{ "cells": [ { "cell_type": "markdown", "id": "assisted-machinery", "metadata": {}, "source": [ "# Course Module for ANDA-NI 2024" ] }, { "cell_type": "markdown", "id": "digital-assessment", "metadata": {}, "source": [ "All data was kindly provided by Alexa Riehle, Institut de Neurosciences de la Timone (INT),\n", "Centre National de la Recherche Scientifique (CNRS) - Aix-Marseille Université (AMU),\n", "UMR7289, 13005 Marseille, France. Use of this data outside of this teaching course is strictly\n", "prohibited. Data is published under https://doi.org/10.12751/g-node.rz77m8." ] }, { "cell_type": "markdown", "id": "shared-edgar", "metadata": {}, "source": [ "# **Trial-by-trial variability and firing irregularity in single unit spike trains from monkey motor cortex.**" ] }, { "cell_type": "markdown", "id": "finished-workshop", "metadata": {}, "source": [ "Lecturer: *Martin Nawrot* \\\n", "Tutor: *Ibrahim Tunc*" ] }, { "cell_type": "markdown", "id": "changed-wireless", "metadata": {}, "source": [ "This course module introduces the student to second order statistical analyses of\n", "single neuron spike trains. The exercises cover the time-resolved measurement of\n", "trial-by-trial variability using the Fano factor (FF) and the estimation of interval\n", "variability using the local coefficient of variation (CV2). The results on interval and\n", "count variability are interpreted in the context of predictions from point process\n", "theory. This teaching module addresses (post-)graduate students with a solid background\n", "in programming. The module is designed for one course day including an\n", "introductory lecture, practical course work, and final presentation of results." ] }, { "cell_type": "markdown", "id": "structural-medicine", "metadata": {}, "source": [ "### Data sets" ] }, { "cell_type": "markdown", "id": "duplicate-superior", "metadata": {}, "source": [ "In the present exercises, you will analyze single unit recordings from the primary motor\n", "cortex (M1) of one monkey (monkey 1 in Rickert et al., 2009 and monkey M1 Rostami et al. 2024), which performed a delayed\n", "center-out task. The experiments were carried out in the lab of Alexa Riehle. The task and\n", "the data are described in detail in Rickert et al., (2009). For an introduction to directional\n", "tuning in the motor cortex an in-depth coverage is found in Riehle & Vaadia (2005).\n", "The data format is as follows. For each single neuron and each recording session there\n", "exists a single data file, e.g. `joe105-6-C1-TS.mat`. The filename encodes the monkey's name\n", "(joe), the experimental session number (105), and the number of the single unit (6) in this file\n", "as well as the experimental condition (C1=one target information, C2=two target information,\n", "C3 = three target information) and the temporal trigger (TS=trial start, MO=movement onset)\n", "that was used to temporally align all single trial spike trains. The spike data is stored with a\n", "time resolution of 1ms. For loading this data file in Python you need to use `scipy.io` module,\n", "which enables you to load Matlab files in Python, as follows:" ] }, { "cell_type": "code", "execution_count": 1, "id": "mature-metallic", "metadata": {}, "outputs": [], "source": [ "import scipy.io as spio\n", "import scipy.signal\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "#from itertools import tee\n", "#import glob\n", "#from elephant import statistics\n", "#import neo" ] }, { "cell_type": "code", "execution_count": 2, "id": "subjective-gauge", "metadata": {}, "outputs": [], "source": [ "#choose a file\n", "path = \"./Data/nawrot_data/\" #path to ANDA 2021 Collab\n", "file = path + 'SelectedDataC1/joe096-5-C1-MO.mat'\n", "file2 = path + 'SelectedDataC1/joe096-5-C1-TS.mat'\n", "mat_contents = spio.loadmat(file, struct_as_record=False, squeeze_me=True)\n", "mat_contents2 = spio.loadmat(file2, struct_as_record=False, squeeze_me=True)" ] }, { "cell_type": "markdown", "id": "shaped-portrait", "metadata": {}, "source": [ "`mat_contents` is a dictionary which contains the struct SparseFormat from Matlab. To\n", "access different data and metadata within