " W=np.arange(0.0065,0.3,0.0005)\n", " aligned_times = spiketimes_align(these_trials,spikes,trigger,trial_duration)\n", " y, t, optw, C, C_min = sskernel(aligned_times, xtime, W)\n", " \n", " return y, t, optw, C, C_min\n", "\n", "def get_response_hist(trigger_times,data_times,binw,trigger,trial_duration):\n", " #binw in seconds\n", " aligned_times = spiketimes_align(trigger_times,data_times,trigger,trial_duration)\n", " nbins = np.trunc(trial_duration/binw) #only look at full bins or else last bin would underestimate rate\n", " r = np.histogram(aligned_times,np.linspace(0,nbins*binw,nbins+1))\n", " yh = r[0]/len(trigger_times)/binw\n", " xc = r[1]-np.mean(np.diff(r[1]))\n", " xc = xc[1:] # [0:len(yh)]\n", " \n", " return yh, xc\n", "\n", "def get_response(trigger_times,data_func,trial_duration,tau):\n", " response = [data_func(np.linspace(t,t+trial_duration,int(trial_duration/dt))) for t in trigger_times]\n", " return np.asarray(response)\n", " \"\"\"\n", " Creates a spike density function from a spike train to do continuous analysis with (such as trial correlations)\n", " \"\"\" \n", "\n", "def filtered_response(spk_times, tau):\n", " \"\"\"\n", " Creates a function with a gaussian centered at every spike time (the mean) with standard deviation tau\n", " normalized by tau to estimate spike rate\n", " \"\"\"\n", " spk_times = spk_times.reshape((-1, 1))\n", " norm_factor = tau * np.sqrt(2. * np.pi)\n", " \n", " return lambda t: np.sum(np.exp(-(spk_times - t.reshape((1, -1))) ** 2 / (2 * tau * tau)), 0) / norm_factor\n", " \n", "\n", "def _kde_support(data, bw, gridsize, cut, clip):\n", " \"\"\"Establish support for a kernel density estimate.\"\"\"\n", " support_min = max(data.min() - bw * cut, clip[0])\n", " support_max = min(data.max() + bw * cut, clip[1])\n", " return np.linspace(support_min, support_max, gridsize)\n", "\n", "# function to calculate Cohen's d for independent samples\n", "def cohend(d1, d2):\n", " # calculate the size of samples\n", " n1, n2 = len(d1), len(d2)\n", " # calculate the variance of the samples\n", " s1, s2 = np.var(d1, ddof=1), np.var(d2, ddof=1)\n", " # calculate the pooled standard deviation\n", " s = np.sqrt(((n1 - 1) * s1 + (n2 - 1) * s2) / (n1 + n2 - 2))\n", " # calculate the means of the samples\n", " u1, u2 = np.mean(d1), np.mean(d2)\n", " # calculate the effect size\n", " return (u1 - u2) / s" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "path_to_file = Path.cwd()\n", "file_to_open = path_to_file / 'DataMat.csv'\n", "datamat = pd.read_csv(file_to_open)\n", "datamat = datamat.loc[:, ~datamat.columns.str.contains('^Unnamed')]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# afferent_proprio=['20070718_598_coupled_SPK.mat',\n", "# '20070719_91_coupled_SPK.mat',\n", "# '20070723_287_SPK.mat',\n", "# '20070723_311_SPK.mat',\n", "# '20070723_339_SPK.mat',\n", "# '20070723_415_SPK.mat'\n", "# ]\n", "# df_aff_proprio = datamat[datamat['exptname'].isin(afferent_proprio)]#.assign(what='aff_proprio',cell='afferent')\n", "\n", "# afferent_free=['20180407_freerun2_SPK.mat',\n", "# '20180407_freerun3_SPK.mat',\n", "# '20180407_freerun4_SPK.mat',\n", "# '20180407_freerun5_SPK.mat'\n", "# ]\n", "# df_aff_free = datamat[datamat['exptname'].isin(afferent_free)]#.assign(what='aff_free',cell='afferent')\n", "\n", "# aen_vent=['20050727_3224_SPK.mat',\n", "# '20050728_3404_latency2_SPK.mat',\n", "# '20050808_3629_latency2_SPK.mat',\n", "# '20050811_3221_SPK.mat',\n", "# '20050818_2874_SPK.mat',\n", "# '20050819_2601_SPK.mat',\n", "# '20050820_2960_trial1_SPK.mat',\n", "# '20050824_2627_trial1_SPK.mat',\n", "# '20060626_2815_SPK.mat',\n", "# '20060627_2673_SPK.mat',\n", "# '20060708_3004_SPK.mat',\n", "# '20060710_2767_SPK.mat',\n", "# '20060710_3196_SPK.mat',\n", "# '20060804_2107_SPK.mat',\n", "# '20060812_2921_SPK.mat',\n", "# '20060812_3066_SPK.mat',\n", "# '20060812_3205_SPK.mat',\n", "# '20060816_3893_SPK.mat',\n", "# '20071025_3335_SPK.mat',\n", "# '20071026_3108_SPK.mat',\n", "# ]\n", "# df_aen_vent = datamat[datamat['exptname'].isin(aen_vent)]#.assign(what='aen_vent',cell='aen')\n", "\n", "# aen_proprio=['20070816_3375_SPK.mat',\n", "# '20070816_3509_SPK.mat',\n", "# '20070720_3352_SPK.mat',\n", "# '20070727_3798_SPK.mat',,\n", "# '20070808_3727_SPK.mat'\n", "# '20070816_3838_SPK.mat'\n", "# ]\n", "# df_aen_proprio = datamat[datamat['exptname'].isin(aen_proprio)]#.assign(what='aen_proprio',cell='aen')\n", "\n", "# aen_ventcmd=['20170810_expt4_vent_SPK.mat',\n", "# '20170810_expt5_vent_SPK.mat',\n", "# '20170815_expt8_vent_SPK.mat',\n", "# '20170815_expt9_vent_SPK.mat'\n", "# ]\n", "# df_aen_ventcmd = datamat[datamat['exptname'].isin(aen_ventcmd)]#.assign(what='aen_ventcmd',cell='aen')\n", "\n", "# aen_swimcmd=['20180124_expt1_swim_SPK.mat',\n", "# '20180124_expt2_swim_SPK.mat',\n", "# '20170815_expt10_swim_SPK.mat'\n", "# ]\n", "# df_aen_swimcmd = datamat[datamat['exptname'].isin(aen_swimcmd)]#.assign(what='aen_swimcmd',cell='aen')\n", "\n", "# aen_test = ['20050824_2627_trial1_SPK.mat',\n", "# '20070727_3798_SPK.mat',\n", "# '20071025_3335_SPK.mat',\n", "# '20071026_3108_SPK.mat',\n", "# '20070801_3838_SPK.mat'\n", "# ]\n", "# df_aen_test = datamat[datamat['exptname'].isin(aen_test)]#.assign(what='aen_test',cell='aen')\n", "\n", "# aen_control = ['20050824_2627_control_SPK.mat',\n", "# '20070727_3798_control_SPK.mat',\n", "# '20071025_3335_control_SPK.mat',\n", "# '20071026_3108_control_SPK.mat',\n", "# '20070801_3838_control_SPK.mat'\n", "# ]\n", "# df_aen_control = datamat[datamat['exptname'].isin(aen_control)]#.assign(what='aen_control',cell='aen')\n", "\n", "# aen_latency1 = ['20050728_3404_latency1_SPK.mat',\n", "# '20050808_3629_latency1_SPK.mat'\n", "# ]\n", "# df_aen_latency1 = datamat[datamat['exptname'].isin(aen_latency1)]#.assign(what='aen_latency1',cell='aen')\n", "\n", "# aen_latency2 = ['20050728_3404_latency2_SPK.mat',\n", "# '20050808_3629_latency2_SPK.mat'\n", "# ]\n", "# df_aen_latency2 = datamat[datamat['exptname'].isin(aen_latency2)]#.assign(what='aen_latency2',cell='aen')\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "######## add columns for calculated changes in spike rate relative to prestimulus rates\n", "alldf = datamat\n", "alldf['spkrt_change_stimi_base'] = alldf['spkrt_stimi']-alldf['spkrt_base']\n", "alldf['spkrt_change_stimf_base'] = alldf['spkrt_stimf']-alldf['spkrt_base']\n", "alldf['spkrt_change_stimf_stimi'] = alldf['spkrt_stimf']-alldf['spkrt_stimi']\n", "alldf['spkrt_change_post_base'] = alldf['spkrt_post']-alldf['spkrt_base']\n", "alldf['spkrt_change_recover_base'] = alldf['spkrt_recovery']-alldf['spkrt_base']\n", "alldf['spkrt_change_post_recover'] = alldf['spkrt_recovery']-alldf['spkrt_post']\n", "alldf['sp_change'] = alldf['sp_poststim_mean']- alldf['sp_basestim_mean']\n", "alldf['sp_change_recover'] = alldf['sp_recoverstim_mean']- alldf['sp_basestim_mean']\n", "\n", "alldf = alldf[alldf.exptname!='20180407_freerun2_SPK.mat']\n", " #this afferent does not seem modulated by stimulus; \n", " #include only afferents that were modulated by mechanosensory stimuli as per Methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In revised manuscipt excluding motor command experimental conditions because sample size too small to stand alone and to determine if can be grouped with other conditions or not." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "alldf = alldf[(alldf.condition!='aen_swimcmd')&(alldf.condition!='aen_ventcmd')]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Throughout each experiment in the paired condition, trial onset was triggered by one of two signals associated with ventilation or swimming movements (Fig. 2B; as described in Methods). Based on previous literature in the electrosensory system, proprioceptive signals and motor commands associated with both ventilation and proprioceptive signals and sensory feedback signals associated with fin movements are expected to provide a basis for the generation of cancellation signals (Schmidt and Bodznick, 1987; Montgomery et al. 1996; ref from DON? refs). The results obtained under these two conditions were statistically not significant from each other and have been combined for all analyses (an example cell from each condition is shown in Fig. 3A&B).
\n", "STATS as follows for main results of study (1) changes in poststimulus spike rate and (2) correlation to stimulus: " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1)\n", "MannwhitneyuResult(statistic=56.0, pvalue=0.35231137054800765)\n", "(2)\n", "MannwhitneyuResult(statistic=56.0, pvalue=0.35231137054800765)\n" ] } ], "source": [ "sr_v = alldf[(alldf['condition']=='aen_vent')&(alldf['phase']=='phase1')&(alldf['trigger_type']=='paired')]['spkrt_change_post_base'].values\n", "sr_fl = alldf[(alldf['condition']=='aen_proprio')&(alldf['phase']=='phase1')&(alldf['trigger_type']=='paired')]['spkrt_change_post_base'].values\n", "r = stats.mannwhitneyu(sr_v,sr_fl)\n", "print('(1)')\n", "print(r)\n", "\n", "sp_v = alldf[(alldf['condition']=='aen_vent')&(alldf['phase']=='phase1')&(alldf['trigger_type']=='paired')]['sp_change'].values\n", "sp_fl = alldf[(alldf['condition']=='aen_proprio')&(alldf['phase']=='phase1')&(alldf['trigger_type']=='paired')]['sp_change'].values\n", "r = stats.mannwhitneyu(sp_v,sp_fl)\n", "print('(2)')\n", "print(r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**some notes on effect sizes and power for results**" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "difference in correlation between the prestimulus:stimulus response and the poststimulus:stimulus response\n", "effect size = -0.6679196441256972\n" ] }, { "data": { "text/html": [ "
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sp_poststim_meansp_basestim_mean
count27.00000027.000000
mean-0.1964770.033320
std0.3441140.343983
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" ], "text/plain": [ " sp_poststim_mean sp_basestim_mean\n", "count 27.000000 27.000000\n", "mean -0.196477 0.033320\n", "std 0.344114 0.343983\n", "min -0.711848 -0.728269\n", "25% -0.463366 -0.187173\n", "50% -0.214484 0.108765\n", "75% -0.001742 0.245227\n", "max 0.772056 0.495683" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " \n", "difference in spike rate between the prestimulus and the poststimulus response\n", "effect size = -0.15313514756894434\n" ] } ], "source": [ "print('difference in correlation between the prestimulus:stimulus response and the poststimulus:stimulus response')\n", "d1 = alldf[(alldf['cell']=='aen')&(alldf['phase']=='phase1')&(alldf['trigger_type']=='paired')]['sp_poststim_mean'].values\n", "d2 = alldf[(alldf['cell']=='aen')&(alldf['phase']=='phase1')&(alldf['trigger_type']=='paired')]['sp_basestim_mean'].values\n", "es = cohend(d1, d2)\n", "print('effect size = ' + str(es))\n", "\n", "display(alldf[(alldf['cell']=='aen')&(alldf['phase']=='phase1')&(alldf['trigger_type']=='paired')][['sp_poststim_mean','sp_basestim_mean']].describe())\n", "\n", "print(' ')\n", "print('difference in spike rate between the prestimulus and the poststimulus response')\n", "d1 = alldf[(alldf['cell']=='aen')&(alldf['phase']=='phase1')&(alldf['trigger_type']=='paired')]['spkrt_post'].values\n", "d2 = alldf[(alldf['cell']=='aen')&(alldf['phase']=='phase1')&(alldf['trigger_type']=='paired')]['spkrt_base'].values\n", "es = cohend(d1, d2)\n", "print('effect size = ' + str(es))" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "given above calculated effect size, power for change in response correlation to stimulus in poststimulus period:\n", "0.6758489856771918\n", "note that effect size calculation is for independent groups study design,\n", " whereas within-group implemented here throughout\n" ] } ], "source": [ "from statsmodels.stats.power import TTestIndPower\n", "\n", "thistest = TTestIndPower()\n", "r = thistest.solve_power(effect_size=0.67, nobs1=27, alpha=0.05, power=None, ratio=1.0, alternative='two-sided')\n", "\n", "print('given above calculated effect size, power for change in response correlation to stimulus in poststimulus period:')\n", "print(r)\n", "print('note that effect size calculation is for independent groups study design,')\n", "print(' whereas within-group implemented here throughout')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Relevant background information about how many trials for each analysis period across population**
see Methods" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 39.000000\n", "mean 332.769231\n", "std 258.795028\n", "min 91.000000\n", "25% 186.000000\n", "50% 219.000000\n", "75% 394.500000\n", "max 1051.000000\n", "Name: ntrials_pairing, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(alldf[alldf['trigger_type']=='paired']['ntrials_pairing'].describe())" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "17 20060812_2921_SPK.mat\n", "22 20070719_91_coupled_SPK.mat\n", "24 20070723_287_SPK.mat\n", "25 20070723_311_SPK.mat\n", "26 20070723_339_SPK.mat\n", "27 20070723_415_SPK.mat\n", "30 20070816_3375_SPK.mat\n", "31 20070816_3509_SPK.mat\n", "49 20070801_3838_SPK.mat\n", "50 20070801_3838_control_SPK.mat\n", "Name: exptname, dtype: object" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alldf[alldf['ntrials_base']<75].exptname" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10 20050824_2627_trial1_SPK.mat\n", "18 20060812_3066_SPK.mat\n", "24 20070723_287_SPK.mat\n", "25 20070723_311_SPK.mat\n", "26 20070723_339_SPK.mat\n", "27 20070723_415_SPK.mat\n", "33 20071025_3335_control_SPK.mat\n", "49 20070801_3838_SPK.mat\n", "Name: exptname, dtype: object" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alldf[alldf['ntrials_pairing']<150].exptname" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "24 20070723_287_SPK.mat\n", "25 20070723_311_SPK.mat\n", "26 20070723_339_SPK.mat\n", "27 20070723_415_SPK.mat\n", "43 20180407_freerun4_SPK.mat\n", "50 20070801_3838_control_SPK.mat\n", "Name: exptname, dtype: object" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alldf[alldf['ntrials_post']<75].exptname" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Calculate inter-event interval stats**\n", "\n", "--> Each ventilation cyle had a median period of 2.93sec (interquartile range, IQR = 1.96 - 3.62; n=21 paired experiments in 21 AENs). In experiments in which the fin was artificially raised and lowered to mimic swimming movements, the fin lift interval (triggered by an internal clock) was set to a slightly longer duration of 4-6sec to minimize turbulent water disturbance (n = 6 paired experiments in 6 AENs). The speed and amplitude of artificial fin lifts were titrated to avoid excessive modulation of baseline spike rates in AENs, which were 1.8 (1.5 – 2.4) Hz for the 6 AENs in this condition. AEN spike rates were 1.5Hz (1.1 – 2.3; n=21 cells) under the ventilation condition. The difference in baseline firing rates between these two experimental conditions was not significant (Mann Whitney U = 55; p=0.33; n1 = 21 AENs in ventilation condition and n2 = 6 AENs in fin lift condition). " ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "inter-event interval stats\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " count mean std min 25% 50% 75% \\\n", "condition \n", "aen_proprio 6.0 4.912612 0.636920 4.190760 4.510133 4.74655 5.331148 \n", "aen_vent 21.0 2.933595 1.079997 1.280815 1.955700 2.75513 3.617535 \n", "\n", " max \n", "condition \n", "aen_proprio 5.83447 \n", "aen_vent 5.05126 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " \n", "prestimulus spike rates under each condition\n" ] }, { "data": { "text/html": [ "
