{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Spike Train Analysis: Exercise 0 - Visualizing Spike Trains\n",
"\n",
"This tutorial covers the following topics:\n",
"\n",
"* Introduction to NEO and Elephant\n",
"* Computing ISI distribution and PSTH of spike trains using Elephant functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NEO: representing electrophysiology data in Python\n",
"\n",
"\n",
"\n",
"|||\n",
"|--:|---|\n",
"|Homepage|[http://neuralensemble.org/neo](http://neuralensemble.org/neo) |\n",
"|GitHub|[https://github.com/NeuralEnsemble/python-neo](https://github.com/NeuralEnsemble/python-neo)|\n",
"|Documentation|[https://neo.readthedocs.io/en/latest/](https://neo.readthedocs.io/en/latest/)|\n",
"|Bug reports|[https://github.com/NeuralEnsemble/python-neo/issues](https://github.com/NeuralEnsemble/python-neo/issues) |\n",
"|Python Package Index (PyPI)|[https://pypi.org/project/neo/](https://pypi.org/project/neo/) |\n",
"\n",
"NEO provides a collection of useful Python class objects that are designed specifically for storing electrophysiology recording data.\n",
"\n",
"The schematic below illustrates the NEO data object model.\n",
"\n",
"![Structure](images/base_schematic.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Elephant: data processing and analysis\n",
"\n",
"![Elephant](images/elephant_logo.png)\n",
"\n",
"|||\n",
"|--:|---|\n",
"|Homepage|[http://python-elephant.org](http://python-elephant.org) |\n",
"|GitHub|[https://github.com/NeuralEnsemble/elephant](https://github.com/NeuralEnsemble/elephant)|\n",
"|Documentation|[https://elephant.readthedocs.io/en/stable/](https://elephant.readthedocs.io/en/stable/)|\n",
"|Bug reports|[https://github.com/NeuralEnsemble/elephant/issues](https://github.com/NeuralEnsemble/elephant/issues) |\n",
"|Python Package Index (PyPI)|[https://pypi.org/project/elephant/](https://pypi.org/project/elephant/) |\n",
"\n",
"Elephant, short for ELEctroPHysiology Analysis Toolkit, is an open-source library for the analysis of electrophysiology data in Python.\n",
"It depends on NEO as the backend for data representation. This means that many of Elephant functions take NEO data objects as input and return the results also as NEO data objects."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import quantities as pq\n",
"import neo\n",
"import elephant.spike_train_generation as stgen\n",
"import elephant.statistics as stats\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 1.1: generate and handle `neo.SpikeTrain` object\n",
"\n",
"Let's first generate a `neo.SpikeTrain` object and play around with it, to get used to handling NEO data objects.\n",
"\n",
"To generate a spike train data, we use here an elephant function `elephant.spike_train_generation.StationaryPoissonProcess()`, which generates a random spike train as a realization of a stationary Poisson process with a given rate. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Generate a Poisson spike train\n",
"rate = 10. * pq.Hz\n",
"t_start = 0. * pq.ms\n",
"t_stop = 1000. * pq.ms\n",
"\n",
"spiketrain = stgen.StationaryPoissonProcess(rate, t_start, t_stop).generate_spiketrain()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SpikeTrain containing 7 spikes; units ms; datatype float64 \n",
"time: 0.0 ms to 1000.0 ms"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spiketrain"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[110.79693738 244.32981503 280.0937049 344.60708914 378.47823147\n",
" 460.62583834 522.49564963] ms\n"
]
}
],
"source": [
"# spike times\n",
"print(spiketrain.times)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.11079694 0.24432982 0.2800937 0.34460709 0.37847823 0.46062584\n",
" 0.52249565] s\n"
]
}
],
"source": [
"# spike times in the units of second\n",
"print(spiketrain.times.rescale('s'))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.11079694 0.24432982 0.2800937 0.34460709 0.37847823 0.46062584\n",
" 0.52249565]\n"
]
}
],
"source": [
"# spike times as a plain numpy array in the units of second\n",
"print(spiketrain.rescale('s').magnitude)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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