this dictionay you simply call the values of\n", "`'SparseFormat'` key." ] }, { "cell_type": "code", "execution_count": 3, "id": "applicable-cargo", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'_fieldnames': ['GeneratingMatlabFile',\n", " 'TimeResolutionMS',\n", " 'CutTriggers',\n", " 'CutTriggerNames',\n", " 'CutIntervalMS',\n", " 'ExternelEventNames',\n", " 'InputFileName',\n", " 'SessionName',\n", " 'ExperimentalCondition',\n", " 'ExperimentalConditionName',\n", " 'ExternalEventTimes',\n", " 'Data'],\n", " 'GeneratingMatlabFile': 'gdf2sparse.m',\n", " 'TimeResolutionMS': 1,\n", " 'CutTriggers': array([131, 132, 133, 134, 135, 136], dtype=uint8),\n", " 'CutTriggerNames': 'MO',\n", " 'CutIntervalMS': array([-1000, 1000], dtype=int16),\n", " 'ExternelEventNames': 'ME',\n", " 'InputFileName': 'joe096-23457.gdf',\n", " 'SessionName': 'joe096',\n", " 'ExperimentalCondition': 1,\n", " 'ExperimentalConditionName': 'full',\n", " 'ExternalEventTimes': array([array([[ 1, 1163],\n", " [ 2, 1151],\n", " [ 3, 1835],\n", " [ 4, 1203],\n", " [ 5, 1153],\n", " [ 6, 1173],\n", " [ 7, 1373],\n", " [ 8, 1146],\n", " [ 9, 1202],\n", " [ 10, 1341],\n", " [ 11, 1144],\n", " [ 12, 1288],\n", " [ 13, 1378],\n", " [ 14, 1147],\n", " [ 15, 1238],\n", " [ 16, 1193],\n", " [ 17, 1153],\n", " [ 18, 1170],\n", " [ 19, 1196]], dtype=uint16),\n", " array([[ 1, 1198],\n", " [ 2, 1204],\n", " [ 3, 1234],\n", " [ 4, 1155],\n", " [ 5, 1201],\n", " [ 6, 1174],\n", " [ 7, 1182],\n", " [ 8, 1175],\n", " [ 9, 1179],\n", " [ 10, 1182],\n", " [ 11, 1152],\n", " [ 12, 1191],\n", " [ 13, 1151],\n", " [ 14, 1180],\n", " [ 15, 1196],\n", " [ 16, 1239],\n", " [ 17, 1213],\n", " [ 18, 1271]], dtype=uint16),\n", " array([[ 1, 1171],\n", " [ 2, 1151],\n", " [ 3, 1170],\n", " [ 4, 1188],\n", " [ 5, 1186],\n", " [ 6, 1155],\n", " [ 7, 1141],\n", " [ 8, 1222],\n", " [ 9, 1265],\n", " [ 10, 1210],\n", " [ 11, 1178],\n", " [ 12, 1160],\n", " [ 13, 1126],\n", " [ 14, 1167],\n", " [ 15, 1168],\n", " [ 16, 1135],\n", " [ 17, 1198],\n", " [ 18, 1189],\n", " [ 19, 1190],\n", " [ 20, 1176]], dtype=uint16),\n", " array([[ 1, 1362],\n", " [ 2, 1189],\n", " [ 3, 1238],\n", " [ 4, 1222],\n", " [ 5, 1192],\n", " [ 6, 1186],\n", " [ 7, 1181],\n", " [ 8, 1212],\n", " [ 9, 1215],\n", " [ 10, 1276],\n", " [ 11, 1179],\n", " [ 12, 1224],\n", " [ 13, 1162],\n", " [ 14, 1272],\n", " [ 15, 1226],\n", " [ 16, 1379],\n", " [ 17, 1339],\n", " [ 18, 1353],\n", " [ 19, 1170],\n", " [ 20, 1204]], dtype=uint16),\n", " array([[ 1, 1311],\n", " [ 2, 1286],\n", " [ 3, 1229],\n", " [ 4, 1328],\n", " [ 5, 1169],\n", " [ 6, 1214],\n", " [ 7, 1246],\n", " [ 8, 1223],\n", " [ 9, 1269],\n", " [ 10, 1244],\n", " [ 11, 1328],\n", " [ 12, 1188],\n", " [ 13, 1227],\n", " [ 14, 1217],\n", " [ 15, 1301],\n", " [ 16, 1279],\n", " [ 17, 1237],\n", " [ 18, 1246],\n", " [ 19, 1282]], dtype=uint16),\n", " array([[ 1, 1239],\n", " [ 2, 1257],\n", " [ 3, 1175],\n", " [ 4, 1197],\n", " [ 5, 1317],\n", " [ 6, 1250],\n", " [ 7, 1206],\n", " [ 8, 1289],\n", " [ 9, 1293],\n", " [ 10, 1286],\n", " [ 11, 1264],\n", " [ 12, 1250],\n", " [ 13, 1253],\n", " [ 14, 1209],\n", " [ 15, 1187],\n", " [ 16, 1255]], dtype=uint16)], dtype=object),\n", " 'Data': array([ ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ],\n", " dtype=object)}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#show all\n", "vars(mat_contents['SparseFormat'])" ] }, { "cell_type": "markdown", "id": "thirty-flower", "metadata": {}, "source": [ "resolution of data matrix relative to ms:" ] }, { "cell_type": "code", "execution_count": 4, "id": "aboriginal-register", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat_contents['SparseFormat'].TimeResolutionMS" ] }, { "cell_type": "markdown", "id": "honey-donor", "metadata": {}, "source": [ "spike data for all trials and all 6 directions:" ] }, { "cell_type": "markdown", "id": "offensive-wisconsin", "metadata": {}, "source": [ "time interval of data recording relative to cut event trigger (TS/MO):" ] }, { "cell_type": "code", "execution_count": 5, "id": "tribal-providence", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-1000, 