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aen_vent21.02.5314632.2295890.7000001.1081271.5443042.3291678.865248
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" ], "text/plain": [ " count mean std min 25% 50% \\\n", "condition \n", "aen_proprio 6.0 1.920743 0.657747 1.110465 1.469241 1.840774 \n", "aen_vent 21.0 2.531463 2.229589 0.700000 1.108127 1.544304 \n", "\n", " 75% max \n", "condition \n", "aen_proprio 2.373432 2.836066 \n", "aen_vent 2.329167 8.865248 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "MannwhitneyuResult(statistic=55.0, pvalue=0.33090742168694987)\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print('inter-event interval stats')\n", "display(alldf[((alldf.cell=='aen')&(alldf.trigger_type=='paired')&(alldf.phase=='phase1'))].\n", " groupby(['condition'])['iei_median'].describe())\n", "sns.stripplot(x = \"condition\", y = \"iei_median\", \n", " data = alldf[((alldf.cell=='aen')&(alldf.trigger_type=='paired'))],\n", " color='black')\n", "print(' ')\n", "print('prestimulus spike rates under each condition')\n", "display(alldf[((alldf.trigger_type=='paired')&\n", " (alldf.phase=='phase1')&\n", " ((alldf.condition=='aen_vent')|(alldf.condition=='aen_proprio')))].\n", " groupby(['condition'])['spkrt_base'].describe())\n", "r = stats.mannwhitneyu(alldf[((alldf.condition=='aen_vent')&\n", " (alldf.trigger_type=='paired')&\n", " (alldf.phase=='phase1'))]['spkrt_base'].values,\n", " alldf[((alldf.condition=='aen_proprio')&\n", " (alldf.trigger_type=='paired')&\n", " (alldf.phase=='phase1'))]['spkrt_base'].values)\n", "print(r)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "for AENs:\n" ] }, { "data": { "text/html": [ "
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optwoptw_stim
count35.00000035.000000
mean0.0697710.024000
std0.0413530.016328
min0.0100000.006000
25%0.0420000.012000
50%0.0600000.016000
75%0.0770000.037000
max0.1780000.064000
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" ], "text/plain": [ " optw optw_stim\n", "count 35.000000 35.000000\n", "mean 0.069771 0.024000\n", "std 0.041353 0.016328\n", "min 0.010000 0.006000\n", "25% 0.042000 0.012000\n", "50% 0.060000 0.016000\n", "75% 0.077000 0.037000\n", "max 0.178000 0.064000" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "for afferents:\n" ] }, { "data": { "text/html": [ "
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optwoptw_stim
count9.0000009.000000
mean0.1028890.022000
std0.0744120.018466
min0.0320000.006000
25%0.0420000.006000
50%0.0600000.018000
75%0.1980000.032000
max0.1980000.058000
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" ], "text/plain": [ " optw optw_stim\n", "count 9.000000 9.000000\n", "mean 0.102889 0.022000\n", "std 0.074412 0.018466\n", "min 0.032000 0.006000\n", "25% 0.042000 0.006000\n", "50% 0.060000 0.018000\n", "75% 0.198000 0.032000\n", "max 0.198000 0.058000" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "aen\n", "\n", "afferent\n" ] }, { "data": { "image/png": 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MeiRGlbHvyDFuHjuXuRv28D/XdzUBxjleasILgMEish6nTWhDFHHMtwePcvPYuazcvo8XbupG386n+x2SUQZeRNg36lEYVcKu/YUMHDOH9bsPkjsom4s7mCdoIhBpKVMDVd2H47htxDnb9h5mwMtz2Lb3CONu6cH5Z5onaKIQqSacBPwUmI/TGxo8sKRAmyjGZZSDTXscT9BvDzqeoNlZZkmYSITtmFHVn7o/W6tqG/dnYPMkwEr6jg4WkdXuZl/PHYZ1uxxP0H2Hi5g4tJcJMAEps3dURD70ci5EmoDv6BVAR6B/iF7WgO/opFJ5G+F4nfbCsU58xLW8MIJYtX0/14+ezbHiEqbk9KZLq4Z+h2RUgEhtwtpAOtDEFUDgdbQB0MLDvSvjO3o5MFNV97jXZ+J0EE328NxqwbItexn4yhxq1Uhh4tA+nHmaWRImKpHahMOBu3AEN5/vRbgPp4Yri1C+o708xuXJs1REcoAcgIyM6jMWNr/gW24ZN5cGtWsyaVgvMhvX9TskoxJEsrd4DnhORH6jqs9X4N6V8R31lLc6+o5+uXY3t42fx2n1azFpWG/zBE0CvMyY2S4i9QFE5EEReVtEzvOQrzK+o5XyLE1WZq3ayS3j5nJGwzpMHd7HBJgkeBHhQ6q6X0QuwGmrjQde9JDvuO+oiKTh+I56XRL1PnCZiJzqtkcvc89VW95fvp1hE/Jp27Qerw/vw2kNzJQ3WfAiwmL351XAi6o6DQ9O3KpaBAR8R78CpgZ8R0WkH4CI9BCRzcB1OD6jy928e4DHcYQ8D3gs0ElTHZm+eCu/nriATi1OYfKw3mbKm2SE9R09nkBkBo6D9qVAd+AwMFdVu0Q/PO8kq+/o1PxNjHxrCT2zGvHKLT2oZ56gCUkk31EvNeH1OLVZX1X9DmiEY3dhRJkJX27gvjeXcMGZTXh1SE8TYJLi5bsoDhG0gFdVtwHbohmUAbmfruVP/7eSS89uxgsDzBM0mbGP1jhDVfn7h2v4nw++5qpzT+fZG7qaJ2iSYyKMI1SVP7+3ipc+Wcsvz2vJ09eaJ2h1wEQYJ5SUKI/NWMGrX2xgYO8MHuvX2TxBqwkmwjiguEQZ9c5SpszbxNALWjPqqrPNkrAaYSL0maLiEn7/xmKmLdrKnZecye9+cpYJsJphIvSRo0Ul3Dl5Ie8t3869l7fn9ovP9Dskwwes280nHE/QfN5bvp2Hf9rRVwFOnDiRrKwsUlJSyMrKYuLEib7FUh2xmtAHDhYWMWxCPl+u282TvziH/j5aEk6cOJGcnBwOHToEQEFBATk5OQAMGDDAt7iqE2VOW0sUEmXa2r4jx7h13DwWbPyWv17fhZ93a+lrPFlZWRQUFJx0PjMzkw0bNsQ+oCSlQl+XbVQ93x1yPEFXbN3HP246jyvP8d8TdOPGjeU6b1Q91iaMEbv2F3Jj7mxWbt9P7s3d40KAEN6RoDo5FfiNiTAGbN97hBtyv6Rg9yHGDu7BJR2aRfV55eloeeKJJ0hPTz/hXHp6Ok888URUYzSCUNWobTjmTKuANcD9Ia7XAl53r88BstzzWThLpha520tlPat79+4aD+Tl5WlmZqaKiGZmZurzYyfpD//8kXZ6+D2du353TJ6fnp6uOHYgCmh6errm5eV5jjlSWqNiAPkaTifhLlR2A1KBtTgmwWnAYqBjqTS/DggMZ+X96/q9CJeV53nxIMLSAqhxagtt+etXtcP/m66LNn4bMn1V//NnZmaeIMDAlpmZWel7GxXHLxH2Ad4POn4AeKBUmveBPu5+DeAbHJOnhBRhsABqNsnQM26foC3vyNPMLj84KW1FaiwviEhIEYpIpe5rVI5IIoxmm9CLbeHxNOrYYewFGrvXWovIQhH5RER+GMU4q4xAj2Jas7Y06/8kqLJ98v0ULP7ipLbZqFGjjo/NBTh06BCjRo2qVAzW0ZJ4RFOEXmwLw6XZBmSoajfgbmCSiDQ46QEiOSKSLyL5u3btqnTAlSUjI4O0Fu1pduMT6LFCdky6n6Ldm4HvB8EDQgw3BFBQUFCpGSvW0ZKAhKsiK7tRidfREPeaBWRHep5fr6PB7bqmnX6grX73hrbIydXU+k1Dvhbits8aN24c9nplX0utoyX+wKc2YQ1gHdCa7ztmOpVKczsndsxMdfebAqnufhsco6lGkZ7nhwiD23W1W5+nre5+S1sMfVGbtGobVmCBLS0tTWvWrBn2empqqokniYgkwqi9jqoHy0PgFaCxiKzBee0MfHPThcASEVkMvAn8SuPQ8jDQrqtzZi9O+8VDFO3ZzPaJI6mbUkRmZmbEvEePHqVBg5PesI9TXFx8wuurkcSEU2eibbGsCQOve4Cmd/ihZtw7TZsP+qum1Kp7vCcyVO9n6S3wuhgpjQ0tJAf41DualARWHRQUFFC3849p8rN7KNzyFTtef5CSwoOA00EzYMAAcnNzI9aIGRkZITtSgrE5nNWAcOpMtC1WNWGg5qrX9QrNHDlDT7v+MZUatSJ2qpQ1JpiXl6epqalWEyYxWE1YdWzcuJH6Pa6h8eW3c2j1HHa+9ThaVAhAamrq8bG+4LZccK0oImRmZpKbm3vCer2GDU/+gk8bWqgmhFNnom1VXROG6uYvKSnRzCuGa+bIGdrk6pFKSujai3IMM4RrOzZu3Nh6R5MI/BiiiPVWlSIM9/o46Jk3NXPkDG3W715FUsochgj1Klla3OHGC+01NLmIJEJb1BuCUFPKavUZyKe7anNTrwzO7tyFhxa3CrkiPZjSnSqhrCS85jWSF7O3CEFKSgrHfy+SQqPLfk39rn3Zlz+N3TNHH7ckPCFdCEpbRISzkvCS10hsKvutTNWO45OdJYXGV95F/a592fvFFBqs+c8JnqCRJkWH6lTxWrtZh0z1wkQYgiuvvBJSatDk6pHU63wJ334ynu/+m+ecDyLcGF/jxo2P934Gr3JPSfH2665TJ/LXYJtFYZIRrrGYaFtVdsxktm6rTX/5sGaOnKH1s/uVq6MluEfTy6yZcFu43tVorUM0ogvWOxqZE4TUpp2edsMfNeO+6VqvS9+TxBHMiBEjjg+yp6am6ogRI064XtaUtLK2UKK33tTExEQYgeCaRdLqaLOb/qwZ907Tup0uCbmyIcCIESNCiiEgxLy8vEoJEE5eDV/WPY34xUQYgUBtlVK7njYf9DfNuOd/Nb39+WX+o4ebZhZYgpSWllZpEZau3SLVrIFJ40Z8YiKMgIhoSvopevqQ5zXj9+9onbY9PYkikngiLditTJswnH9MNF5JK7IwOC8v74Sy26yf7zERRiCzw7naYuiL2uruN7V2VtcTapZIoohUE1ZFDRjqn7esNmZVmTlV1DYxVO1fs2ZNE6L6KEIq6DvqXnvAPb8KuLysZ1VEhJv2HNRuD03TVr97Q2u17HTCP9yIESMi1gSR2oRliaxevXpha7VItVlZva1VVRNWxDYx0geEdRr5JEIq5zva0U1fC8ceYy2u3UW4rbwiXLfrgPb50wd6ziPv6ZO5kyvkyRKud9SLf8yIESPKrG1DUfqVrzx5vVIR28RIr8pmt+ifCCvjO3pC2uB04bbyiPDr7fs0+48ztdtj/9FlW74r7++zTPLy8kL6x5RuI1XGkCmaZk5WE1Y9fonwWmBM0PEg4B+l0iwDWgYdrwWaAP8ABgadfwW4NtLzvIqwuLhEL/3rLO3xx5n69fZ9Ffh1eiORHc+sTVj1+CXC60KI8PlSaZaHEGFj4IUQIvxliGfkAPlAfkZGhudfyPIte3X9rgPl/01WI6x3tGqJJMKoraIQkT7Ao6p6uXv8AICqPhmU5n03zZciUgPYjmN3eH9w2uB04Z6XKF8SalRP/FpFMQ9oJyKtRSQNp+Nleqk004HB7v61wEfup8Z04EYRqSUirYF2wNwoxmoYvhG1Rb2qWiQiAd/RVGCsur6jOFXzdJzXzNdc39E9OELFTTcVWAEUAberanG0YjUMP7FFvYYRA2xRr2HEMSZCw/AZE6Fh+IyJ0DB8xkRoGD5jIjQMnzERGobPmAgNw2eSZrBeRHYB3uytHZrgLJ1KZJKhDJAc5SirDJmq2jTUhaQRYXkRkfxwMxgShWQoAyRHOSpTBnsdNQyfMREahs9UZxHm+h1AFZAMZYDkKEeFy1Bt24SGES9U55rQMOKCpBehiPQVkVUiskZE7g9xvZaIvO5enyMiWbGPMjIeynChiCwQkSIRudaPGL3goRx3i8gKEVkiIh+KSKYfcUbCQxl+JSJLRWSRiHwmIh3LvGk485lk2KiE92m8bB7LkAWcC0ygDFe6OC/HxUC6uz8iQf8WDYL2+wHvlXXfZK8JewJrVHWdqh4FpgBXl0pzNTDe3X8T+LEEfx2v/5RZBlXdoKpLgBI/AvSIl3J8rKqH3MPZQMsYx1gWXsqwL+iwLo7zXESSXYRnAJuCjje750KmUdUiYC+O7WK84KUMiUB5y3Eb8G5UIyo/nsogIreLyFrgaeDOsm6a7CIMVaOV/mTyksZP4j0+r3guh4gMBLKBv0Q1ovLjqQyq+oKqtgVGAg+WddNkF+FmoFXQcUtga7g0rvfpKTjOb/GClzIkAp7KISKXAqOAfqpaGKPYvFLev8UU4JqybprsIqyM92m84KUMiUCZ5RCRbsBoHAHu9CHGsvBShnZBh1cBq8u8q989TjHo0boS+BqnV2uUe+4xnD80QG3gDZyvYZsLtPE75gqUoQfOp/RBYDew3O+YK1iOD4AdwCJ3m+53zBUow3M4X++wCPgY6FTWPW3GjGH4TLK/jhpG3GMiNAyfMREahs+YCA3DZ0yEhuEzJsI4QkSyRGRZOfN0cGfsLxSRttGKzWMs2SLy9/Jeq+7YEEUc4S6jmqGqncuR536gjqo+4jG94PzdYzbZW0RqqDMv1wiB1YTxRw0RGe+uqXtTRNIBRKS7iHwiIvNF5H0ROV1ErgTuAoaKyMduurtFZJm73eWeyxKRr0Tkn8ACoJWIXCYiX7rrEN8QkXqlAxGRWSLyrIh84d6vp3u+p3tuofuzvXv+IhGZ4e4/KiK5IvIfYEKIa2Pd+68TkTuDnvmQiKwUkZkiMllE7oni7zo+8HsGgm0nzMbIwpkQfL57PBa4B6gJfAE0dc/fgPPNxwCPAve4+92BpThLaOrhzNzo5t63BOjtpmsCfArUdY9HAg+HiGcW8LK7fyGwzN1vANRw9y8F3nL3L8KpyQNxzceppUNd+wKo5cay2y1jNs5MkzpAfZwpX/f4/XeJ9ha1r8s2KswmVf3c3c/DWQrzHtAZmOkudUwFtoXIewHwjqoeBBCRt4Ef4sxvLFDV2W663kBH4HP3fmnAl2HimQygqp+KSAMRaYgjkPHuPEnFEVAopqvq4TDX/q3OBO1CEdkJNHPjnxbIIyL/CpM3qTARxh+lG+mKs4Rmuar2KSNvpMXIB0ulm6mq/SsYz+PAx6r6c7cdO8vDM0sTvEKiGOd/MZ4WU8cMaxPGHxkiEhBbf+AzYBXQNHBeRGqKSKcQeT8FrhGRdBGpC/wc+G+IdLOB80XkTPd+6SJyVph4bnDTXADsVdW9OMu9trjXbylvASPwGfAzEanttlGvqsJ7xy0mwvjjK2CwiCwBGgEvqmOlcC3wZxFZjNNu+kHpjKq6AHgVZzXIHGCMqi4MkW4Xjngmu8+ZDXQIE8+3IvIF8BLOandwVow/KSKf47waVwmqOg/n1Xkx8DaQj+N0kNTYEIURFhGZhdMxkh/DZ9ZT1QNur/CnQI774ZK0WJvQiDdyXZvA2sD4ZBcgWE1oGL5jbULD8BkToWH4jInQMHzGRGgYPmMiNAyfMREahs/8f8vjTlyg/oJGAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "########plot optw between baseline and stimulus period for afferents and aens\n", "\n", "print('for AENs:')\n", "display(alldf[(alldf['cell']=='aen')][['optw','optw_stim']].describe())\n", "print('')\n", "print('for afferents:')\n", "display(alldf[(alldf['cell']=='afferent')][['optw','optw_stim']].describe())\n", "\n", "\n", "########plot optw between baseline and stimulus period for afferents and aens\n", "print('aen')\n", "cell = 'aen'\n", "lims = [-0.01,0.2]\n", "fig= plt.figure(figsize = (3,3))\n", "ax = plt.gca()\n", "data = np.asarray([alldf[alldf.cell==cell]['optw'].values,\n", " alldf[alldf.cell==cell]['optw_stim'].values]).T\n", "plt.scatter(data[:,0],data[:,1],color = 'black')\n", "plt.plot([0,0.3],[0,0.3])\n", "plt.xlabel('before pairing')\n", "plt.ylabel('stim initial')\n", "plt.savefig('Figures/revisions/scatter_optwstim_optwbase_' + cell + '.eps', format='eps', dpi=1000)\n", "\n", "print('')\n", "print('afferent')\n", "cell = 'afferent'\n", "lims = [-0.01,0.2]\n", "fig= plt.figure(figsize = (3,3))\n", "ax = plt.gca()\n", "data = np.asarray([alldf[alldf.cell==cell]['optw'].values,\n", " alldf[alldf.cell==cell]['optw_stim'].values]).T\n", "plt.scatter(data[:,0],data[:,1],color = 'black')\n", "plt.plot([0,0.3],[0,0.3])\n", "plt.xlabel('before pairing')\n", "plt.ylabel('stim initial')\n", "plt.savefig('Figures/revisions/scatter_optwstim_optwbase_' + cell + '.eps', format='eps', dpi=1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**check some of the statistical assumptions of tests in the paper: normality and homoscedasticity** " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "** NORMALITY **\n", "AEN\n", "post vs base spikerate effect in AEN... is the distribution of within cell changes normal?\n", "NormaltestResult(statistic=1.378380834483559, pvalue=0.5019823008204316)\n", "\n", "stimi vs stimf spikerate effect in AEN... is the distribution of within cell changes normal?\n", "NormaltestResult(statistic=3.241546535746715, pvalue=0.1977457295282777)\n", "\n", "baseline spikerate aen normal?\n", "NormaltestResult(statistic=30.48443222689529, pvalue=2.4009861229121144e-07)\n", "\n", "post vs base correlation with stim effect in AEN... is the distribution of within cell changes normal?\n", "NormaltestResult(statistic=0.40488707452256806, pvalue=0.81673259625543)\n", " \n", "baseline correlation to stim aen normal?\n", "NormaltestResult(statistic=0.8895302869147168, pvalue=0.6409747954807752)\n", "\n", "post-pairing correlation to stim aen normal?\n", "NormaltestResult(statistic=5.766365313151493, pvalue=0.05595638916370598)\n", "\n", "AFFERENT\n", "post vs base spikerate effect in afferent... is the distribution of within cell changes normal?\n", "NormaltestResult(statistic=7.764093499533953, pvalue=0.020608601335647868)\n", "\n", "baseline spikerate afferent normal?\n", "NormaltestResult(statistic=4.276604169716073, pvalue=0.11785478065180233)\n", "\n", "change pre to post correlation to stim afferent normal?\n", "NormaltestResult(statistic=8.01994959116063, pvalue=0.018133852284264715)\n", "\n", "baseline correlation to stim afferent normal?\n", "NormaltestResult(statistic=1.48772856756825, pvalue=0.4752737696668584)\n", "\n", "post-pairing correlation to stim afferent normal?\n", "NormaltestResult(statistic=5.528589351933127, pvalue=0.06302053357283856)\n", "\n", "** homoscedasticity **\n", "AEN\n", "aen post and pre spkrate are the distributions homoscedastic\n", "BartlettResult(statistic=0.03012581770737272, pvalue=0.8622050570745396)\n", "\n", "aen stimi and stimf spkrate are the distributions homoscedastic\n", "BartlettResult(statistic=0.1467732312941197, pvalue=0.7016382448267544)\n", "\n", "aen post and pre correlation with stimulus are the distributions homoscedastic\n", "BartlettResult(statistic=0.14095165697739806, pvalue=0.7073367634775007)\n", "AFFERENT\n", "\n", "afferent post and pre spkrate are the distributions homoscedastic\n", "BartlettResult(statistic=0.5202657115230122, pvalue=0.47072835903292976)\n", "\n", "afferent stii and stimf spkrate are the distributions homoscedastic\n", "BartlettResult(statistic=0.09263774940305826, pvalue=0.7608502098106558)\n", "\n", "afferent post and pre correlation with stimulus are the distributions homoscedastic\n", "BartlettResult(statistic=0.16942860618416944, pvalue=0.6806201234207014)\n", "\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1416: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n", " \"anyway, n=%i\" % int(n))\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1416: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n", " \"anyway, n=%i\" % int(n))\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1416: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n", " \"anyway, n=%i\" % int(n))\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1416: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n", " \"anyway, n=%i\" % int(n))\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1416: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n", " \"anyway, n=%i\" % int(n))\n" ] } ], "source": [ "print('** NORMALITY **')\n", "print('AEN')\n", "print('post vs base spikerate effect in AEN... is the distribution of within cell changes normal?')