1000], dtype=int16)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat_contents['SparseFormat'].CutIntervalMS #aligend to MO" ] }, { "cell_type": "code", "execution_count": 6, "id": "recorded-sessions", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 2000], dtype=uint16)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat_contents2['SparseFormat'].CutIntervalMS #aligend to TS" ] }, { "cell_type": "code", "execution_count": 7, "id": "competitive-lesbian", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ],\n", " dtype=object)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat_contents['SparseFormat'].Data" ] }, { "cell_type": "markdown", "id": "minute-darwin", "metadata": {}, "source": [ "The `.Data` field is an array of length 6. It contains the binary spike matrices for the 6\n", "directions, i.e. one trial ensemble for each direction.\n", "`mat_contents['SparseFormat'].Data[0]` refers to direction 1 etc. Each direction\n", "contains a sparse matrix where the first dimension is time and the second trial. The number\n", "of trials varies for the different directions. To obtain the full matrix representation from the\n", "sparse representation use `toarray()` function, e.g for direction `i`" ] }, { "cell_type": "code", "execution_count": 8, "id": "juvenile-entity", "metadata": {}, "outputs": [], "source": [ "i = 0\n", "spike_data = mat_contents['SparseFormat'].Data[i].toarray()" ] }, { "cell_type": "code", "execution_count": 15, "id": "16ca0ebd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 1],\n", " [0, 0, 1, ..., 1, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spike_data" ] }, { "cell_type": "markdown", "id": "plastic-thesis", "metadata": {}, "source": [ "# **Exercises**" ] }, { "cell_type": "markdown", "id": "cleared-still", "metadata": {}, "source": [ "The present teaching module is designed for 2-3 hours of practical work and divides\n", "into three parts. The goal of this module is to finish part by part properly, no matter\n", "whether all parts will be completed. Students will present and discuss their results on\n", "individual parts at the end of class." ] }, { "cell_type": "markdown", "id": "liked-official", "metadata": {}, "source": [ "### **Part I – Spike train raster plot**" ] }, { "cell_type": "markdown", "id": "residential-demonstration", "metadata": {}, "source": [ "1. The results shall be presented in Figure 1 with 2 x 3 panels. You may use the `subplots` from `matplotlib.pyplot` module to generate several axes within one figure.\n", "2. Choose arbitrarily one single data file and load it into memory. What is the time resolution of the binary spike representation?\n", "3. *Spike raster plot*. Generate one spike raster plot (dot-display) for each movement direction using the proper time axis in Figure 1. To obtain spike times from the matrix corresponding to one movement direction you may use the ‘where’ function from numpy package" ] }, { "cell_type": "code", "execution_count": 10, "id": "great-presence", "metadata": {}, "outputs": [], "source": [ "TimeResolutionMS = mat_contents['SparseFormat'].TimeResolutionMS\n", "\n", "def spikingTime(spikedatI, TimeResolution):\n", " TupleSpike = np.where(spikedatI)\n", " if len(TupleSpike)==2:\n", " spike_time, trial_num=TupleSpike\n", " spike_time=spike_time*TimeResolution\n", " return (spike_time, trial_num)\n", " else:\n", " spike_time = TupleSpike[0]\n", " spike_time=spike_time*TimeResolution\n", " return spike_time\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "resistant-thinking", "metadata": {}, "outputs": [], "source": [ "spike_time, trial_num = spikingTime(spike_data, TimeResolutionMS)" ] }, { "cell_type": "markdown", "id": "designing-cemetery", "metadata": {}, "source": [ "`spike_time` holds all spike times (1st matrix dimension) and `trial_num` the corresponding trial number (2nd dim.)." ] }, { "cell_type": "markdown", "id": "arabic-content", "metadata": {}, "source": [ "Using `matplotlib.pyplot` and `plt.plot(spike_time, trial_num)` readily produces the desired dot-display. Remember that for data that was cut relative\n", "to the temporal trigger MO, the index 1 in the first dimension (time) refers to the time point t=-1000ms ***before*** movement onset. How can you easily make sure that the time\n", "points are plotted relative to MO at t=0 independent of your axis limits? You may also\n", "explore the raster displays when data is aligned relative to TS." ] }, { "cell_type": "markdown", "id": "alert-thread", "metadata": {}, "source": [ "#### Data aligend to MO" ] }, { "cell_type": "code", "execution_count": 17, "id": "advisory-mercury", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Time [ms]')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(spike_time-1000, trial_num, '|' , color='k') #t=0 MO\n", "plt.ylabel('Trial')\n", "plt.xlabel('Time [ms]')" ] }, { "cell_type": "markdown", "id": "integral-clark", "metadata": {}, "source": [ "#### Data aligend to TS" ] }, { "cell_type": "code", "execution_count": 18, "id": "compatible-oxford", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Time [ms]')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spike_data2 = mat_contents2['SparseFormat'].Data[i].toarray()\n", "spike_time2, trial_num2 = spikingTime(spike_data2, TimeResolutionMS)\n", "\n", "plt.plot(spike_time2, trial_num2, '|' , color='k') #t=0 TS\n", "plt.ylabel('Trial')\n", "plt.xlabel('Time [ms]')" ] }, { "cell_type": "code", "execution_count": null, "id": "f78e27cf", "metadata": {}, "outputs": [], "source": [ "#skipped this section" ] }, { "cell_type": "markdown", "id": "through-cylinder", "metadata": {}, "source": [ "### **Part II – Time resolved Fano factor estimation**" ] }, { "cell_type": "markdown", "id": "alien-yemen", "metadata": {}, "source": [ "Neural activity is inherently variable across identical experimental repetitions and this\n", "variability is typically high with respect to the trial-averaged mean response (e.g. Arieli et al.,\n", "1996). In spike trains that were measured across repeated experimental trials we can\n", "measure the trial-by-trial variability of the single neuron output (e.g. Shadlen and Newsome,\n", "1998). The most established measure is the Fano factor defined as the variance of the spike\n", "count across trials divided by the mean spike count (for a practical introduction and\n", "interpretation of results see Nawrot 2010). This measure has interpretational advantages in\n", "the context of stochastic point process theory. Trial-by-trial spike count is particularly high in\n", "the neocortex and there it is typically highest in the motor areas. However, trial-by-trial\n", "variability is modulated in a task-related manner which was first shown in motor cortex\n", "(Churchland M et al., 2006; Nawrot et al., 2008; Rickert et al., 2009) but is a signature in all\n", "cortical areas (Churchland M et al., 2010; Churchland A et al., 2011). For the present data\n", "set this has been demonstrated in Nawrot et al. (2003) and in the supplements of Rickert et\n", "al. (2009).\n", "\n", "In this part of the exercises we will use the kernel method to estimate the time-resolved Fano\n", "factor averaged over a population of neurons." ] }, { "cell_type": "markdown", "id": "early-award", "metadata": {}, "source": [ "Tasks\n", "1. The results shall be presented in *Figure 2* with 2 x 1 panels (2 row panels). You may use the `subplots` from `matplotlib.pyplot` module to generate several axes within one figure.\n", "2. Choose one single data file with spike trains aligned to either TS or MO and load it into memory. Select one out of the six movement directions. What is the time resolution of the binary spike representation?\n", "3. *Computation of the Fano factor*. For a single unit data file we need to compute the mean spike count across trials and the variance of spike count across trials. To this end count the number of spikes in a moving window. This is conveniently accomplished by applying the kernel convolution method with a rectangular-shaped kernel *k* (also called matrix) applied to all single trials. If the entries in the boxcar kernel are equal to 1, then this results in counting the number of spikes per window (hint: you can use `np.ones` for constructing *k*). The convolution with a boxcar kernel is illustrated below. The spikecount equals the spikes within the window of the kernel.