\n", "r = stats.normaltest(alldf[(alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')]['spkrt_post'].values-\n", " alldf[(alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')]['spkrt_base'].values)\n", "print(r)\n", "print('')\n", "print('stimi vs stimf spikerate effect in AEN... is the distribution of within cell changes normal?')\n", "r = stats.normaltest(alldf[(alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')]['spkrt_stimi'].values-\n", " alldf[(alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')]['spkrt_stimf'].values)\n", "print(r)\n", "print('')\n", "print('baseline spikerate aen normal?')\n", "r = stats.normaltest(alldf[alldf.cell=='aen']['spkrt_base'].values)\n", "print(r)\n", "print('')\n", "print('post vs base correlation with stim effect in AEN... is the distribution of within cell changes normal?')\n", "r = stats.normaltest(alldf[alldf.cell=='aen']['sp_change'].values)\n", "print(r)\n", "print(' ')\n", "print('baseline correlation to stim aen normal?')\n", "r = stats.normaltest(alldf[(alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')]['sp_basestim_mean'].values)\n", "print(r)\n", "print('')\n", "print('post-pairing correlation to stim aen normal?')\n", "r = stats.normaltest(alldf[(alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')]['sp_poststim_mean'].values)\n", "print(r)\n", "print('')\n", "print('AFFERENT')\n", "print('post vs base spikerate effect in afferent... is the distribution of within cell changes normal?')\n", "r = stats.normaltest(alldf[alldf.cell=='afferent']['spkrt_post'].values-\n", " alldf[alldf.cell=='afferent']['spkrt_base'].values)\n", "print(r)\n", "print('')\n", "print('baseline spikerate afferent normal?')\n", "r = stats.normaltest(alldf[alldf.cell=='afferent']['spkrt_base'].values)\n", "print(r)\n", "print('')\n", "print('change pre to post correlation to stim afferent normal?')\n", "r = stats.normaltest(alldf[alldf.cell=='afferent']['sp_change'].values)\n", "print(r)\n", "print('')\n", "print('baseline correlation to stim afferent normal?')\n", "r = stats.normaltest(alldf[alldf.cell=='afferent']['sp_basestim_mean'].values)\n", "print(r)\n", "print('')\n", "print('post-pairing correlation to stim afferent normal?')\n", "r = stats.normaltest(alldf[alldf.cell=='afferent']['sp_poststim_mean'].values)\n", "print(r)\n", "print('')\n", "print('** homoscedasticity **')\n", "print('AEN')\n", "print('aen post and pre spkrate are the distributions homoscedastic')\n", "r = stats.bartlett(alldf[(alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')]['spkrt_post'].values,\n", " alldf[(alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')]['spkrt_base'].values)\n", "print(r)\n", "print('')\n", "print('aen stimi and stimf spkrate are the distributions homoscedastic')\n", "r = stats.bartlett(alldf[(alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')]['spkrt_stimi'].values,\n", " alldf[(alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')]['spkrt_stimf'].values)\n", "print(r)\n", "print('')\n", "print('aen post and pre correlation with stimulus are the distributions homoscedastic')\n", "r = stats.bartlett(alldf[(alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')]['sp_poststim_mean'].values,\n", " alldf[(alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')]['sp_basestim_mean'].values)\n", "print(r)\n", "print('AFFERENT')\n", "print('')\n", "print('afferent post and pre spkrate are the distributions homoscedastic')\n", "r = stats.bartlett(alldf[alldf.cell == 'afferent']['spkrt_post'],\n", " alldf[alldf.cell == 'afferent']['spkrt_base'])\n", "print(r)\n", "print('')\n", "print('afferent stii and stimf spkrate are the distributions homoscedastic')\n", "r = stats.bartlett(alldf[alldf.cell == 'afferent']['spkrt_stimi'],\n", " alldf[alldf.cell == 'afferent']['spkrt_stimf'])\n", "print(r)\n", "print('')\n", "print('afferent post and pre correlation with stimulus are the distributions homoscedastic')\n", "r = stats.bartlett(alldf[alldf.cell == 'afferent']['sp_poststim_mean'],\n", " alldf[alldf.cell == 'afferent']['sp_basestim_mean'])\n", "print(r)\n", "print('')\n", "print('')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "**Figure 4A&B: Changes in response rates driven by stimulation and before and after pairing**\n", "
\n", "--> After repeated pairing of the stimulus with a behavioral cue we found, first, that the stimulus-driven response had decreased (Fig 4A). Over the population of AENs tested in the paired experimental condition, spike rates decreased significantly during the stimulus period by 0.5 Hz (0.9 – 0.1) (Fig. 4A; W = 98; p = 0.03; n=27 cells), though these rates were still elevated by 1.1Hz (0.3 – 1.8) compared to the prestimulus period (W = 17; p<0.001; n=27 cells). And, second, that when we then withheld the stimulus, the AEN spike rates in the poststimulus period had decreased significantly relative to the prestimulus period by 0.3 Hz (0.7 – +0.1) (Fig. 4A; W = 80; p=0.009; n=27 cells). This effect is also readily observed in the example spike rasters depicted in Figs. 3&5. These results are consistent with the generation of a cancellation signal, and are not likely explained by changes to afferent input. Afferents fired more regularly than AENs in the prestimulus period at 7.4Hz (2.1 - 15; n=9). As shown in the example cell of Fig. 3C, the mechanosensory stimulus modulated afferent spiking activity. However, when averaged across each trial, spike rates were not changed significantly either by the stimulus or during or after the stimulus period (data not shown). Based on extensive work on the adaptive filter model in the electrosensory and auditory systems, one expects that this observed change in AEN responses after the stimulus period would depend on coupling between the stimulus and a movement-related signal (conveyed by parallel fiber inputs to AENs) (Bell, 1981, Bodznick and Montgomery 1999, Zhang and Bodznick 2008, Singla et al, 2017). For a subset of these AENs (n = 5/27 cells), we performed an additional iteration of the experiment in which the trials were yolked to an internal computer clock (‘freerun’ condition) rather than the animal’s own behavior. Only in the paired condition did all five AENs exhibit decreased spike rates in the poststimulus period relative to the prestimulus period (Fig. 4B; same cells as those in the analysis corresponding to Fig. 5D) (paired condition: W=0; critical W at p<0.05 = 0 for n=5 cells; freerun condition: W=7: critical W at alpha(0.05) = 0 for n=5 cells). Specifically, the poststimulus spike rates were lower in the paired relative the freerun condition in 4/5 of these cells. Together, these results are consistent with the development of a cancellation signal for the stimulus-driven response as a result of stimulus pairing with movement-related signals. However, changes to the overall spike rate of AENs could be explained by other factors such as neural fatigue. Although a comparison between freerun and paired conditions would rule out this alternative explanation of the results, with a sample size of n=5 it is difficult to make a strong conclusion from this result alone. Importantly, changes to the temporal profile of the AEN spiking response are equally as important to changes in its magnitude when assessing the generation of a cancellation signal / negative image. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Part1: generate the figures, show summary stats, do significance stats**" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "grouped by cell type\n", "aen\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " spkrt_base spkrt_change_stimi_base spkrt_change_stimf_stimi \\\n", "count 5.000000 5.000000 5.000000 \n", "mean 1.471923 2.013025 -0.675599 \n", "std 0.286832 0.981987 0.833073 \n", "min 1.110465 0.549535 -2.037559 \n", "25% 1.394309 1.716802 -0.853333 \n", "50% 1.410377 2.020782 -0.403333 \n", "75% 1.544304 2.673474 -0.120000 \n", "max 1.900161 3.104534 0.036232 \n", "\n", " spkrt_change_post_base spkrt_change_post_recover \\\n", "count 5.000000 1.000000 \n", "mean -0.490590 0.888889 \n", "std 0.309085 NaN \n", "min -0.940161 0.888889 \n", "25% -0.678753 0.888889 \n", "50% -0.320377 0.888889 \n", "75% -0.313193 0.888889 \n", "max -0.200465 0.888889 \n", "\n", " spkrt_change_recover_base \n", "count 1.000000 \n", "mean -0.051272 \n", "std NaN \n", "min -0.051272 \n", "25% -0.051272 \n", "50% -0.051272 \n", "75% -0.051272 \n", "max -0.051272 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "AEN control test cells freerun\n" ] }, { "data": { "text/html": [ "
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spkrt_basespkrt_change_stimi_basespkrt_change_stimf_stimispkrt_change_post_basespkrt_change_post_recoverspkrt_change_recover_base
count5.0000005.0000005.0000005.0000000.00.0
mean1.2744131.511769-0.3695680.067604NaNNaN
std0.2594620.6685050.5769130.614367NaNNaN
min0.9713260.601677-1.088889-0.586667NaNNaN
25%1.0976561.312024-0.817778-0.286882NaNNaN
50%1.2222221.375341-0.168950-0.205349NaNNaN
75%1.5094341.885677-0.1155560.535010NaNNaN
max1.5714292.3841270.3433330.881905NaNNaN
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" ], "text/plain": [ " spkrt_base spkrt_change_stimi_base spkrt_change_stimf_stimi \\\n", "count 5.000000 5.000000 5.000000 \n", "mean 1.274413 1.511769 -0.369568 \n", "std 0.259462 0.668505 0.576913 \n", "min 0.971326 0.601677 -1.088889 \n", "25% 1.097656 1.312024 -0.817778 \n", "50% 1.222222 1.375341 -0.168950 \n", "75% 1.509434 1.885677 -0.115556 \n", "max 1.571429 2.384127 0.343333 \n", "\n", " spkrt_change_post_base spkrt_change_post_recover \\\n", "count 5.000000 0.0 \n", "mean 0.067604 NaN \n", "std 0.614367 NaN \n", "min -0.586667 NaN \n", "25% -0.286882 NaN \n", "50% -0.205349 NaN \n", "75% 0.535010 NaN \n", "max 0.881905 NaN \n", "\n", " spkrt_change_recover_base \n", "count 0.0 \n", "mean NaN \n", "std NaN \n", "min NaN \n", "25% NaN \n", "50% NaN \n", "75% NaN \n", "max NaN " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##############STATS#################\n", "# get stats, grouped by cell type, for spike rates and change during stim and pre vs post pairing\n", "print('grouped by cell type')\n", "print('aen')\n", "display(alldf[((alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')&\n", " (alldf.phase=='phase1'))]\n", " [['spkrt_base','spkrt_change_stimi_base','spkrt_change_stimf_base',\n", " 'spkrt_change_stimf_stimi','spkrt_change_post_base',\n", " 'spkrt_change_post_recover','spkrt_change_recover_base']].describe())\n", "\n", "print('afferent')\n", "display(alldf[alldf.cell=='afferent']\n", " [['spkrt_base','spkrt_change_stimi_base',\n", " 'spkrt_change_stimf_stimi','spkrt_change_post_base',\n", " 'spkrt_change_post_recover','spkrt_change_recover_base']].describe())\n", "\n", "print('AEN control test cells paired')\n", "display(alldf[(alldf.cell=='aen')&(alldf.control=='yes')&(alldf.trigger_type=='paired')]\n", " [['spkrt_base','spkrt_change_stimi_base',\n", " 'spkrt_change_stimf_stimi','spkrt_change_post_base',\n", " 'spkrt_change_post_recover','spkrt_change_recover_base']].describe())\n", "\n", "print('AEN control test cells freerun')\n", "display(alldf[(alldf.cell=='aen')&(alldf.control=='yes')&(alldf.trigger_type=='freerun')]\n", " [['spkrt_base','spkrt_change_stimi_base',\n", " 'spkrt_change_stimf_stimi','spkrt_change_post_base',\n", " 'spkrt_change_post_recover','spkrt_change_recover_base']].describe())\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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spkrt_basespkrt_change_stimi_basespkrt_change_stimf_stimispkrt_change_post_base
count27.00000027.00000027.00000027.000000
mean2.3957471.784202-0.452852-0.310293
std1.9935001.4131920.9590920.595476
min0.700000-0.649167-3.004444-1.363740
25%1.2150970.815709-0.920920-0.679769
50%1.6458331.716802-0.486667-0.320377
75%2.4075852.7258040.0800000.083417
max8.8652485.7185391.0755561.280601
\n", "
" ], "text/plain": [ " spkrt_base spkrt_change_stimi_base spkrt_change_stimf_stimi \\\n", "count 27.000000 27.000000 27.000000 \n", "mean 2.395747 1.784202 -0.452852 \n", "std 1.993500 1.413192 0.959092 \n", "min 0.700000 -0.649167 -3.004444 \n", "25% 1.215097 0.815709 -0.920920 \n", "50% 1.645833 1.716802 -0.486667 \n", "75% 2.407585 2.725804 0.080000 \n", "max 8.865248 5.718539 1.075556 \n", "\n", " spkrt_change_post_base \n", "count 27.000000 \n", "mean -0.310293 \n", "std 0.595476 \n", "min -1.363740 \n", "25% -0.679769 \n", "50% -0.320377 \n", "75% 0.083417 \n", "max 1.280601 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "aen\n", "n = 27\n", "post-pairing to base: H0 = post-pairing same as base\n", "WilcoxonResult(statistic=80.0, pvalue=0.00882590938459905)\n", "aen\n", "stimulus initial to base: H0 = stimi same as base\n", "WilcoxonResult(statistic=8.0, pvalue=1.3705630970686292e-05)\n", "aen\n", "stimulus change: H0 = stimf same as stimi\n", "WilcoxonResult(statistic=98.0, pvalue=0.028795498649410566)\n", "aen\n", "stimulus final to base: H0 = stimf same as base\n", "WilcoxonResult(statistic=17.0, pvalue=3.591515022031889e-05)\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#######AENS changes in spike rate#########\n", "plotdf = alldf[((alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')&\n", " (alldf.phase=='phase1'))][['spkrt_change_stimi_base','spkrt_change_stimf_base',\n", " 'spkrt_change_post_base','spkrt_change_recover_base']]\n", "\n", "fig = plt.figure(figsize=(7,8))\n", "ax = sns.stripplot(data=plotdf,color='black',s=10,alpha=1,jitter=0.2)\n", "# [ax.plot([0,1,2,3,4],sweep,color='grey') for sweep in plotdf[['base','stimi','stimf','post','recovery']].values];\n", "plt.plot([-1,4],[0,0],color='grey')\n", "colhead = plotdf.columns.to_list()\n", "for i,c in enumerate(colhead):\n", " q = plotdf[c].quantile([.25, .5,0.75]) \n", " ax.plot([i-0.2,i+0.2],[q.values[1],q.values[1]],color = 'red',lw = 3,zorder=30)\n", " ax.plot([i,i],[q.values[1],q.values[0]],color = 'red',lw = 3,zorder=30)\n", " ax.plot([i,i],[q.values[1],q.values[2]],color = 'red',lw = 3,zorder=30)\n", "plt.ylim(-2.5,6)\n", "plt.savefig('Figures/revisions/scatter_aen_spkrates_.eps', format='eps', dpi=1000)\n", "\n", "#######and separate condition by color#######\n", "####ventilation####\n", "#plot changes in spike rate for each period\n", "plotdf = alldf[((alldf.condition=='aen_vent')&\n", " (alldf.trigger_type=='paired')&\n", " (alldf.phase=='phase1'))][['spkrt_change_stimi_base','spkrt_change_stimf_base',\n", " 'spkrt_change_post_base','spkrt_change_recover_base']]\n", "\n", "fig = plt.figure(figsize=(7,8))\n", "ax = sns.stripplot(data=plotdf,color='green',s=10,alpha=1,jitter=0.2)\n", "# [ax.plot([0,1,2,3,4],sweep,color='grey') for sweep in plotdf[['base','stimi','stimf','post','recovery']].values];\n", "plt.plot([-1,4],[0,0],color='grey')\n", "colhead = plotdf.columns.to_list()\n", "for i,c in enumerate(colhead):\n", " q = plotdf[c].quantile([.25, .5,0.75]) \n", " ax.plot([i-0.2,i+0.2],[q.values[1],q.values[1]],color = 'red',lw = 3,zorder=30)\n", " ax.plot([i,i],[q.values[1],q.values[0]],color = 'red',lw = 3,zorder=30)\n", " ax.plot([i,i],[q.values[1],q.values[2]],color = 'red',lw = 3,zorder=30)\n", "plt.ylim(-2.5,6)\n", "plt.savefig('Figures/revisions/scatter_aenV_spkrates_.eps', format='eps', dpi=1000)\n", "\n", "######fin lift######\n", "#plot changes in spike rate for each period\n", "plotdf = alldf[((alldf.condition=='aen_proprio')&\n", " (alldf.trigger_type=='paired')&\n", " (alldf.phase=='phase1'))][['spkrt_change_stimi_base','spkrt_change_stimf_base',\n", " 'spkrt_change_post_base','spkrt_change_recover_base']]\n", "\n", "fig = plt.figure(figsize=(7,8))\n", "ax = sns.stripplot(data=plotdf,color='blue',s=10,alpha=1,jitter=0.2)\n", "# [ax.plot([0,1,2,3,4],sweep,color='grey') for sweep in plotdf[['base','stimi','stimf','post','recovery']].values];\n", "plt.plot([-1,4],[0,0],color='grey')\n", "colhead = plotdf.columns.to_list()\n", "for i,c in enumerate(colhead):\n", " q = plotdf[c].quantile([.25, .5,0.75]) \n", " ax.plot([i-0.2,i+0.2],[q.values[1],q.values[1]],color = 'red',lw = 3,zorder=30)\n", " ax.plot([i,i],[q.values[1],q.values[0]],color = 'red',lw = 3,zorder=30)\n", " ax.plot([i,i],[q.values[1],q.values[2]],color = 'red',lw = 3,zorder=30)\n", "plt.ylim(-2.5,6)\n", "plt.savefig('Figures/revisions/scatter_aenFL_spkrates_.eps', format='eps', dpi=1000)\n", "\n", "\n", "######STATS for population data#############\n", "thisdf = alldf[((alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')&\n", " (alldf.phase=='phase1'))]\n", "\n", "display(thisdf[['spkrt_base','spkrt_change_stimi_base','spkrt_change_stimf_stimi',\n", " 'spkrt_change_post_base']].describe())\n", "\n", "print('aen')\n", "print('n = ' + str(len(thisdf)))\n", "print('post-pairing to base: H0 = post-pairing same as base')\n", "r = scipy.stats.wilcoxon(thisdf['spkrt_post'].values,thisdf['spkrt_base'].values)\n", "print(r)\n", "\n", "print('aen')\n", "print('stimulus initial to base: H0 = stimi same as base')\n", "r = scipy.stats.wilcoxon(thisdf['spkrt_stimi'].values,thisdf['spkrt_base'].values)\n", "print(r)\n", "\n", "print('aen')\n", "print('stimulus change: H0 = stimf same as stimi')\n", "r = scipy.stats.wilcoxon(thisdf['spkrt_stimf'].values,thisdf['spkrt_stimi'].values)\n", "print(r)\n", "\n", "print('aen')\n", "print('stimulus final to base: H0 = stimf same as base')\n", "r = scipy.stats.wilcoxon(thisdf['spkrt_stimf'].values,thisdf['spkrt_base'].values)\n", "print(r)\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "was there a difference between these conditions (vent or fin lift) for spike rates postpairing?\n", "n1=21 ventilation; n2=6 fin lift\n", "MannwhitneyuResult(statistic=56.0, pvalue=0.35231137054800765)\n" ] } ], "source": [ "r = stats.mannwhitneyu(alldf[((alldf.phase=='phase1')\n", " &(alldf.trigger_type=='paired')\n", " &(alldf.condition=='aen_vent'))]['spkrt_change_post_base'].values,\n", "alldf[((alldf.phase=='phase1')\n", " &(alldf.trigger_type=='paired')\n", " &(alldf.condition=='aen_proprio'))]['spkrt_change_post_base'].values)\n", "print('was there a difference between these conditions (vent or fin lift) for spike rates postpairing?')\n", "print('n1=21 ventilation; n2=6 fin lift')\n", "print(r)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n = 5\n", "\n", "compare test versus control change in post-pairing and baseline \n", "Is change in post-pairing response from baseline different for control versus test AENs?\n", " wilcoxon compared within cells with baseline subtracted from post\n", "WilcoxonResult(statistic=1.0, pvalue=0.6547208460185769)\n", "\n", "compare each against median zero\n", "aen test\n", "WilcoxonResult(statistic=0.0, pvalue=0.17971249487899976)\n", "sample size <20; norm distribution p = 0.17971249487899976\n", "for n = 5, Wcritical = 0 at alpha = 0.05; Wcritical = 0 at alpha = 0.1\n", "\n", "aen control\n", "WilcoxonResult(statistic=0.0, pvalue=0.17971249487899976)\n", "sample size <20; norm distribution p = 0.17971249487899976\n", "for n = 5, Wcritical = 0 at alpha = 0.05; Wcritical = 0 at alpha = 0.1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n" ] }, { "data": { "image/png": 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\n", 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hO6rqAWAwMBE4F2cWAZNC/O54Dt9UXQ8ePGh9XcvgJSHTRSQdJyHHqeoJYusMYAKsbt26XHrppSxevJgdO3b4dt5Q1XXhwoVWdS2Fl4R8DliPM7HVDHflq9hXcjGBlZeXR40aNZg8ebKv583Pz6dRo0ZWdS2Flzl1nlLVFqp6tft8cwPOrAEmxWRkZJCXl8fq1atZv369b+etWrUqgwcPtqprKbzMqVNXRJ4IdU8TkcexaSBTVo8ePahdu7ZvHc9DrOpaOi9V1heBg8BQ93UAeCkeQZnkS09Pp3///mzZssX3FZSt6loyLwnZRlUfVmeo1Fr3eeR58QrMJF/nzp1p3LixLzOeh7NW15J5ScijIpIX+uAucW4TqKSwKlWqMHDgQPbs2cPChQt9PXfLli3p1asXCxcuZPmXy3ll0Sv0e7kf5z55Li2eaEH3Md3565y/sq/Qv+ehFYFE+/tARLrgDBSuizMk6mvgFlVdEr/wYpOTk6Pz589PdhgpQVV56aWX2Lt3L3fffTfVqlXz7dxFRUXc/rfbeefwOxzWwxHL1Eyvya/zfs2DfR/07brJJiIFqpoTaZ+XVtZFqtoZuBjopKpdg5iMxl+hjueHDh3yteM5wOiFo3n10KslJiPAkRNHeGjqQ9zzn3t8vXZQeWllbSgiTwHTgKki8jcRsZH7lcC5557LBRdcwKxZs3zreD5n8xzu/s/dUZf/+9y/88qiV8ouWMF5+Q35Os7Uj0NwRnnsAt6IR1AmeEIdzz/99FNfzvfknCcpVm8NRU/MecKXaweZl4RsoKqPquo69/U7oF68AjPB0rhxY7p06cK8efPK3fF8+6HtvLviXc/HLdmxhJkb/RuJEkReEnKqiHxXRKq4r6HAhHgFZoInPz8fEWHq1KnlOs+09dM4URzbzAQffZXaj0m8JOSPgLHAMff1OnCviBwUEevTWgnUqVOHSy+9lCVLlrB9+/aYz3PgWOx/XcpzbEXgpZW1tqpWUdV091XF3VZbVeuIyIXxDNQEQ25ubrk7nmemx97jsla1WjEfWxH4OQ3kP308lwmojIwM+vTpw5o1a1i3bl1M57i05aVITLOGQs+WPWM6rqJI9ryspgLq0aMHderUibnj+fkNzufyNpd7Pi67XjZXt73a83EVSbJnLjcVUNWqVenfvz9bt25l+fLlMZ3jnh7eH/SPyBlBFfHzr2zwpPa3M3Fz8cUX06RJE6ZMmcLJkyc9H39Nu2u4t+e9UZe/tt213Nsr+vIVlZ8JedzHc5mAC3U8//rrr1mwYEFM53j8isd5qM9DZd71hl88nHeGvkNalXJNA1wheOk6JyIyXER+434+V0R6hParamr/2jZnadu2La1atWL69OkcPx7bv8ePDniUtfes5Vd5v6JJZpNT22tXq82Pu/2YJT9ewj+v/yfV0vzr1B5kXkZ7PAsUAwNUtYOI1Ac+UtXu8QwwFjbaI3E2b97MCy+8QH5+Pv369Sv3+Y6eOMpJPZnSjzd8Ge0BXKqqdwGFAKq6F2dWc1OJtWzZkg4dOvDZZ59x+HDJozailZGekdLJWBYvCXnCXfFYAUSkMc4dMyYi0kBEPhaR1e6f9Usod1JEFrmvMxfiMQEwYMAATpw4wYwZM5IdSoXnJSGfAt4FmojIY8BMIJYVr0IeACaraltgMiUvonNUVbu4r2+XUMYkUaNGjejatSvz589n7969yQ6nQqsabUFVfU1ECoCBOJ0ABqvqinJcexCQ775/BWec5S/Lcb6Utmn/JsYsGMOSHUsoLCqkYc2GDLpgENe3v570tPRkh0d+fj5Llixh6tSp3HDDDckOp8KKOiFFpAGwE/hX2LZ0dwbzWDRV1W0AqrpNRJqUUK6GiMwHioA/qup7MV6vQtp8YDM/mfgTPvjyA07q6c/7xi4dS/Nazbmv9338vNfPkxSho3bt2vTs2ZOZM2fSq1cvmjdvntR4KiovVdYFOIOSvwRWu+/XicgCEekW6QAR+UREvojwGuThuue6LVLDgL+KSJsSrpW0Jc3jZc3Xa+j5fE/GrRp3VjKGbDu0jXs/upc7P7gzwdGdLTc3l4yMDN9nPK9MvCTkh8DVqtpIVRsCVwFvAiOAZyIdoKqXqepFEV7jgB0i0hzA/XNnCefY6v65Fqda27WEcqNVNUdVcxo3buzhawXT0RNHueq1q9hycEtU5UcVjOJ/P/vfOEdVuho1atCnTx+++uor1q61ZTxj4SUhc1T11KqbqvoR0FdV5wDVY7h2+FLltwDjziwgIvVFpLr7vhGQi7MUXsobu3Qsa772NrP3n2f9mWNFx+IUUXS6d+9O3bp1fZ/xvLLwkpBfi8gvRaSV+7of2Os+Conl8ccfgctFZDVwufsZEckRkefdMh2A+SKyGJiK8xuyUiTkM/MjVjpKtevILt5a/lYcooleqOP5tm3bWLZsWVJjqYi8JOQwoCXwHs7d7Fx3WxrO0gKeqOoeVR2oqm3dP792t89X1R+67z9T1U6q2tn98wWv16mINuzbwIJtsfUPfWfFOz5H412nTp1o2rSp547nG/dvZMaGGUxdN5VVu1fFMcLg8vLYYzdQ0rx9tmqKj/Yc3RPzsdv3b+f48eO+TmjsVajj+dixYykoKKBHjx4lli3WYsatHMcz859h8trJaNgovpysHO7MuZNhnYZRo2qNRISedF4ee7QDfgFkhx+nqgP8D6tyK09H6p3bdvKHP/yBGjVqULduXerWrUudOnVOvQ99rl27Nmlp8Rs9cf7555Odnc306dPp3Lkz1auf3cxw+PhhvvP2d5iwOvJcafO3zuf292/nH3P/wYRhE2heO/UfpUSdkMBbwCjgecD7ADgTtex62WSmZ3L4hPe+ob3a9GJg9kD279/PgQMH2L9/Pxs3bjxrlSkRoXbt2qeS9cykrVu3LhkZGYjENhFEaMbz559/ntmzZ59aKj2kqLiIIW8OYdJXkyKfIMzC7Qu57J+XMeu2WdSrkdozj3pJyCJVfTZukZhTalWrxbBOwxizYIznYx+84kE6NO5w1vbjx4+flqTh77dt28bKlSvP+r1XtWrViHfZ8Pfp6SX3EmrRogUdO3bks88+Iycnh1q1vuk0/vKil6NKxpDlu5bz2+m/5YkrUnuyZC/Dr0biPCt8F2caSABCjTFBkgrDrxZvX0yX57p4OqZ/dn+m3DIlpuupKkeOHDmVrJGS9+DBg2cdl5GRUWrV+Pjx4zz77LPk5ORw9dXfzIdzyXOXsHC7txW16teoz5Z7t5CRnhHTdwyK0oZfeblDhp4Z3he2TbE1IuOic7POPNjnQR779LGoyjfMaMioa0fFfD0RITMzk8zMTLKysiKWOXnyJAcOHDgtUUPJunfvXtavX8+xY8fOOm96ejrz5s1j//79NGrUiC26xXMyAuwt3Muby97kli63lF24gvLSyto6noGYs/1uwO9QVX4/s/RBNVm1s5g4bCLtGraLazxpaWnUr1+f+vUjjpQD4NixY2cl6549e1ixYgXr1q1jzZo1LCiO7ZEOwIrd5RnPEHxe7pCIyEVAR+BUG7Sqvup3UOYbjw18jOs7XM/T857mjS/e4GjRN2vktmvYjjtz7uTWLrcGprGjevXqNGnShCZNTh8rMHXqVGbMmMEPf/hD6q2vx3ufxDZGINk9keLNy2OPh3GGS3UEJuL0ZZ0JWELGWU5WDi8Neoknr3iS1XtWnxp+1aFRh5hbQROtd+/ezJ8/n8mTJ9Oie4uYz9OoZiMfowoeLz11bsQZC7ldVX8AdCa2PqwmRvVq1KN7i+70adWHjo07VphkBOfO2bdvX9atW8f5cj51qteJ6Tw3drzR58iCxUtCHlXVYqBIROrgtLhag46JWrdu3ahXrx6zp89m+MXDPR8/oPUALmh0QRwiCw4vCTlfROoBY4ACnPGRc+MSlUlJVatWZcCAAWzfvp1r6l/j6S6ZJmk81OehOEYXDF5WvxqhqvtUdRTO6Ixb3KqrMVG76KKLaNasGavnrubtG9+OaiWsNElj9HWj6d+6fwIiTC5PM5eLSAsR6Y0z0qOeiPSNT1gmVYW61O3bt486e+ow4wcz6Neq5PlcOzftzPs3v89tXW9LYJTJ46WV9U/Ad3AGCIf6WClgc/8ZT8477zxat27NjBkzuOeee5h26zSW71rOiwtfZN2+dZwsPknzWs0ZfvFwcs/NTXa4CeWl69wq4GJVDfyDoFToOpfqtm7dypgxY+jbty/9+6d+VTScXzOXrwWSP9+gSQlZWVlceOGFzJ49m0OHDiU7nMAos8oqIn/HqZoeARaJyGRO71zufaE/Y3BmPF+xYgXTp0/nmmuuSXY4gRDNb8hQ3a8AZ2IqY3zRoEEDunXrxmfzPuPL2l/y5uo3Wbd3HSf1JFm1s/hep+9xe9fbaVizYbJDTZgyE1JVXwEQkUygUNWZINSd3Mp66phy2dBgA4/zOIVTTx9AvfvIbpbsWMLD0x7m/t73MzJ/ZIXqmRQrL78hJwPhA9EygE/8DcdUJn+b8zdGTBpBIYUlliksKuS3M37LiAkjEhhZ8nhJyBqqeurXt/u+pv8hmcpg1sZZ3PtR9EuUjyoYxYsLX4xjRMHgJSEPi8gloQ/u8gFHSylvTImenPMkxeptOt8nZqf29B3gbTzkz4C3RGSr+7k5TkcBYzzZenAr41adNVF9mZbtWsb09dPpl13+lZqDyktf1nlAe+BOnPU8OqhqQWi/iFzuf3gmFU1fP52i4qKYjp28LrUX8vHUl1VVT6jqF6q6NMIydH/yMS6Twg4eP3uyrGgdOHbAx0iCx1NClsFTm7SI3CQiy0SkWEQidiNyy10pIqtEZI2IlLTKsqlAohnhUZJa1WqVXagC8zMhvS519AVwA6V0TnefdT6NM11IR+BmEekYc4QmEHqd0wvx9u/3Kb3P6e1zNMHiZ0J6oqorVLWsFVV6AGtUda2qHgdex1kK3VRg59U/jyvOvyKm4648/8o4RBQcUSdkaJ3GUrat9yOgM7QANoV93uxuMxXcPT28d4G+q/tdVJGk3UMSwsu3m13aNlW94cydPixpHqleE7FqnIpLmqeyq9pexf2974+6/PXtr+dnPX8Wx4iCIZrRHs1w7koZItKVb5KkDmX01FHVy8oZ32bgnLDPLYGtkQqq6mhgNDjjIct5XZMAf7r8T9SuXptHpj9S6mOQ27rcxqhrR6X83RGi6xhwBXArTjI8zjcJeQD4dXzCOmUe0FZEWgNbgO/iLBJrUsRDfR/itq63MbpgNC8ufJFNB5xfKA0yGjC803BGdB+R8jPNhYtqxgARqQLcrKqv+XZhkeuBvwONgX3AIlW9QkSygOdV9Wq33NXAX3FWan5RVctc7MJmDKi4ioqLKNbicq2RGXSlzRjgZQqPGapaISa1soQ0QebXFB4fi8gvROQcEWkQevkUozEGb53LQ/Pw3RW2zZajM8ZHXhLyPD2jfisiNUoqbIzxzkuV9YXwD+6UHhP8DceYys1LQm4RkWcBRKQ+8DHwf3GJyphKyst4yP8BDojIKOAj4HFVfSlukRlTCUXTUye8S9xc4H/cP1VEblDVf8crOGMqm2gada474/NCnBnMr8NpZbWENMYn0czLakvOGZMgXoZfnSci40Vkl4jsFJFxbh9TY4xPvLSyjgXexJltLgt4C2fAsDHGJ14SUlT1n6pa5L7+D+/TdhhjSuGlp85Ud5Kp13ES8TvAhFB/VlX9Og7xGVOpeEnI0KTIP+KbO6Pg9HG1Pq3G+MBLlfWXQGdVbQ28BCwGhqhqa1W1ZDTGB14S8iFVPSAiecDlwMvAs3GJyphKyktCnnT/vAYYparjgNQd1m1MEnjtXP4cMBSY6E4BmfqzDhmTQF4SaigwCbhSVfcBDYD74hKVMZVU1K2sqnqEsH6rqroN2BaPoIyprKzKaUyAWEIaEyCWkMYEiCWkMQFiCWlMgFhCGhMglpDGBEjSElJEbhKRZSJSLCIR1zlwy60XkaUiskhEbMEOk9K8DL/y2xfADcBzUZTtr6q74xyPMUmXtIRU1RUAIpEWSTamcqoIvyEV+EhECkTkjpIK2ZLmJhXE9Q4pIp8AzSLsetAdvhWNXFXdKiJNcJbEW6mqM84sZEuamwqhwjIAAAe5SURBVFQQ14RU1ct8OMdW98+dIvIu0AM4KyGNSQWBrrKKSKaI1A69B76F0xhkTEpK5mOP60VkM9ALZ/a6Se72LBGZ6BZrCswUkcU464lMUNUPkxOxMfGXzFbWd4F3I2zfClztvl8LdE5waMYkTaCrrMZUNpaQxgSIJaQxAWIJaUyAWEIaEyCWkMYEiCWkMQFiCWlMgFhCGhMglpDGBIglpDEBYglpTIBYQhoTIJaQxgSIJaQxAWIJaUyAWEIaEyCWkMYEiCWkMQFiCWlMgFhCGhMglpDGBIglpDEBYglpTIBYQhoTIJaQxgRIMtf2+IuIrBSRJSLyrojUK6HclSKySkTWiMgDiY7TmERK5h3yY+AiVb0Y+BL41ZkFRCQNeBq4CugI3CwiHRMapTEJlLSEVNWPVLXI/TgHaBmhWA9gjaquVdXjwOvAoETFaEyiJW31qzPcBrwRYXsLYFPY583ApZFO4C53Hlry/JCIrPI1wmBpBOxOdhBxlOrfr1VJO5K+pLmIPAgUAa9FOkWEbRGXKw9f0jzVich8Vc1JdhzxkurfrzRJXdJcRG4BrgUGqmqkRNsMnBP2uSWw1b8IjQmWZLayXgn8Evi2qh4podg8oK2ItBaRasB3gfcTFaMxiZbMVtZ/ALWBj0VkkYiMgtOXNHcbfX4CTAJWAG+q6rJkBRwgqV41T/XvVyKJXFM0xiSD9dQxJkAsIY0JEEvIBBORe0RkhYhEesxTaYnIb0Wk1Fb5KM5xyK94ksV+QyaYiKwErlLVdWHbqob1WorlnILz/7LYjxiDRETSVPVklGUPqWqteMcUT3aHTCC3Jfk84H0R2S8io0XkI+BVEUlzO9zPczvc/yjsuPvCtj/ibst277TPAAuAc8LvECJyo4i87L5/WUSeEpHPRGStiNyYgO+a7Q4eeMWN+20RqSkiv3G/yxfu95ewGG903693y80EbhKRNiLyoYgUiMinItLeLddaRGa753s03t8pESwhE0hVf4zTsaE/8CTQDRikqsOA24H9qtod6A78l/sX7ltAW5x+vV2AbiLS1z3lBcCrqtpVVTeUcfnmQB5OR4w/+vzVSnIBMNodQHAAGAH8Q1W7q+pFQIYbTySFqpqnqq/jPAa5W1W7Ab8AnnHL/A141v1vtj2eXyRRgtKXtbJ6X1WPuu+/BVwcdveqi5OI33JfC93ttdztG4ENqjonymu951Zpl4tIU1+iL9smVZ3lvv8/4B5gnYjcD9QEGgDLgPERjn0DQERqAb2Bt9ybKUB1989cYIj7/p/An/z+AolmCZlch8PeC85dYFJ4ARG5AviDqj53xvbsM46H0/v51jhj37EzrpUIZzZQKM7dLUdVN4nISM6OMyT03aoA+1S1S5TXqNCsyhock4A7RSQdQETaiUimu/02906BiLQQkSYlnGOHiHQQkSrA9QmJunTnikgv9/3NwEz3/W73+5T5W1ZVD+DcVW8CpwFLRDq7u2fhdKcE+J5/YSePJWRwPA8sBxaIyBfAc0BVVf0IGAvMFpGlwNs4XQ4jeQD4AJgCbIt/yGVaAdwiIktwqqfPAmOApcB7OH2Vo/E94HYRWYxTxQ2Nif0pcJeIzMOp4ld49tjDxIVbpf7AbbwxUbI7pDEBYndIYwLE7pDGBIglpDEBYglpTIBYQhoTIJaQFYiI1BOREaXs/yyR8YRdN1tEhiXj2qnGErJiqYfTQfs07gzvqGrveF5cRErqapkNWEL6wB57VCAiEpq5fRVwAjiE0yOni6p2DI0HdLvO/QPoB6zD+Yf3RVV9W0SuBp7AmYh4AXCeql7rdtP7O9AJp4/zSFUdJyK3Atfg9DnNVNUBEeKaA3Rwr/UKcANOv9xF7v5ZwJ3u9jY4E2CfA/xZVce4Ze4DhuJ0HH9XVR/28T9dxaGq9qogL5w70Rfu+3ycDtitw/Yfcv+8EZiIk4jNgL3utho4M8G3dsv9C6c3DcDvgeHu+3o4661kArfizI/boJS48kPncT/fAvzVfd8OmO++Hwksxhl21ciNJQtnNMtonE7vVXC6//VN9n/vZLysylqxzdWwmQfC5AFvqWqxqm4Hprrb2wNrw475V9gx3wIeEJFFwDSc5D3X3fexqn7tIa63gGvdjvK3AS+H7RunqkdVdbcbVw9OH2K2wI2zrYfrpQwbflWxnTn8KqSk4VWlDbsSYIiqnrYmiohcWsp1IlLVIyLyMU71eigQvixApCFZQoQhZpWR3SErloOUPNIj3ExgiIhUcQcj57vbVwLnuR2/Ab4Tdswk4O6wKTW6ljOu54GngHln3F0HiUgNEWnoxjUPb0PMUprdISsQVd0jIrPc4VlHgR0lFH0HGAh8gfNb8HOc6UGOuo9NPhSR3cDcsGMeBf4KLHGTcj0lT69xpiVAkTs86mVVfVJVC0TkAPDSGWXnAhNwqsOPqupWYKuIdMAZYgZOY9VwYGeU108Z1sqaokSklqoecu9Ec4FcVd0etl1wFsNdrapPxuH6WTi/RdurOxueO0PAIVX9X7+vlyqsypq6PnAbaD7FuROFJoH6L3f7MpxBvb7/bhOR7+PclR/UFJyaMp7sDmmiJiKdcCaTCndMVSMuomu8s4Q0JkCsympMgFhCGhMglpDGBIglpDEB8v8BJuqoGHxUuk8AAAAASUVORK5CYII=\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#############################\n", "######### aens in which have control/freerun condition ###########\n", "thisdf = alldf[(alldf.control=='yes')]\n", "\n", "plt.figure(figsize=(3,4))\n", "sns.stripplot(x='trigger_type',y='spkrt_change_post_base',data = thisdf,\n", " jitter=False,s=15,color='black')\n", "[plt.plot([0,1],[x,y],color = 'gray') for x,y \n", " in zip(thisdf[thisdf.trigger_type=='freerun']['spkrt_change_post_base'].values,\n", " thisdf[thisdf.trigger_type=='paired']['spkrt_change_post_base'].values)]\n", "plt.xlim(-1,2)\n", "plt.ylim(-2,1.1)\n", "plt.plot([-0.5,1.5],[0,0],color = 'black',lw = 1,linestyle='--')\n", "plt.savefig('Figures/revisions/scatter_aen_spkrates_controls.eps', format='eps', dpi=1000)\n", "\n", "#####color by condition\n", "thisdf = alldf[(alldf.control=='yes')&(alldf.condition=='aen_vent')]\n", "\n", "plt.figure(figsize=(3,4))\n", "sns.stripplot(x='trigger_type',y='spkrt_change_post_base',data = thisdf,\n", " jitter=False,s=15,color='green')\n", "[plt.plot([0,1],[x,y],color = 'gray') for x,y \n", " in zip(thisdf[thisdf.trigger_type=='freerun']['spkrt_change_post_base'].values,\n", " thisdf[thisdf.trigger_type=='paired']['spkrt_change_post_base'].values)]\n", "plt.xlim(-1,2)\n", "plt.ylim(-2,1.1)\n", "plt.plot([-0.5,1.5],[0,0],color = 'black',lw = 1,linestyle='--')\n", "plt.savefig('Figures/revisions/scatter_aenV_spkrates_controls.eps', format='eps', dpi=1000)\n", "\n", "#############################\n", "#########aens in which have control###########\n", "thisdf = alldf[(alldf.control=='yes')&(alldf.condition=='aen_proprio')]\n", "\n", "plt.figure(figsize=(3,4))\n", "sns.stripplot(x='trigger_type',y='spkrt_change_post_base',data = thisdf,\n", " jitter=False,s=15,color='blue')\n", "[plt.plot([0,1],[x,y],color = 'gray') for x,y \n", " in zip(thisdf[thisdf.trigger_type=='paired']['spkrt_change_post_base'].values,\n", " thisdf[thisdf.trigger_type=='freerun']['spkrt_change_post_base'].values)]\n", "plt.xlim(-1,2)\n", "plt.ylim(-2,1.1)\n", "plt.plot([-0.5,1.5],[0,0],color = 'black',lw = 1,linestyle='--')\n", "plt.savefig('Figures/revisions/scatter_aenFL_spkrates_controls.eps', format='eps', dpi=1000)\n", "\n", "########STATS########\n", "#verbose stat tests for the single cell level changes compared across groups\n", "print('n = 5')\n", "print('')\n", "print('compare test versus control change in post-pairing and baseline ')\n", "print('Is change in post-pairing response from baseline different for control versus test AENs?')\n", "print(' wilcoxon compared within cells with baseline subtracted from post')\n", "r = scipy.stats.wilcoxon(thisdf[thisdf.trigger_type=='paired']['spkrt_change_post_base'].values,\n", " thisdf[thisdf.trigger_type=='freerun']['spkrt_change_post_base'].values)\n", "print(r)\n", "print('')\n", "print('compare each against median zero')\n", "print('aen test')\n", "r = scipy.stats.wilcoxon(thisdf[thisdf.trigger_type=='paired']['spkrt_change_post_base'].values)\n", "print(r)\n", "print('sample size <20; norm distribution p = ' + str(r[1]))\n", "print('for n = 5, Wcritical = 0 at alpha = 0.05; Wcritical = 0 at alpha = 0.1')\n", "print('')\n", "print('aen control')\n", "r = scipy.stats.wilcoxon(thisdf[thisdf.trigger_type=='freerun']['spkrt_change_post_base'].values)\n", "print(r)\n", "print('sample size <20; norm distribution p = ' + str(r[1]))\n", "print('for n = 5, Wcritical = 0 at alpha = 0.05; Wcritical = 0 at alpha = 0.1')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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spkrt_basespkrt_change_stimi_basespkrt_change_stimf_stimispkrt_change_post_base
count9.0000009.0000009.0000009.000000
mean10.8875920.809815-0.536576-1.047995
std10.7746432.9636451.4528093.034190
min0.420904-5.031473-3.540000-7.864806
25%2.132622-0.638889-1.457778-2.552682
50%7.3627910.514120-0.0851060.223277
75%14.9722223.2324290.1555560.559028
max33.8081405.0038761.2222221.819562
\n", "
" ], "text/plain": [ " spkrt_base spkrt_change_stimi_base spkrt_change_stimf_stimi \\\n", "count 9.000000 9.000000 9.000000 \n", "mean 10.887592 0.809815 -0.536576 \n", "std 10.774643 2.963645 1.452809 \n", "min 0.420904 -5.031473 -3.540000 \n", "25% 2.132622 -0.638889 -1.457778 \n", "50% 7.362791 0.514120 -0.085106 \n", "75% 14.972222 3.232429 0.155556 \n", "max 33.808140 5.003876 1.222222 \n", "\n", " spkrt_change_post_base \n", "count 9.000000 \n", "mean -1.047995 \n", "std 3.034190 \n", "min -7.864806 \n", "25% -2.552682 \n", "50% 0.223277 \n", "75% 0.559028 \n", "max 1.819562 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "n = 9\n", "sample size <20; cannot use norm distribution for p\n", "for n = 10, Wcritical = 8 at alpha = 0.05; Wcritical = 10 at alpha = 0.1\n", "afferent\n", "post-pairing to base: H0 = post-pairing same as base\n", "WilcoxonResult(statistic=19.0, pvalue=0.6784023758521882)\n", "Ttest_relResult(statistic=-1.0361857021445595, pvalue=0.3304183976626629)\n", "\n", "afferent\n", "stimulus to base: H0 = stimi same as base\n", "WilcoxonResult(statistic=16.0, pvalue=0.44126813332892967)\n", "Ttest_relResult(statistic=0.8197490170289373, pvalue=0.43609942456154926)\n", "\n", "afferent\n", "change stimulus: H0 = stimf same as stimi\n", "WilcoxonResult(statistic=14.0, pvalue=0.3139380937749148)\n", "Ttest_relResult(statistic=-1.108009865435005, pvalue=0.30005515074814754)\n", "\n", "outlier stiminitial respnse\n", "22 20070719_91_coupled_SPK.mat\n", "Name: exptname, dtype: object\n", "outlier stim change respnse\n", "Series([], Name: exptname, dtype: object)\n", "outlier post pairing change \n", "22 20070719_91_coupled_SPK.mat\n", "Name: exptname, dtype: object\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n" ] }, { "data": { "text/plain": [ "(-1, 1)" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#######afferents changes in spike rate#########\n", "thisdf = alldf[(alldf.cell=='afferent')] \n", "\n", "display(thisdf[['spkrt_base','spkrt_change_stimi_base','spkrt_change_stimf_stimi',\n", " 'spkrt_change_post_base']].describe())\n", "\n", "#plot changes in spike rate for each period\n", "plotdf = thisdf[['spkrt_change_stimi_base','spkrt_change_stimf_base',\n", " 'spkrt_change_post_base']]\n", "\n", "fig = plt.figure(figsize=(4,5))\n", "ax = sns.stripplot(data=plotdf,color='black',s=10,alpha=1,jitter=0.2)\n", "plt.plot([-1,4],[0,0],color='grey')\n", "colhead = plotdf.columns.to_list()\n", "for i,c in enumerate(colhead):\n", " q = plotdf[c].quantile([.25, .5,0.75]) \n", " ax.plot([i-0.2,i+0.2],[q.values[1],q.values[1]],color = 'red',lw = 3,zorder=30)\n", " ax.plot([i,i],[q.values[1],q.values[0]],color = 'red',lw = 3,zorder=30)\n", " ax.plot([i,i],[q.values[1],q.values[2]],color = 'red',lw = 3,zorder=30)\n", "# plt.ylim(-2.5,6)\n", "plt.savefig('Figures/revisions/scatter_afferent_spkrates_.eps', format='eps', dpi=1000)\n", "\n", "\n", "lims = [-2,35]\n", "fig= plt.figure(figsize = (3,3))\n", "ax = plt.gca()\n", "data = np.asarray([thisdf['spkrt_stimi'].values,thisdf['spkrt_stimf'].values]).T\n", "plt.scatter(data[:,0],data[:,1],color='black')\n", "plt.plot([lims[0],lims[1]],[lims[0],lims[1]])\n", "plt.xlabel('stim initial')\n", "plt.ylabel('stim final')\n", "\n", "\n", "print('n = ' + str(len(thisdf)))\n", "print('sample size <20; cannot use norm distribution for p')\n", "print('for n = 10, Wcritical = 8 at alpha = 0.05; Wcritical = 10 at alpha = 0.1')\n", "\n", "print('afferent')\n", "print('post-pairing to base: H0 = post-pairing same as base')\n", "r = scipy.stats.wilcoxon(thisdf['spkrt_post'].values,thisdf['spkrt_base'].values)\n", "print(r)\n", "r = scipy.stats.ttest_rel(thisdf['spkrt_post'].values,thisdf['spkrt_base'].values)\n", "print(r)\n", "print('')\n", "\n", "print('afferent')\n", "print('stimulus to base: H0 = stimi same as base')\n", "r = scipy.stats.wilcoxon(thisdf['spkrt_stimi'].values,thisdf['spkrt_base'].values)\n", "print(r)\n", "r = scipy.stats.ttest_rel(thisdf['spkrt_stimi'].values,thisdf['spkrt_base'].values)\n", "print(r)\n", "print('')\n", "\n", "print('afferent')\n", "print('change stimulus: H0 = stimf same as stimi')\n", "r = scipy.stats.wilcoxon(thisdf['spkrt_stimf'].values,thisdf['spkrt_stimi'].values)\n", "print(r)\n", "r = scipy.stats.ttest_rel(thisdf['spkrt_stimf'].values,thisdf['spkrt_stimi'].values)\n", "print(r)\n", "print('')\n", "\n", "print('outlier stiminitial respnse')\n", "print(thisdf[(thisdf['spkrt_stimi'].values-thisdf['spkrt_base'].values)<-4].exptname)\n", "\n", "print('outlier stim change respnse')\n", "print(thisdf[(thisdf['spkrt_stimf'].values-thisdf['spkrt_stimi'].values)<-4].exptname)\n", "\n", "print('outlier post pairing change ')\n", "print(thisdf[(thisdf['spkrt_post'].values-thisdf['spkrt_base'].values)<-4].exptname)\n", "\n", "thisdf[(thisdf['spkrt_post'].values-thisdf['spkrt_base'].values)<-4].spkrt_base\n", "\n", "plt.figure(figsize=(4,4))\n", "ax = sns.stripplot(x='cell',y='spkrt_base',data=thisdf,color='black',s=10,alpha=0.6,jitter=0.1)\n", "plt.scatter(-0.2,thisdf[(thisdf['spkrt_post'].values-thisdf['spkrt_base'].values)<-4].spkrt_base,\n", " marker='>',color='red')\n", "plt.xlim(-1,1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Fig 4C&D: Examine changes in temporal structure of responses** \n", "
\n", "\n", "First, to examine whether the observed poststimulus changes in AEN spike rate (Fig. 4A) preserved the temporal specificity of the stimulus-driven response, we estimated the trial-averaged spike rate per 200msec bin in each period of the experiment in each cell (Fig. 4C&D). We then aligned responses across cells according to the bin in which the maximum stimulus-driven spike rate was evoked. In the stimulus period, spike rates in this center bin were initially 10Hz (6.3--13; n=27 cells) above prestimulus rates (W=0; p<0.001; n=27 cells). Consistent with the previous results, the peak stimulus-driven spike rates decreased significantly by 1.1Hz (3.5 -- +0.3; n=27 cells) by the end of the stimulus period (Fig. 4C; center bin: W = 96; p = 0.025; n = 27 cells). As would be predicted by a temporally specific cancellation mechanism, we found that the poststimulus response was decreased relative to the prestimulus response only in the bins including and immediately surrounding the peak stimulus-driven response (Fig. 4C; center bin: -0.6Hz (-1.2 -- -0.2); W = 65; p = 0.003; n = 27 cells). Across the afferent population, peak stimulus-driven spike rates were also significantly elevated above prestimulus rates by 7.7Hz (7.3, 9.9) in the center bin (W=0; critical W at alpha(0.05) = 6 for n=9; Fig. 4D). However, this response did not decrease significantly by the end of the stimulus period (center bin: -1.7Hz, -2.6 -- -0.3; W = 9; critical W at alpha(0.05) = 6 for n=9). Finally, unlike AENs, afferents exhibited no difference between the poststimulus spiking response and the prestimulus spiking response in this (or any other) bins (Fig. 4D; center bin: -0.1Hz, -2.4 -- +0.2; W = 17; critical W at alpha(0.05) = 6 for n=9). These results indicate that changes in AEN spike rate cannot be accounted for by changes in the afferent input. These results are consistent with the generation of a cancellation signal within AENs themselves. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#functions for making and plotting raw histogram data\n", "def get_response_hist(trigger_times,data_times,binw,trigger,trial_duration):\n", " #binw in seconds\n", " aligned_times = spiketimes_align(trigger_times,data_times,trigger,trial_duration)\n", " nbins = np.trunc(trial_duration/binw)\n", " r = np.histogram(aligned_times,np.linspace(0,nbins*binw,nbins+1))\n", " yh = r[0]/len(trigger_times)/binw\n", " xc = r[1]-np.mean(np.diff(r[1]))\n", " xc = xc[1:] # [0:len(yh)]\n", " \n", " return yh, xc\n", "\n", "def get_stats_r(pop_response):\n", " p=[]\n", " w=[]\n", " for c in pop_response:\n", " inds = np.where(~np.isnan(c))[0]\n", " if len(inds)>5:\n", " r = stats.wilcoxon(c[inds])\n", " p.append(r[1])\n", " w.append(r[0])\n", " if len(inds)<=5:\n", " p.append(np.nan)\n", " w.append(np.nan)\n", " w = np.asarray(w)\n", " p = np.asarray(p)\n", " \n", " return w,p\n", "\n", "def align_mat(_unaligned_):\n", " #row 0 is xtime\n", " #row 1 i baseline response (not z-scored)\n", " #row 2 is stimulus initial (mean of z-scored trials) which base alignment off of\n", " #row 3 is stimulus final (mean of z-scored trials) \n", " #row 4 is post-pairing (mean of z-scored trials)\n", " #row 5 is recovery (mean of z-scored trials)\n", "\n", " #for each experiment, find length of data and get max length of all data = L\n", " L = np.max([len(mat) for mat in list(_unaligned_.values())])\n", "\n", " #for each experiment, \n", " base_mat = []\n", " stimi_mat = []\n", " stimf_mat = []\n", " postpairing_mat = []\n", " recovery_mat = []\n", " xtime_mat = []\n", "\n", " for mat in list(_unaligned_.values()):\n", " #find the max peak of the stimulus response \n", " sweep = mat[:,2]\n", " l = len(sweep)\n", " c_ind = np.argmax(sweep)\n", " pad_front = L-c_ind\n", " pad_back = L-(l-c_ind)\n", "\n", " #pad front and back of sweeps so that total 2L length\n", " #append each row of sweeps array to appropriate MetaMatrix\n", " sweep = mat[:,2]\n", " stimi_mat.append(np.pad(sweep, (pad_front,pad_back), \n", " 'constant', constant_values=(np.nan, np.nan)))\n", "\n", " sweep = mat[:,0]\n", " thisx = np.pad(sweep, (pad_front,pad_back), \n", " 'constant', constant_values=(np.nan, np.nan))\n", " xtime_mat.append(thisx - sweep[c_ind])\n", "\n", " sweep = mat[:,1]\n", " base_mat.append(np.pad(sweep, (pad_front,pad_back), \n", " 'constant', constant_values=(np.nan, np.nan)))\n", "\n", " sweep = mat[:,3]\n", " stimf_mat.append(np.pad(sweep, (pad_front,pad_back), \n", " 'constant', constant_values=(np.nan, np.nan)))\n", "\n", " sweep = mat[:,4]\n", " postpairing_mat.append(np.pad(sweep, (pad_front,pad_back), \n", " 'constant', constant_values=(np.nan, np.nan)))\n", "\n", " sweep = mat[:,5]\n", " recovery_mat.append(np.pad(sweep, (pad_front,pad_back), \n", " 'constant', constant_values=(np.nan, np.nan)))\n", "\n", " base_ = np.asarray(base_mat).T\n", " stimi_ = np.asarray(stimi_mat).T\n", " stimf_ = np.asarray(stimf_mat).T\n", " postpairing_ = np.asarray(postpairing_mat).T\n", " recovery_ = np.asarray(recovery_mat).T\n", " xtime_ = np.asarray(xtime_mat).T\n", " \n", " return base_, stimi_, stimf_, postpairing_, recovery_, xtime_\n", "\n", "def get_meta_data():\n", " cell = []\n", " condition = []\n", " trigger_type = []\n", " phase = []\n", " exptname = []\n", " \n", " for iexpt,thisexpt_name in enumerate(meta.exptname):\n", " thisexpt = meta.loc[meta['exptname'] == thisexpt_name]\n", " exptname.append(thisexpt_name)\n", " cell.append(thisexpt.cell.values[0])\n", " condition.append(thisexpt.condition.values[0])\n", " trigger_type.append(thisexpt.trigger_type.values[0])\n", " phase.append(thisexpt.phase.values[0])\n", " \n", " df_meta = pd.DataFrame({\n", " 'exptname' : exptname,\n", " 'cell' : cell,\n", " 'condition' : condition,\n", " 'trigger_type' : trigger_type,\n", " 'phase' : phase\n", " })\n", " return df_meta\n", " \n", "def process_meta_data(iexpt,thisexpt_name):\n", "\n", " thisexpt = meta.loc[meta['exptname'] == thisexpt_name]\n", "\n", " trial_duration = thisexpt.trial_duration.values[0]\n", "\n", " basename = thisexpt_name[0:-8]\n", " data_folder = Path.cwd() / basename\n", " file_to_open = data_folder / Path(basename + '.smr')\n", " bl = Spike2IO(file_to_open,try_signal_grouping=False).read_block()\n", " \n", " trigger = []\n", " for sublist in np.asarray([seg.events[[s.name for \n", " s in seg.events].index(thisexpt.chan_trigger.values[0])].magnitude \n", " for seg in