\n", " You may convolve the full spike matrix (referred to as `spike_data`) at once with the kernel array *k* by using the `lfilter` function from `scipy.signal` module: `R = lfilter(k, 1, spike_data, axis=0)` or use `np.convolve` in a loop or use `convolve2d` function from the `scipy.signal` module which allows convolution of the matrix with a row vector (with 2 dimensions).\n", " " ] }, { "cell_type": "markdown", "id": "aging-thesis", "metadata": {}, "source": [ "![SegmentLocal](Convolve.gif \"segment\")" ] }, { "cell_type": "code", "execution_count": 51, "id": "db1086cc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time resolution: 1\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#select TS and 1 movement dir\n", "file1 = path + 'SelectedDataC1/joe096-5-C1-TS.mat'\n", "mat_contents = spio.loadmat(file, struct_as_record=False, squeeze_me=True)\n", "mov_dir = 3\n", "#time res\n", "print(f'Time resolution: {mat_contents[\"SparseFormat\"].TimeResolutionMS}')\n", "#get spikes\n", "spike_data = mat_contents['SparseFormat'].Data[mov_dir].toarray()\n", "#perform convolution\n", "k = np.ones(50)\n", "R = scipy.signal.lfilter(k, 1, spike_data, axis=0)\n", "\n", "plt.subplot(1, 2, 1)\n", "plt.plot(R);\n", "# plt.subplot(1, 2, 2)" ] }, { "cell_type": "markdown", "id": "occasional-symposium", "metadata": {}, "source": [ "4. As a result we obtain a time-resolved estimates of the spike count for each single trial. In a next step compute the variance and the mean across trials and the Fano factor. Plot the resulting function $FF(t)$ in the top panel of *Figure 2*. What is a good window width *w* for the boxcar kernel? Try out different single unit data files. What happens if the trial-averaged spike count drops to zero?\n", "5. In a next step we want to estimate the average Fano factor across different directions and many single units (as e.g. in Rickert et al., 2009 or Churchland et al., 2010).
\n", " To this end you need to modify your script to (i) loop across all six directions for each data file, and (ii) loop across a pre-defined list of data files. Choose one experimental condition C1, C2, or C3 and data either cut to TS or MO.
\n", " Compute per single unit the Fano factor for each movement direction, average $FF$ across directions. Then average across all single units and plot the grand average of the FF in panel 2 of *Figure 2*.\n", "6. Are your results consistent with the results in Churchland et al., 2010 (https://www.nature.com/articles/nn.2501) and Rickert et al., 2009 (https://www.jneurosci.org/content/29/44/13870 cf. supplemental material there)? During presentation of results compare Fano factors for different experimental conditions and for the same condition but different temporal cutting trigger (TS vs MO)." ] }, { "cell_type": "markdown", "id": "south-prayer", "metadata": {}, "source": [ "Given the vector v containing the observed spike counts (one per spike train) in the time window [t0, t1], $FF$ is defined as:\n", "$$ FF := \\frac{var(v)}{mean(v)} $$" ] }, { "cell_type": "code", "execution_count": 56, "id": "sound-hebrew", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2000,)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/3z/dbx85zz927g1_5_j8xt2npf0gxngn0/T/ipykernel_49186/2525774568.py:4: RuntimeWarning: invalid value encountered in divide\n", " ff = trial_var/trial_mean\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#part 4\n", "#FF\n", "trial_mean = R.mean(axis=1)\n", "trial_var = R.var(axis=1)\n", "ff = trial_var/trial_mean\n", "print(ff.shape)\n", "#plt.plot(ff);\n", "fig, ax = plt.subplots(3)\n", "ax[0].plot(trial_mean);\n", "ax[1].plot(trial_var);\n", "ax[2].plot(ff);\n" ] }, { "cell_type": "code", "execution_count": 87, "id": "bf666d0a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[array([0.94736842, 0.89473684, 0.78947368, ..., 1.28477952, 1.36045519,\n", " 1.36045519]), array([ nan, nan, nan, ..., 0.99382716, 0.99382716,\n", " 1.03055556]), array([ nan, nan, nan, ..., 1.91607143, 1.85495495,\n", " 1.85495495]), array([0.95 , 0.95 , 0.95 , ..., 1.9976874 , 1.9976874 ,\n", " 1.96017488]), array([ nan, nan, nan, ..., 2.16356581, 