bl.segments]):\n", " for item in sublist:\n", " trigger.append(item)\n", " trigger = np.asarray(trigger)\n", " \n", " if thisexpt.spike_times_from.values[0] == 'Spyking-Circus':\n", " spikes = np.load(data_folder / 'spikes.npy')\n", "\n", " if thisexpt.spike_times_from.values[0] == 'Spike2':\n", " spikes = []\n", " for sublist in np.asarray([seg.events[[s.name for \n", " s in seg.events].index(thisexpt.chan_spikes.values[0])].magnitude \n", " for seg in bl.segments]):\n", " for item in sublist:\n", " spikes.append(item)\n", " spikes = np.asarray(spikes)\n", "\n", " postpairing_range = get_range_bounds([thisexpt.post_start.values[0],thisexpt.post_stop.values[0]],trigger)\n", " stim_range = get_range_bounds([thisexpt.stim_start.values[0],thisexpt.stim_stop.values[0]],trigger)\n", " base_range = get_range_bounds([thisexpt.base_start.values[0],thisexpt.base_stop.values[0]],trigger)\n", "\n", " if thisexpt_name == '20060812_3066_SPK.mat':\n", " #removing a few trials in which spike detection mis-triggered\n", " removeinds = np.arange(360,np.max(postpairing_range)-1,1)\n", " [postpairing_range.remove(x) for x in removeinds]\n", " removeinds = [263, 264, 265, 326, 327] #, 360, 401, 407, 408, 409, 423, 424]\n", " [postpairing_range.remove(x) for x in removeinds]\n", " \n", " return (trial_duration, spikes, trigger, base_range, stim_range, postpairing_range)\n", "\n", "def process_raw_responses_hist():\n", " dt = 1/1000 \n", " ntrials_per_period = 75 #this could be a parameter to play with, but want it to be consistent across all experiments\n", "\n", " unaligned_ = {}\n", " binw = 0.2\n", " recovery = []\n", "\n", " for iexpt,thisexpt_name in enumerate(meta.exptname):\n", " (trial_duration, spikes, trigger, base_range, \n", " stim_range, postpairing_range) = process_meta_data(iexpt,thisexpt_name)\n", "\n", " these_trials = base_range\n", " yh, xc = get_response_hist(these_trials,spikes,binw,trigger,trial_duration)\n", " baseline_response = yh\n", "\n", " if thisexpt_name == '20071025_3335_control_SPK.mat': \n", " #not enough baseline recorded so get estimate of baseline from test cell clock trigger baseline\n", " print('using pre pairing expt to get baseline (recovery end)')\n", " suppname = '20071025_3335_SPK.mat'\n", " suppexpt = meta.loc[meta['exptname'] == suppname]\n", " basename = suppname[0:-8]\n", " data_folder = Path.cwd() / basename\n", " supptrigger = np.arange(suppexpt.post_stop.values[0]-250,suppexpt.post_stop.values[0],trial_duration)\n", " suppbase_range = np.arange(0,len(supptrigger),1)\n", " base_range = suppbase_range\n", "\n", " suppspikes = np.load(data_folder / 'spikes.npy')\n", "\n", " baseline_response, xc = get_response_hist(suppbase_range,suppspikes,binw,supptrigger,trial_duration)\n", "\n", " if thisexpt_name == '20071026_3108_control_SPK.mat': \n", " #not enough baseline recorded so get estimate of baseline from test cell clock trigger baseline\n", " print('using pre pairing expt to get baseline')\n", " suppname = '20071026_3108_SPK.mat'\n", " suppexpt = meta.loc[meta['exptname'] == suppname]\n", " basename = suppname[0:-8]\n", " data_folder = Path.cwd() / basename\n", " file_to_open = data_folder / Path(basename + '.smr')\n", " bl = Spike2IO(file_to_open,try_signal_grouping=False).read_block()\n", " supptrigger = np.arange(0,suppexpt.base_stop.values[0],trial_duration)\n", " suppbase_range = np.arange(0,len(supptrigger),1)\n", " base_range = suppbase_range\n", "\n", " suppspikes = []\n", " for sublist in np.asarray([seg.events[[s.name for \n", " s in seg.events].index(suppexpt.chan_spikes.values[0])].magnitude \n", " for seg in bl.segments]):\n", " for item in sublist:\n", " suppspikes.append(item)\n", " suppspikes = np.asarray(suppspikes)\n", "\n", " baseline_response, xc = get_response_hist(suppbase_range,suppspikes,binw,supptrigger,trial_duration) \n", "\n", " ntrials = np.min([len(stim_range),ntrials_per_period*2])\n", " stiminitial_trials = stim_range[0:int(ntrials/2)]\n", " stiminitial_response, xc = get_response_hist(stiminitial_trials,spikes,binw,trigger,trial_duration)\n", "\n", " stimfinal_trials = stim_range[-int(ntrials/2):]\n", " stimfinal_response, xc = get_response_hist(stimfinal_trials,spikes,binw,trigger,trial_duration)\n", "\n", " ntrials = np.min([len(postpairing_range),ntrials_per_period])\n", " postpairing_trials = postpairing_range[0:ntrials]\n", " postpairing_response, xc = get_response_hist(postpairing_trials,spikes,binw,trigger,trial_duration)\n", "\n", " recovery_response = np.empty(len(baseline_response))\n", " if len(postpairing_range)>=200+ntrials_per_period:\n", " recovery_trials = postpairing_range[200:200+ntrials_per_period]\n", " recovery_response, xc = get_response_hist(recovery_trials,spikes,binw,trigger,trial_duration)\n", " recovery.append(True)\n", " if len(postpairing_range)<200+ntrials_per_period:\n", " recovery.append(False)\n", "\n", " thisRmat = np.asarray([xc,\n", " baseline_response,\n", " stiminitial_response,\n", " stimfinal_response,\n", " postpairing_response,\n", " recovery_response])\n", "\n", " unaligned_[thisexpt_name] = thisRmat.T\n", "\n", " return unaligned_\n", "\n", "def do_strip_plot(ind):\n", " plotdf = pd.DataFrame({\n", " 'stim' : this_stimf[ind,:] - this_stimi[ind,:],\n", " 'post' : this_post[ind,:] - this_base[ind,:]\n", " })\n", " fig = plt.figure(figsize=(3,5))\n", " ax = sns.stripplot(data=plotdf,color='black',s=7,alpha=1,jitter=0.2)\n", " plt.plot([-1,2],[0,0],color='grey')\n", " colhead = plotdf.columns.to_list()\n", " for i,c in enumerate(colhead):\n", " q = plotdf[c].quantile([.25, .5,0.75]) \n", " ax.plot([i-0.2,i+0.2],[q.values[1],q.values[1]],color = 'red',lw = 3,zorder=30)\n", " ax.plot([i,i],[q.values[1],q.values[0]],color = 'red',lw = 3,zorder=30)\n", " ax.plot([i,i],[q.values[1],q.values[2]],color = 'red',lw = 3,zorder=30)\n", " return ax" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/neo-0.8.0.dev0-py3.6.egg/neo/rawio/spike2rawio.py:609: RuntimeWarning: overflow encountered in short_scalars\n", " info['time_per_adc']) * 1e-6\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "using pre pairing expt to get baseline (recovery end)\n", "using pre pairing expt to get baseline\n" ] } ], "source": [ "############# do the binned analysis from above functions #######\n", "meta = pd.read_csv('MetaData.csv')\n", "hist_unaligned_ = process_raw_responses_hist()\n", "\n", "df_meta = get_meta_data()\n", "df_meta = df_meta[df_meta.exptname!='20180407_freerun2_SPK.mat']\n", "base_mat, stimi_mat, stimf_mat, postpairing_mat, recovery_mat, xtime_mat = align_mat(hist_unaligned_)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:9: RuntimeWarning: Mean of empty slice\n", " if __name__ == '__main__':\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:11: RuntimeWarning: invalid value encountered in greater\n", " # This is added back by InteractiveShellApp.init_path()\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:11: RuntimeWarning: invalid value encountered in less\n", " # This is added back by InteractiveShellApp.init_path()\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "peak stimulus response bin\n", "n = 27\n", "stimulus-evoked response relative to base\n", "quantiles\n", "[ 6.25522388 9.99735772 12.99284155]\n", "WilcoxonResult(statistic=0.0, pvalue=5.606116527496013e-06)\n", "\n", "change in stimulus-evoked response\n", "quantiles\n", "[-3.53333333 -1.13333333 0.3 ]\n", "WilcoxonResult(statistic=96.0, pvalue=0.025456403148726894)\n", "\n", "change in baseline response after pairing\n", "quantiles\n", "[-1.24252022 -0.58902954 -0.21315193]\n", "WilcoxonResult(statistic=65.0, pvalue=0.0028909652879307935)\n", "\n" ] }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "####sort results for AENs########\n", "celltype = 'aen'\n", "dfinds = df_meta[(df_meta['cell']==celltype)&\n", " (df_meta['trigger_type']=='paired')&\n", " (df_meta['phase']=='phase1')&\n", " (df_meta['condition']!='aen_swimcmd')&\n", " (df_meta['condition']!='aen_ventcmd')].index\n", "\n", "this_x = xtime_mat[:,dfinds]\n", "xtime = np.nanmean(this_x,1)\n", "\n", "#######only plot between -1.5 and 1.5 sec around center bin\n", "xinds = np.where((xtime>-1.5)&(xtime<1.5))[0]\n", "xtime = xtime[xinds]\n", "binw = np.round(np.mean(np.diff(xtime)),3)\n", "\n", "this_base = base_mat[:,dfinds][xinds,:]\n", "this_stimi = stimi_mat[:,dfinds][xinds,:]\n", "this_stimf = stimf_mat[:,dfinds][xinds,:]\n", "this_post = postpairing_mat[:,dfinds][xinds,:]\n", "this_recovery = recovery_mat[:,dfinds][xinds,:]\n", "\n", "n = [len(c[~np.isnan(c)]) for c in this_base]\n", "\n", "plt.figure(figsize=(5,8))\n", "r = np.nanquantile((this_stimf-this_base),[0.25,0.5,0.75],axis=1)\n", "plt.step(xtime,r[1],color = sns.xkcd_rgb['light green'],lw = 4,where='mid')#np.nanmean(this_stimi-this_base,1));\n", "r = np.nanquantile((this_stimi-this_base),[0.25,0.5,0.75],axis=1)\n", "plt.step(xtime,r[1],color = sns.xkcd_rgb['green'],lw = 4,where='mid')#np.nanmean(this_stimi-this_base,1));\n", "\n", "####shade IQR around poststimulus response#####\n", "####indicate bins that were significant at 0.01 or 0.05#####\n", "w,p = get_stats_r((this_post-this_base))\n", "r = np.nanquantile((this_post-this_base),[0.25,0.5,0.75],axis=1)\n", "plt.step(xtime,r[1],color = sns.xkcd_rgb['purple'],lw=4,where='mid')#np.nanmean(this_stimi-this_base,1));\n", "plt.fill_between(xtime,r[0],r[2],step='mid',color = sns.xkcd_rgb['bright purple'],alpha = 0.2)\n", "\n", "for x_,p_ in zip(xtime,p):\n", " if p_<0.01:\n", " plt.scatter(x_,9.8,color='purple',marker='*',s=100)\n", "for x_,p_ in zip(xtime,p):\n", " if p_<0.05:\n", " plt.scatter(x_,10.2,color='blue',marker='*',s=100)\n", "plt.plot([-1.3,1.3],[0,0],color = 'black')\n", "plt.xlim(-1.3,1.3)\n", "plt.ylim(-2,11)\n", "plt.savefig('Figures/revisions/histogram_StimPeakAligned_' + celltype + '.eps', format='eps', dpi=1000)\n", "\n", "#plot scatter of changes in center bin spike rate for each period\n", "ind = np.where(xtime==0)[0][0]\n", "ax = do_strip_plot(ind)\n", "plt.ylim(-11,5)\n", "ax.set_title('peak response')\n", "plt.savefig('Figures/revisions/histogram_StimPeakBin_scatter_' + celltype + '.eps', format='eps', dpi=1000)\n", "\n", "######color by ventialtion trigger#####\n", "celltype = 'aen_vent'\n", "dfinds = df_meta[(df_meta['condition']==celltype)&\n", " (df_meta['trigger_type']=='paired')&\n", " (df_meta['phase']=='phase1')&\n", " (df_meta['condition']!='aen_swimcmd')&\n", " (df_meta['condition']!='aen_ventcmd')].index\n", "\n", "this_x = xtime_mat[:,dfinds]\n", "xtime = np.nanmean(this_x,1)\n", "\n", "xinds = np.where((xtime>-1.5)&(xtime<1.5))[0]\n", "xtime = xtime[xinds]\n", "binw = np.round(np.mean(np.diff(xtime)),3)\n", "\n", "this_base = base_mat[:,dfinds][xinds,:]\n", "this_stimi = stimi_mat[:,dfinds][xinds,:]\n", "this_stimf = stimf_mat[:,dfinds][xinds,:]\n", "this_post = postpairing_mat[:,dfinds][xinds,:]\n", "this_recovery = recovery_mat[:,dfinds][xinds,:]\n", "\n", "n = [len(c[~np.isnan(c)]) for c in this_base]\n", "\n", "#plot scatter of changes in spike rate for each period\n", "ind = np.where(xtime==0)[0][0]\n", "plotdf = pd.DataFrame({\n", "'stim' : this_stimf[ind,:] - this_stimi[ind,:],\n", "'post' : this_post[ind,:] - this_base[ind,:]\n", "})\n", "fig = plt.figure(figsize=(3,5))\n", "ax = sns.stripplot(data=plotdf,color=sns.xkcd_rgb['bright purple'],s=7,alpha=1,jitter=0.2)\n", "plt.plot([-1,2],[0,0],color='grey')\n", "plt.ylim(-11,5)\n", "ax.set_title('peak response')\n", "plt.savefig('Figures/revisions/histogram_StimPeakBin_scatter_V' + celltype + '.eps', format='eps', dpi=1000)\n", "\n", "\n", "###color by Fin lift trigger###\n", "celltype = 'aen_proprio'\n", "dfinds = df_meta[(df_meta['condition']==celltype)&\n", " (df_meta['trigger_type']=='paired')&\n", " (df_meta['phase']=='phase1')&\n", " (df_meta['condition']!='aen_swimcmd')&\n", " (df_meta['condition']!='aen_ventcmd')].index\n", "\n", "this_x = xtime_mat[:,dfinds]\n", "xtime = np.nanmean(this_x,1)\n", "\n", "xinds = np.where((xtime>-1.5)&(xtime<1.5))[0]\n", "xtime = xtime[xinds]\n", "binw = np.round(np.mean(np.diff(xtime)),3)\n", "\n", "this_base = base_mat[:,dfinds][xinds,:]\n", "this_stimi = stimi_mat[:,dfinds][xinds,:]\n", "this_stimf = stimf_mat[:,dfinds][xinds,:]\n", "this_post = postpairing_mat[:,dfinds][xinds,:]\n", "this_recovery = recovery_mat[:,dfinds][xinds,:]\n", "\n", "n = [len(c[~np.isnan(c)]) for c in this_base]\n", "\n", "#plot scatter of changes in spike rate for each period\n", "ind = np.where(xtime==0)[0][0]\n", "plotdf = pd.DataFrame({\n", "'stim' : this_stimf[ind,:] - this_stimi[ind,:],\n", "'post' : this_post[ind,:] - this_base[ind,:]\n", "})\n", "fig = plt.figure(figsize=(3,5))\n", "ax = sns.stripplot(data=plotdf,color=sns.xkcd_rgb['bright purple'],s=7,alpha=1,jitter=0.2)\n", "plt.plot([-1,2],[0,0],color='grey')\n", "plt.ylim(-11,5)\n", "ax.set_title('peak response')\n", "plt.savefig('Figures/revisions/histogram_StimPeakBin_scatter_FL' + celltype + '.eps', format='eps', dpi=1000)\n", "\n", "\n", "\n", "#########################################\n", "###############STATS#####################\n", "print('peak stimulus response bin')\n", "print('n = ' + str(len(this_base[ind,:])))\n", "print('stimulus-evoked response relative to base')\n", "print('quantiles')\n", "q = np.nanquantile(this_stimi[ind,:] - this_base[ind,:],q = [0.25,0.5,0.75])\n", "print(q)\n", "r = stats.wilcoxon(this_stimi[ind,:], this_base[ind,:])\n", "print(r)\n", "print('')\n", "print('change in stimulus-evoked response')\n", "print('quantiles')\n", "q = np.nanquantile(this_stimf[ind,:] - this_stimi[ind,:],q = [0.25,0.5,0.75])\n", "print(q)\n", "r = stats.wilcoxon(this_stimf[ind,:], this_stimi[ind,:])\n", "print(r)\n", "print('')\n", "print('change in baseline response after pairing')\n", "print('quantiles')\n", "q = np.nanquantile(this_post[ind,:] - this_base[ind,:],q = [0.25,0.5,0.75])\n", "print(q)\n", "r = stats.wilcoxon(this_post[ind,:], this_base[ind,:])\n", "print(r)\n", "print('')" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: Mean of empty slice\n", " \n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in greater\n", " \n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in less\n", " \n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "peak stimulus response bin\n", "n = 9\n", "stimulus-evoked response relative to base\n", "quantiles\n", "[7.28926554 7.74545455 9.88475177]\n", "WilcoxonResult(statistic=0.0, pvalue=0.007685794055213263)\n", "\n", "change in stimulus-evoked response\n", "quantiles\n", "[-2.6 -1.66666667 -0.31914894]\n", "WilcoxonResult(statistic=9.0, pvalue=0.10974463874701328)\n", "\n", "change in baseline response after pairing\n", "quantiles\n", "[-2.35701754 -0.11073446 0.15162602]\n", "WilcoxonResult(statistic=17.0, pvalue=0.5146697234497355)\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n" ] }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "####sort results for AENs########\n", "celltype = 'afferent'\n", "dfinds = df_meta[(df_meta['cell']==celltype)].index\n", "\n", "this_x = xtime_mat[:,dfinds]\n", "xtime = np.nanmean(this_x,1)\n", "\n", "xinds = np.where((xtime>-1.5)&(xtime<1.5))[0]\n", "xtime = xtime[xinds]\n", "binw = np.round(np.mean(np.diff(xtime)),3)\n", "\n", "this_base = base_mat[:,dfinds][xinds,:]\n", "this_stimi = stimi_mat[:,dfinds][xinds,:]\n", "this_stimf = stimf_mat[:,dfinds][xinds,:]\n", "this_post = postpairing_mat[:,dfinds][xinds,:]\n", "this_recovery = recovery_mat[:,dfinds][xinds,:]\n", "\n", "n = [len(c[~np.isnan(c)]) for c in this_base]\n", "\n", "plt.figure(figsize=(5,8))\n", "r = np.nanquantile((this_stimf-this_base),[0.25,0.5,0.75],axis=1)\n", "plt.step(xtime,r[1],color = sns.xkcd_rgb['light green'],lw = 4,where='mid')#np.nanmean(this_stimi-this_base,1));\n", "r = np.nanquantile((this_stimi-this_base),[0.25,0.5,0.75],axis=1)\n", "plt.step(xtime,r[1],color = sns.xkcd_rgb['green'],lw = 4,where='mid')#np.nanmean(this_stimi-this_base,1));\n", "\n", "####shade IQR around poststimulus response#####\n", "####indicate bins that were significant at 0.01#####\n", "w,p = get_stats_r((this_post-this_base))\n", "r = np.nanquantile((this_post-this_base),[0.25,0.5,0.75],axis=1)\n", "plt.step(xtime,r[1],color = sns.xkcd_rgb['purple'],lw=4,where='mid')#np.nanmean(this_stimi-this_base,1));\n", "plt.fill_between(xtime,r[0],r[2],step='mid',color = sns.xkcd_rgb['bright purple'],alpha = 0.2)\n", "\n", "for x_,p_ in zip(xtime,p):\n", " if p_<0.01:\n", " plt.scatter(x_,9.8,color='purple',marker='*',s=100)\n", "plt.plot([-1.3,1.3],[0,0],color = 'black')\n", "plt.xlim(-1.3,1.3)\n", "plt.ylim(-3,10)\n", "plt.savefig('Figures/revisions/histogram_StimPeakAligned_' + celltype + '.eps', format='eps', dpi=1000)\n", "\n", "#plot changes in spike rate for each period\n", "ind = np.where(xtime==0)[0][0]\n", "ax = do_strip_plot(ind)\n", "ax.set_title('peak response')\n", "plt.savefig('Figures/revisions/histogram_StimPeakBin_scatter_' + celltype + '.eps', format='eps', dpi=1000)\n", "\n", "#################################\n", "###########STATS################\n", "print('peak stimulus response bin')\n", "print('n = ' + str(len(this_base[ind,:])))\n", "print('stimulus-evoked response relative to base')\n", "print('quantiles')\n", "q = np.nanquantile(this_stimi[ind,:] - this_base[ind,:],q = [0.25,0.5,0.75])\n", "print(q)\n", "r = stats.wilcoxon(this_stimi[ind,:], this_base[ind,:])\n", "print(r)\n", "print('')\n", "print('change in stimulus-evoked response')\n", "print('quantiles')\n", "q = np.nanquantile(this_stimf[ind,:] - this_stimi[ind,:],q = [0.25,0.5,0.75])\n", "print(q)\n", "r = stats.wilcoxon(this_stimf[ind,:], this_stimi[ind,:])\n", "print(r)\n", "print('')\n", "print('change in baseline response after pairing')\n", "print('quantiles')\n", "q = np.nanquantile(this_post[ind,:] - this_base[ind,:],q = [0.25,0.5,0.75])\n", "print(q)\n", "r = stats.wilcoxon(this_post[ind,:], this_base[ind,:])\n", "print(r)\n", "print('')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Figure 5: Test whether spiking becomes more negatively correlated with the stimulus response after pairing.**\n", "\n", "--> We preserved the pattern of spike times by estimating an instantaneous spike density function (Fig5A&B; refer to Methods for more detail). We then measured the Spearman’s correlation between 1) the prestimulus response and the stimulus response and 2) the poststimulus response and the stimulus response. Within each cell, we compared the correlation between the poststimulus response and the stimulus response to the correlation between the prestimulus response and the stimulus response. In the paired condition, just as predicted, AENs had a poststimulus response that was more negatively correlated with the stimulus response than it was prestimulus (Fig5C; difference in correlation = -0.18 (-0.52 – -0.03); W = 80; p=0.009; n=27 cells). Afferents exhibited no change in the correlation with the stimulus response between the post and prestimulus periods (Fig5C; difference in correlation = 0 (-0.13 – 0.5); W = 18; critical W at alpha(0.05)=8 for 9 cells). For the subset of AENs tested in both the paired and the freerun experimental condition, the poststimulus response was more negatively correlated to the stimulus response in the paired condition than in the freerun condition in all 5/5 cells (Fig5D; W = 0; critical W at alpha(0.05) = 0 for n=5; same cells as those in the analysis corresponding to Fig4B). Under the freerun condition, the AENs were prevented from forming their negative image of the stimulus response. In other words, yoking an external stimulus to a behavioral signal led to a post-pairing response in AENs that was like a negative image of the stimulus response, and this effect was specific to the AEN population. " ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AENs change in spearmans correlation poststimulus versus prestimulus:\n" ] }, { "data": { "text/html": [ "
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sp_poststim_meansp_basestim_meansp_change
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mean-0.1964770.033320-0.229797
std0.3441140.3439830.446700
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" ], "text/plain": [ " sp_poststim_mean sp_basestim_mean sp_change\n", "count 27.000000 27.000000 27.000000\n", "mean -0.196477 0.033320 -0.229797\n", "std 0.344114 0.343983 0.446700\n", "min -0.711848 -0.728269 -1.151269\n", "25% -0.463366 -0.187173 -0.522295\n", "50% -0.214484 0.108765 -0.185130\n", "75% -0.001742 0.245227 -0.033849\n", "max 0.772056 0.495683 0.705711" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " \n", "aen\n", "n = 27\n", "post-pairing: H0 = post-pairing same as base\n", "WilcoxonResult(statistic=80.0, pvalue=0.00882590938459905)\n", " \n", "are the proprio and fin lift groups significantly different from each other?\n", "n1= 21\n", "n2= 6\n", "MannwhitneyuResult(statistic=56.0, pvalue=0.35231137054800765)\n", " \n", " \n", "afferents change in spearmans correlation poststimulus versus prestimulus:\n" ] }, { "data": { "text/html": [ "
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sp_poststim_meansp_basestim_meansp_change
count9.0000009.0000009.000000
mean0.143354-0.1400810.283436
std0.5080550.4371600.688481
min-0.963321-0.877539-0.278421
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" ], "text/plain": [ " sp_poststim_mean sp_basestim_mean sp_change\n", "count 9.000000 9.000000 9.000000\n", "mean 0.143354 -0.140081 0.283436\n", "std 0.508055 0.437160 0.688481\n", "min -0.963321 -0.877539 -0.278421\n", "25% 0.046777 -0.441290 -0.129415\n", "50% 0.212414 0.044780 0.000300\n", "75% 0.287136 0.184206 0.494192\n", "max 0.947601 0.341829 1.825140" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " \n", "n = 9\n", "post-pairing: H0 = post-pairing same as base\n", "WilcoxonResult(statistic=18.0, pvalue=0.5939546753269146)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "######################################\n", "####plot change in spearmans by group cell type: AENs and afferents########\n", "\n", "plotdf = pd.concat([alldf[(alldf.cell=='aen')&(alldf.phase=='phase1')&(alldf.trigger_type=='paired')],\n", " alldf[alldf.cell=='afferent']],sort=True)\n", "\n", "fig = plt.figure(figsize=(5,8))\n", "ax = sns.stripplot(x='cell', y='sp_change', data=plotdf,color='black',s=11,alpha=1,jitter=0.3)\n", "plt.plot([-1,2],[0,0],color='grey')\n", "q = plotdf[plotdf.cell=='aen']['sp_change'].quantile([.25, .5,0.75])\n", "i=0\n", "ax.plot([i-0.2,i+0.2],[q.values[1],q.values[1]],color = 'red',lw = 3,zorder=30)\n", "ax.plot([i,i],[q.values[1],q.values[0]],color = 'red',lw = 3,zorder=30)\n", "ax.plot([i,i],[q.values[1],q.values[2]],color = 'red',lw = 3,zorder=30)\n", "q = plotdf[plotdf.cell=='afferent']['sp_change'].quantile([.25, .5,0.75])\n", "i=1\n", "ax.plot([i-0.2,i+0.2],[q.values[1],q.values[1]],color = 'red',lw = 3,zorder=30)\n", "ax.plot([i,i],[q.values[1],q.values[0]],color = 'red',lw = 3,zorder=30)\n", "ax.plot([i,i],[q.values[1],q.values[2]],color = 'red',lw = 3,zorder=30)\n", "plt.savefig('Figures/revisions/scatter_sp_change_cellGrouped.eps', format='eps', dpi=1000)\n", "\n", "\n", "####### and color AENs by condition ##########\n", "#plot changes in spearmans for each period\n", "plotdf = alldf[((alldf.condition=='aen_vent')&\n", " (alldf.trigger_type=='paired')&\n", " (alldf.phase=='phase1'))][['sp_change']]\n", "\n", "fig = plt.figure(figsize=(5,8))\n", "ax = sns.stripplot(data=plotdf,color='green',s=10,alpha=1,jitter=0.2)\n", "plt.plot([-1,2],[0,0],color='grey')\n", "colhead = plotdf.columns.to_list()\n", "for i,c in enumerate(colhead):\n", " q = plotdf[c].quantile([.25, .5,0.75]) \n", " ax.plot([i-0.2,i+0.2],[q.values[1],q.values[1]],color = 'red',lw = 3,zorder=30)\n", " ax.plot([i,i],[q.values[1],q.values[0]],color = 'red',lw = 3,zorder=30)\n", " ax.plot([i,i],[q.values[1],q.values[2]],color = 'red',lw = 3,zorder=30)\n", "plt.ylim(-1.5,1)\n", "\n", "plt.savefig('Figures/revisions/scatter_aenV_sp_change_.eps', format='eps', dpi=1000)\n", "\n", "#plot changes in spearmans for each period\n", "plotdf = alldf[((alldf.condition=='aen_proprio')&\n", " (alldf.trigger_type=='paired')&\n", " (alldf.phase=='phase1'))][['sp_change']]\n", "\n", "fig = plt.figure(figsize=(5,8))\n", "ax = sns.stripplot(data=plotdf,color='blue',s=10,alpha=1,jitter=0.2)\n", "plt.plot([-1,2],[0,0],color='grey')\n", "colhead = plotdf.columns.to_list()\n", "for i,c in enumerate(colhead):\n", " q = plotdf[c].quantile([.25, .5,0.75]) \n", " ax.plot([i-0.2,i+0.2],[q.values[1],q.values[1]],color = 'red',lw = 3,zorder=30)\n", " ax.plot([i,i],[q.values[1],q.values[0]],color = 'red',lw = 3,zorder=30)\n", " ax.plot([i,i],[q.values[1],q.values[2]],color = 'red',lw = 3,zorder=30)\n", "plt.ylim(-1.5,1)\n", "\n", "plt.savefig('Figures/revisions/scatter_aenFL_sp_change_.eps', format='eps', dpi=1000)\n", "\n", "\n", "############ STATS #########\n", "\n", "#######changes in spearmans correlation between post-stim and pre-stim and stim#########\n", "print('AENs change in spearmans correlation poststimulus versus prestimulus:')\n", "thisdf = alldf[((alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')&\n", " (alldf.phase=='phase1'))]\n", "\n", "display(thisdf[['sp_poststim_mean','sp_basestim_mean','sp_change']].describe())\n", "\n", "print(' ')\n", "print('aen')\n", "print('n = ' + str(len(thisdf)))\n", "print('post-pairing: H0 = post-pairing same as base')\n", "r = scipy.stats.wilcoxon(thisdf['sp_change'].values)\n", "print(r)\n", "\n", "print(' ')\n", "print('are the proprio and fin lift groups significantly different from each other?')\n", "plotdf = alldf[((alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')&\n", " (alldf.phase=='phase1'))][['sp_change','condition']]\n", "r = scipy.stats.mannwhitneyu(plotdf[plotdf.condition=='aen_vent']['sp_change'].values,\n", " plotdf[plotdf.condition=='aen_proprio']['sp_change'].values)\n", "print('n1= ' + str(len(plotdf[plotdf.condition=='aen_vent'])))\n", "print('n2= ' + str(len(plotdf[plotdf.condition=='aen_proprio'])))\n", "print(r)\n", "\n", "print(' ')\n", "print(' ')\n", "print('afferents change in spearmans correlation poststimulus versus prestimulus:')\n", "thisdf = alldf[((alldf.cell=='afferent'))]\n", "\n", "display(thisdf[['sp_poststim_mean','sp_basestim_mean','sp_change']].describe())\n", "\n", "print(' ')\n", "print('n = ' + str(len(thisdf)))\n", "print('post-pairing: H0 = post-pairing same as base')\n", "r = scipy.stats.wilcoxon(thisdf['sp_change'].values)\n", "print(r)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " **\n", " \n", "5 control cells\n", "change in response between post-pairing and baseline\n", "Is change in post-pairing response the same in AENs under control conditions\n", "for sample sizes = 5, critical W=0 at alpha(0.05)\n", " \n", " wilcoxon compared before and after pairing\n", " \n", "aen H0 = post-stim to base-stim correlations the same in each cell?\n", "WilcoxonResult(statistic=0.0, pvalue=0.043114446783075355)\n", " \n", " \n", "what percent of data points lie below the unity line for aen test versus control\n", "100\n", " \n", " \n", "change in response between post-pairing and baseline compared to a median\n", "Is change in post-pairing response the same in AENs under control or test conditions\n", " \n", " wilcoxon compared to median\n", " \n", "aen freerun control H0 = post-stim to base-stim correlations the same as distribution with median 0\n", "WilcoxonResult(statistic=7.0, pvalue=0.892738400944348)\n", " \n", " \n", "aen paired test H0 = post-stim to base-stim correlations the same as distribution with median 0\n", "WilcoxonResult(statistic=0.0, pvalue=0.043114446783075355)\n", " \n", " \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2778: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#############################\n", "#########plot control for AENS ###########\n", "\n", "plotdf = alldf[alldf.control=='yes']\n", "fig = plt.figure(figsize=(3,5))\n", "ax = sns.stripplot(x='trigger_type', y='sp_change', data=plotdf,color='black',s=15,alpha=1,jitter=False)\n", "# [ax.plot([0,1,2,3,4],sweep,color='grey') for sweep in plotdf[['base','stimi','stimf','post','recovery']].values];\n", "plt.plot([-1,2],[0,0],color='grey')\n", "[plt.plot([0,1],[x,y],color = 'gray') for x,y in zip(plotdf[plotdf.trigger_type=='freerun']['sp_change'].values,\n", " plotdf[plotdf.trigger_type=='paired']['sp_change'].values)]\n", "plt.savefig('Figures/revisions/scatter_sp_change_control.eps', format='eps', dpi=1000)\n", "\n", "plotdf = alldf[(alldf.control=='yes')&(alldf.condition=='aen_vent')]\n", "fig = plt.figure(figsize=(3,5))\n", "ax = sns.stripplot(x='trigger_type', y='sp_change', data=plotdf,color='green',s=15,alpha=1,jitter=False)\n", "# [ax.plot([0,1,2,3,4],sweep,color='grey') for sweep in plotdf[['base','stimi','stimf','post','recovery']].values];\n", "plt.plot([-1,2],[0,0],color='grey')\n", "[plt.plot([0,1],[x,y],color = 'gray') for x,y in zip(plotdf[plotdf.trigger_type=='freerun']['sp_change'].values,\n", " plotdf[plotdf.trigger_type=='paired']['sp_change'].values)]\n", "plt.ylim(-1,1)\n", "plt.savefig('Figures/revisions/scatter_sp_change_control_V.eps', format='eps', dpi=1000)\n", "\n", "plotdf = alldf[(alldf.control=='yes')&(alldf.condition=='aen_proprio')]\n", "fig = plt.figure(figsize=(3,5))\n", "ax = sns.stripplot(x='trigger_type', y='sp_change', data=plotdf,color='blue',s=15,alpha=1,jitter=False)\n", "# [ax.plot([0,1,2,3,4],sweep,color='grey') for sweep in plotdf[['base','stimi','stimf','post','recovery']].values];\n", "plt.plot([-1,2],[0,0],color='grey')\n", "[plt.plot([0,1],[x,y],color = 'gray') for x,y in zip(plotdf[plotdf.trigger_type=='paired']['sp_change'].values,\n", " plotdf[plotdf.trigger_type=='freerun']['sp_change'].values)]\n", "plt.ylim(-1,1)\n", "plt.savefig('Figures/revisions/scatter_sp_change_control_FL.eps', format='eps', dpi=1000)\n", "\n", "\n", "######### STATS #########\n", "print(' **')\n", "print(' ')\n", "print('5 control cells')\n", "print('change in response between post-pairing and baseline')\n", "print('Is change in post-pairing response the same in AENs under control conditions')\n", "print('for sample sizes = 5, critical W=0 at alpha(0.05)')\n", "print(' ')\n", "print(' wilcoxon compared before and after pairing')\n", "print(' ')\n", "print('aen H0 = post-stim to base-stim correlations the same in each cell?')