2.21847071,\n", " 2.17669173]), array([0.9375 , 0.9375 , 0.9375 , ..., 0.47533784, 0.47533784,\n", " 0.45371622])]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/3z/dbx85zz927g1_5_j8xt2npf0gxngn0/T/ipykernel_49186/1840297018.py:13: RuntimeWarning: invalid value encountered in divide\n", " ff_avg.append(trial_var/trial_mean)\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# part 5\n", "ff_avg = []\n", "\n", "#dfiff conditions (c1,c2,c3) organized by data file, here we are working with c1\n", "for direction, spikes in enumerate(mat_contents['SparseFormat'].Data):\n", " spikes_data = spikes.toarray()\n", " #apply kernel\n", " k = np.ones(1000)\n", " R = scipy.signal.lfilter(k, 1, spikes_data, axis=0)\n", " #get the means\n", " trial_mean = R.mean(axis=1)\n", " trial_var = R.var(axis=1)\n", " ff_avg.append(trial_var/trial_mean)\n", "\n", "print(ff_avg)\n", "for i in range(len(ff_avg)):\n", " plt.plot(ff_avg[i], label=f'direciton {i}')\n", " plt.legend()" ] }, { "cell_type": "markdown", "id": "conditional-bathroom", "metadata": {}, "source": [ "### **Part III – Interval versus count variability of neural spike trains**" ] }, { "cell_type": "markdown", "id": "overhead-sterling", "metadata": {}, "source": [ "In this part we will directly compare the variability of inter-spike intervals (ISI) with the\n", "trial-by-trial count variability of single units. This allows us to interpret the results in\n", "the light of stochastic point process theory. We have already introduced the Fano\n", "factor ($FF$) as a measure of trial-by-trial variability. In addition we here introduce the\n", "coefficient of variation ($CV$) of the lengths of inter-spike intervals which is defined as\n", "the ratio of standard deviation divided by the mean, i.e. a dimensionless number,\n", "hence a coefficient. The $CV$ is a measure for the irregularity of the spike train and\n", "sometimes spike train irregularity is used synonymously with inter-spike interval\n", "variability. For the broad class of renewal processes, on expectation the two random\n", "variables are approximately related as $ FF \\approx CV^2 $ (Cox and Lewis, 1966; Nawrot\n", "2010). For the Poisson Process, a special case of renewal processes, it holds that $ FF\n", "= CV^2 = 1 $ . For processes with interval history (non-renewal) this equality is violated\n", "(Nawrot 2010; Farkhooi et al., 2009, 2011; Avila-Akerberg and Chacron 2011). Any\n", "strong deviation from the equality can be interpreted with respect to short and long\n", "time scale variability as the ISI variability reflects variability on a short time scale (in\n", "the order of the average ISI) while the $FF$ reflects variability on a long time scale (on\n", "the order of the average separation of identical experimental trials)." ] }, { "cell_type": "markdown", "id": "offshore-cologne", "metadata": {}, "source": [ "The estimation of the CV is, however, hampered by the fact that single unit firing\n", "rates change over time. Here, we make use of a local measure termed CV2 (Holtky\n", "et al., 1996; Ponce-Alvarez et al., 2010). For any two consecutive inter spike intervals\n", "ISI and ISI' encountered in a single trial we define the coefficient of variation for these\n", "two intervals as\n", "$$ m = 2\\frac{\\mid ISI-ISI^{\\prime} \\mid}{ISI + ISI^{\\prime}}.$$" ] }, { "cell_type": "markdown", "id": "82bde2e0440b9c1b", "metadata": {}, "source": [] }, { "cell_type": "markdown", "id": "reported-glass", "metadata": {}, "source": [ "Collect these numbers for all consecutive intervals within the estimation window *W*\n", "and across all trials. Then compute the average across all *m*-values:\n", "$$ CV2 = \\frac{1}{N} \\sum_{i=1}^{N}m $$\n", "This is your CV2 estimate for one trial ensemble.\n" ] }, { "cell_type": "markdown", "id": "arranged-advertiser", "metadata": {}, "source": [ "Tasks\n", "1. The results shall be presented in *Figure 3* with 1 x 3 panels (1 row of 3 panels). You may use the `subplots` from `matplotlib.pyplot` module to generate several axes within one figure.\n", "2. To this end you will loop across all data files for condition C1 aligned to TS. You may use a predfined list or the function 'glob' from 'glob' to search all files matching a pattern. For each of these single units you compute both, the FF and the $CV2^2$ for all six directions within one fixed estimation interval or window *W*. The longer this estimation window, the more robust is the estimation of FF and CV2. Short estimation windows additionally introduce estimation biases (Nawrot et al., 2008). On the other hand you want to restrict your analysis to a well-defined experimental trial epoch. Inspect your results in Part II and define a window over a rather constant period of FF before the reaction signal RS, i.e. before the monkey moves his hand.\n", "3. *Computation of $CV2$*. \n", " To compute the $CV2$ and for each single trial ensemble you need to loop across directions and across trials. For each trial detect all ISIs within W and compute the *m*-values for all consecutive intervals. Collect these m-values for all trials and then compute the $CV2$. Repeat this for all six directions and keep for each single unit all six numbers.\n", "4. *Computation of FF.*\n", " Again for each single unit you need to loop across all six directions. For any given trial ensemble the number of spikes in each trial is easily computed by using the function `sum` on the binary spike matrix.\n", "5. *Irregularity versus trial-by trial variability*. \n", " Now scatter the squared $CV2^2$ against the FF in panel 1 of *Figure 3*. You might want to use logarithmic axes. Make sure that both axes appear with equal length in your figure (quadratic axes). Use the same limits for x and y axes.You can also plot the renewal assumption as a red line.\n", "6. *Marginal distributions*. \n", " Now compute the histograms of $FF$ and $CV2^2$ values and present those in the 2nd and 3rd axes of *Figure 3*. Again think of how to produce nice histograms on logarithmic axes which results in more symmetric distributions. Use the same axes limits as in the 1​st panel. These are called the marginal distributions of the 2D scatter.\n", "7. What is the average across the ratios of $FF$ / $CV2^2$." ] }, { "cell_type": "markdown", "id": "wicked-atlanta", "metadata": {}, "source": [ "You should be able to see clear deviations from the renewal expectation with $FF \\gg CV2^2$. We may interpret this as a non-stationarity across trials that results from\n", "different cortical network states (Nawrot et al., 2003; Nawrot 2010; Riehle et al., 2018)\n", "and attractor network models have been suggested to account for an excessive Fano\n", "factor (Deco et al., 2012; Litwin-Kumar and Doiron 2012; Mazzucato et al., 2015;\n", "Rost et al., 2018; Rostami et al., 2024). The reduction of the FF during task-evolvement (Part II) can\n", "also be interpreted with these models and, in addition, might be a result of cellular\n", "mechanisms (Farkhooi et al., 2013)." ] }, { "cell_type": "code", "execution_count": null, "id": "appreciated-shoulder", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "photographic-layer", "metadata": {}, "source": [ "## **References**" ] }, { "cell_type": "markdown", "id": "hybrid-perry", "metadata": {}, "source": [ "Arieli A, Sterkin A, Grinvald A, Aertsen A. 1996. Dynamics of ongoing activity: explanation of\n", "the large variability in evoked cortical responses. Science 273: 1868-1871\n", "\n", "\n", "Avila-Akerberg O, Chacron MJ (2011) Nonrenewal spike train statistics: causes and\n", "functional consequences on neural coding. Exp Brain Res (2011) 210:353–371\n", "\n", "\n", "Churchland AK, Kiani R, Chaudhuri R, Wang X-J, Pouget A, Shadlen MN (2011) Variance as\n", "a signature of neural computations during decision making. Neuron 69: 818-831.\n", "\n", "\n", "Churchland MM, Yu BM, Ryu SI, Santhanam G, Shenoy KV (2006) Neural variability in\n", "premotor cortex provides a signature of motor preparation. J Neurosci 26: 3697-3712.\n", "\n", "\n", "Churchland MM, Yu BM, Cunningham JP, Sugrue LP, Cohen MR, Corrado GS, Newsome\n", "WT, Clark AM, Hosseini P, Scott BB, Bradley DC, Smith MA, Kohn A, Movshon JA,\n", "Armstrong KM, Moore T, Chang SW, Snyder LH, Lisberger SG, Priebe NJ, Finn IM, Ferster\n", "D, Ryu SI, Santhanam G, Sahani M, Shenoy KV (2010) Stimulus onset quenches neural\n", "variability: a widespread cortical phenomenon. Nat Neurosci 13: 369-378.\n", "\n", "\n", "Cox DR, Lewis PAW. 1966. The statistical analysis of series of events. Monographs on\n", "Applied probability and statistics. London: Chapman & Hall.\n", "\n", "\n", "Deco G, Hugues E. 2012. Neural network mechanisms underlying stimulus driven variability\n", "reduction. ​PLoS Comput Biol​ ​8​: e1002395.\n", "\n", "\n", "Farkhooi F, Strube-Bloss MF, Nawrot MP (2009) Serial correlation in neural spike trains:\n", "experimental evidence, stochastic modeling, and single neuron variability. Phys Rev E Stat\n", "Nonlin Soft Matter Phys 79:021905\n", "\n", "\n", "Farkhooi F, Muller E, Nawrot MP (2011) Adaptation reduces variability of the neuronal\n", "population code. Phys Rev E 83: 050905.\n", "\n", "\n", "Farkhooi F, Froese A, Muller E, Menzel R, Nawrot MP. 2013. Cellular adaptation facilitates\n", "sparse and reliable coding in sensory pathways. ​PLoS Comput Biol​ 9: e1003251.\n", "\n", "\n", "Holt GR, Softky WR, Koch C, Douglas RJ (1996) Comparison of discharge variability in vitro\n", "and in vivo in cat visual cortex neurons. J Neurophysiol 75: 1806-1814.\n", "\n", "\n", "Litwin-Kumar A, Doiron B. 2012. Slow dynamics and high variability in balanced cortical\n", "networks with clustered connections. ​Nat Neurosci​ 15: 1498-1505.\n", "\n", "\n", "Kass RE, Ventura V, Brown EN (2005) Statistical Issues in the Analysis of Neuronal Data. J\n", "neurophysiol 94: 8-25\n", "\n", "\n", "Mazzucato L, Fontanini A, La Camera G. 2015. Dynamics of multistable states during\n", "ongoing and evoked cortical activity. ​J Neurosci​ 35: 8214-8231.\n", "\n", "\n", "Nawrot MP. 2010. Analysis and interpretation of interval and count variability in neural spikes\n", "trains. In: Grün S, Rotter S (eds) Analysis of parallel spikes trains. Springer Series in\n", "Computational Neuroscience 7. Springer Verlag, New York, Berlin, pp 34-58.\n", "\n", "\n", "Nawrot MP, Boucsein C, Rodriguez Molina V, Riehle A, Aertsen A, Rotter S. 2008.\n", "Measurements of variability dynamics in cortical spike trains. J Neurosci Meth 169: 374-390.\n", "\n", "\n", "Nawrot MP, Riehle A, Aertsen A, Rotter S (2003) Variability of motor cortical activity\n", "explained by network dynamics on multiple time scales. In: Ongoing activity in cortical\n", "networks: noise, variability and context (PhD thesis), URN: nbn:de:bsz:25-opus-73426.\n", "\n", "\n", "Ponce-Alvarez A, Kilavik BE, Riehle A (2010) Comparison of local measures of spike time\n", "irregularity and relating variability to firing rate in motor cortical neurons. J Comput Neurosci\n", "29: 351-365\n", "\n", "\n", "Rickert J, Riehle A, Aertsen A, Rotter S, Nawrot MP (2009) Dynamic encoding of movement\n", "direction in motor cortical neurons. Journal of Neuroscience 29: 13870-13882\n", "\n", "\n", "Riehle, A. Monkey behavioral neurophysiology during a delayed center-out reach task with ambiguous target information. https://doi.org/10.12751/g-node.rz77m8 (2024).\n", "\n", "\n", "Riehle A, Vaadia E (Eds., 2005) Motor Cortex in Voluntary Movements: a distributed system\n", "for distributed functions. CRC Press, Boca Raton, Florida, USA\n", "\n", "\n", "Riehle, A., Brochier, T., Nawrot, M., & Grün, S.. (2018). Behavioral Context Determines\n", "Network State and Variability Dynamics in Monkey Motor Cortex. Frontiers in Neural Circuits,\n", "12, 52\n", "\n", "\n", "Rost, T., Deger, M., & Nawrot, M. P.. (2018). Winnerless competition in clustered balanced\n", "networks: inhibitory assemblies do the trick. Biological Cybernetics, 112(1-2), 81–98\n", "\n", "\n", "Rostami, V., Rost, T., Schmitt, F. J., van Albada, S. J., Riehle, A., & Nawrot, M. P. (2024). Spiking attractor model of motor cortex explains modulation of neural and behavioral variability by prior target information. Nature Communications, 15(1), 6304.\n", "\n", "\n", "Shadlen MN, Newsome WT. 1998. The variable discharge of cortical neurons: implications\n", "for connectivity, computations, and information coding. J Neurosci 18: 3870-3896\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 5 }