\n", "r = scipy.stats.wilcoxon(alldf[(alldf.trigger_type=='freerun')&(alldf.control=='yes')]['sp_change'].values,\n", " alldf[(alldf.trigger_type=='paired')&(alldf.control=='yes')]['sp_change'].values)\n", "print(r)\n", "\n", "print(' ')\n", "print(' ')\n", "print('what percent of data points lie below the unity line for aen test versus control')\n", "data = alldf[(alldf.trigger_type=='paired')&(alldf.control=='yes')]['sp_change'].values-alldf[(alldf.trigger_type=='freerun')&(alldf.control=='yes')]['sp_change'].values\n", "print(str(int(100*len(np.where(data<0)[0])/len(data))))\n", "print(' ')\n", "print(' ')\n", "print('change in response between post-pairing and baseline compared to a median')\n", "print('Is change in post-pairing response the same in AENs under control or test conditions')\n", "print(' ')\n", "print(' wilcoxon compared to median')\n", "print(' ')\n", "print('aen freerun control H0 = post-stim to base-stim correlations the same as distribution with median 0')\n", "r = scipy.stats.wilcoxon(alldf[(alldf.trigger_type=='freerun')&(alldf.control=='yes')]['sp_change'].values)\n", "print(r)\n", "print(' ')\n", "print(' ')\n", "print('aen paired test H0 = post-stim to base-stim correlations the same as distribution with median 0')\n", "r = scipy.stats.wilcoxon(alldf[(alldf.trigger_type=='paired')&(alldf.control=='yes')]['sp_change'].values)\n", "print(r)\n", "print(' ')\n", "print(' ')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Other analyses not included in main text of manuscript:**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "results did not seem to depend on number of pairing trials:" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SpearmanrResult(correlation=-0.16760800073157545, pvalue=0.40335673680094797)\n", "SpearmanrResult(correlation=0.12730880929884691, pvalue=0.5268662165056265)\n" ] }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "aendf = alldf[((alldf.cell=='aen')&\n", " (alldf.trigger_type=='paired')&\n", " (alldf.phase=='phase1'))]\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.scatter(aendf['spkrt_post']-aendf['spkrt_base'],\n", " aendf['ntrials_pairing'])\n", "\n", "r = stats.spearmanr(aendf['spkrt_post']-aendf['spkrt_base'],\n", " aendf['ntrials_pairing'])\n", "print(r)\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.scatter(aendf['sp_change'],\n", " aendf['ntrials_pairing'])\n", "\n", "r = stats.spearmanr(aendf['sp_change'],aendf['ntrials_pairing'])\n", "print(r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "effect of condition on negative image in AENs?
\n", "note that motor command conditions sample size too small to assess this result appropriately" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KruskalResult(statistic=nan, pvalue=nan)\n" ] } ], "source": [ "r = stats.kruskal(alldf[(alldf.condition=='aen_vent')&(alldf.cell=='aen')]['sp_change'].values,\n", " alldf[(alldf.condition=='aen_ventcmd')&(alldf.cell=='aen')]['sp_change'].values,\n", " alldf[(alldf.condition=='aen_proprio')&(alldf.cell=='aen')]['sp_change'].values,\n", " alldf[(alldf.condition=='aen_swimcmd')&(alldf.cell=='aen')]['sp_change'].values)\n", "print(r)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# functions for plotting example cell data\n", "def anal_raw(iexpt,thisexpt_name):\n", " dt = 1/1000 \n", " ntrials_per_period = 75 \n", "\n", " print(iexpt,thisexpt_name)\n", "\n", " thisexpt = meta.loc[meta['exptname'] == thisexpt_name]\n", "\n", " trial_duration = thisexpt.trial_duration.values[0]\n", " \n", " basename = thisexpt_name[0:-8]\n", " data_folder = Path.cwd() / basename\n", " file_to_open = data_folder / Path(basename + '.smr')\n", " bl = Spike2IO(file_to_open,try_signal_grouping=False).read_block()\n", "\n", " trigger = []\n", " for sublist in np.asarray([seg.events[[s.name for \n", " s in seg.events].index(thisexpt.chan_trigger.values[0])].magnitude \n", " for seg in bl.segments]):\n", " for item in sublist:\n", " trigger.append(item)\n", " trigger = np.asarray(trigger)\n", "\n", " if thisexpt.spike_times_from.values[0] == 'Spyking-Circus':\n", " spikes = np.load(data_folder / 'spikes.npy')\n", "\n", " if thisexpt.spike_times_from.values[0] == 'Spike2':\n", " spikes = []\n", " for sublist in np.asarray([seg.events[[s.name for \n", " s in seg.events].index(thisexpt.chan_spikes.values[0])].magnitude \n", " for seg in bl.segments]):\n", " for item in sublist:\n", " spikes.append(item)\n", " spikes = np.asarray(spikes)\n", "\n", " postpairing_range = get_range_bounds([thisexpt.post_start.values[0],\n", " thisexpt.post_stop.values[0]],trigger)\n", " stim_range = get_range_bounds([thisexpt.stim_start.values[0],\n", " thisexpt.stim_stop.values[0]],trigger)\n", " base_range = get_range_bounds([thisexpt.base_start.values[0],\n", " thisexpt.base_stop.values[0]],trigger)\n", "\n", " if thisexpt_name == '20060812_3066_SPK.mat':\n", " #removing a few trials in which spike detection was clearly mis-triggered due to movement artifact\n", " removeinds = np.arange(360,np.max(postpairing_range)-1,1)\n", " [postpairing_range.remove(x) for x in removeinds]\n", " removeinds = [263, 264, 265, 326, 327] \n", " [postpairing_range.remove(x) for x in removeinds]\n", "\n", " xtime = np.linspace(0,trial_duration,trial_duration/dt)\n", "\n", " iei = [(TrialEventTimes(trigger,trigger,trial,10))[0] if len(TrialEventTimes(trigger,trigger,trial,10))>0 else 0 for trial in list(range(0,len(trigger)-1))]\n", "\n", " these_trials = base_range\n", " y, t, optw, C, C_min= get_optw(these_trials,spikes,xtime,trigger,trial_duration)\n", "\n", " data_func = filtered_response(spikes, optw)\n", "\n", " baseline_r = get_response(trigger[these_trials],data_func,trial_duration,optw)\n", " baseline_mean = np.mean(baseline_r,0)\n", "\n", " n_trials = len(base_range)\n", " base_spkrt = [np.shape(np.where((spikes>(trigger[t]))&(spikes<(trigger[t]+trial_duration)))[0])[0]\n", " /trial_duration for t in base_range]\n", " \n", " spikemat_base = [spikes[np.where((spikes>trigger[t])&\n", " (spikes(supptrigger[t]))&\n", " (suppspikes<(supptrigger[t]+trial_duration)))[0])[0]\n", " /trial_duration for t in suppbase_range]\n", " spikemat_base = [suppspikes[np.where((suppspikes>supptrigger[t])&\n", " (suppspikes(supptrigger[t]))&\n", " (suppspikes<(supptrigger[t]+trial_duration)))[0])[0]\n", " /trial_duration for t in suppbase_range]\n", " spikemat_base = [suppspikes[np.where((suppspikes>supptrigger[t])&\n", " (suppspikes(trigger[t]))&(spikes<(trigger[t]+trial_duration)))[0])[0]\n", " /trial_duration for t in these_trials]\n", " y, t, optw_stim, C, C_min= get_optw(these_trials,spikes,xtime,trigger,trial_duration)\n", " stiminitial_r = get_response(trigger[these_trials],data_func,trial_duration,optw)\n", " stiminitial_response = np.mean(stiminitial_r,0)\n", "\n", " stimfinal_trials = stim_range[-int(ntrials/2):]\n", " these_trials = stimfinal_trials\n", " stimf_spkrt = [np.shape(np.where((spikes>(trigger[t]))&(spikes<(trigger[t]+trial_duration)))[0])[0]\n", " /trial_duration for t in these_trials]\n", " stimfinal_r = get_response(trigger[these_trials],data_func,trial_duration,optw)\n", " stimfinal_response = np.mean(stimfinal_r,0)\n", "\n", " npost = len(postpairing_range)\n", " ntrials = np.min([npost,ntrials_per_period])\n", " postpairing_trials = postpairing_range[0:ntrials]\n", " these_trials = postpairing_trials\n", " post_spkrt = [np.shape(np.where((spikes>(trigger[t]))&(spikes<(trigger[t]+trial_duration)))[0])[0]\n", " /trial_duration for t in these_trials]\n", " postpairing_r = get_response(trigger[these_trials],data_func,trial_duration,optw)\n", " postpairing_response = np.mean(postpairing_r,0)\n", "\n", " recovery_spkrt = np.nan\n", " recovery_response = np.empty(len(baseline_mean))\n", " if npost>=250:\n", " recovery_trials = postpairing_range[200:250]\n", " recovery_spkrt = [np.shape(np.where((spikes>(trigger[t]))&(spikes<(trigger[t]+trial_duration)))[0])[0]\n", " /trial_duration for t in recovery_trials]\n", " these_trials = recovery_trials\n", " recovery_r = get_response(trigger[these_trials],data_func,trial_duration,optw)\n", " recovery_response = np.mean(recovery_r,0)\n", " \n", "\n", " spikemat_stim = [spikes[np.where((spikes>trigger[t])&\n", " (spikestrigger[t])&\n", " (spikes cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:43: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", " \"\"\"\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "iexpt = 49\n", "scalefig = 75\n", "dt = 1/1000\n", "spkbase, spkstim, spkpost,base, stimi, stimf,post,recover,trial_duration = anal_raw(iexpt,meta.exptname[iexpt])\n", "xtime = np.linspace(0,trial_duration,trial_duration/dt)\n", "ylims = [-5,35]\n", "doplots(meta.exptname[iexpt],scalefig,ylims,spkbase, spkstim, \n", " spkpost,base, stimi, stimf,post,recover,trial_duration)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9 20050824_2627_control_SPK.mat\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:56: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:43: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", " \"\"\"\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "iexpt = 9\n", "scalefig = 150\n", "dt = 1/1000\n", "spkbase, spkstim, spkpost,base, stimi, stimf,post,recover,trial_duration = anal_raw(iexpt,meta.exptname[iexpt])\n", "xtime = np.linspace(0,trial_duration,trial_duration/dt)\n", "ylims = [-5,15]\n", "doplots(meta.exptname[iexpt],scalefig,ylims,spkbase, spkstim, \n", " spkpost,base, stimi, stimf,post,recover,trial_duration)\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 20050824_2627_trial1_SPK.mat\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:56: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:43: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", " \"\"\"\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "iexpt = 10\n", "scalefig = 150\n", "dt = 1/1000\n", "spkbase, spkstim, spkpost,base, stimi, stimf,post,recover,trial_duration = anal_raw(iexpt,meta.exptname[iexpt])\n", "xtime = np.linspace(0,trial_duration,trial_duration/dt)\n", "ylims = [-5,20]\n", "doplots(meta.exptname[iexpt],scalefig,ylims,spkbase, spkstim, \n", " spkpost,base, stimi, stimf,post,recover,trial_duration)\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "21 20070718_598_coupled_SPK.mat\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:56: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:43: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", " \"\"\"\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "iexpt = 21\n", "scalefig = 100\n", "dt = 1/1000\n", "spkbase, spkstim, spkpost,base, stimi, stimf,post,recover,trial_duration = anal_raw(iexpt,meta.exptname[iexpt])\n", "xtime = np.linspace(0,trial_duration,trial_duration/dt)\n", "ylims = [-5,15]\n", "doplots(meta.exptname[iexpt],scalefig,ylims,spkbase, spkstim, \n", " spkpost,base, stimi, stimf,post,recover,trial_duration)\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4 20050808_3629_latency2_SPK.mat\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:56: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:43: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", " \"\"\"\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "iexpt = 4\n", "scalefig = 350\n", "dt = 1/1000\n", "spkbase, spkstim, spkpost,base, stimi, stimf,post,recover,trial_duration = anal_raw(iexpt,meta.exptname[iexpt])\n", "xtime = np.linspace(0,trial_duration,trial_duration/dt)\n", "ylims = [-5,25]\n", "doplots(meta.exptname[iexpt],scalefig,ylims,spkbase, spkstim, \n", " spkpost,base, stimi, stimf,post,recover,trial_duration)\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 20050808_3629_latency1_SPK.mat\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:56: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:43: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", " \"\"\"\n" ] }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "iexpt = 3\n", "scalefig = 350\n", "dt = 1/1000\n", "spkbase, spkstim, spkpost,base, stimi, stimf,post,recover,trial_duration = anal_raw(iexpt,meta.exptname[iexpt])\n", "xtime = np.linspace(0,trial_duration,trial_duration/dt)\n", "ylims = [-5,30]\n", "doplots(meta.exptname[iexpt],scalefig,ylims,spkbase, spkstim, \n", " spkpost,base, stimi, stimf,post,recover,trial_duration)\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "48 20050808_3629_latency1b_SPK.mat\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:56: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:43: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", " \"\"\"\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "iexpt = 48\n", "scalefig = 350\n", "dt = 1/1000\n", "spkbase, spkstim, spkpost,base, stimi, stimf,post,recover,trial_duration = anal_raw(iexpt,meta.exptname[iexpt])\n", "xtime = np.linspace(0,trial_duration,trial_duration/dt)\n", "ylims = [-5,15]\n", "doplots(meta.exptname[iexpt],scalefig,ylims,spkbase, spkstim, \n", " spkpost,base, stimi, stimf,post,recover,trial_duration)\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "47 20050808_3629_latency2b_SPK.mat\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:56: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:43: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", " \"\"\"\n" ] }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "iexpt = 47\n", "scalefig = 350\n", "dt = 1/1000\n", "spkbase, spkstim, spkpost,base, stimi, stimf,post,recover,trial_duration = anal_raw(iexpt,meta.exptname[iexpt])\n", "xtime = np.linspace(0,trial_duration,trial_duration/dt)\n", "ylims = [-5,15]\n", "doplots(meta.exptname[iexpt],scalefig,ylims,spkbase, spkstim, \n", " spkpost,base, stimi, stimf,post,recover,trial_duration)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }