From b3b27724cbea0a1a3ea5d3cb1b95094c8ae31a2b Mon Sep 17 00:00:00 2001 From: Marcel Stimberg Date: Thu, 12 Dec 2019 16:16:42 +0100 Subject: [PATCH] add the completed notebook from the course --- 2019-TD-Brian-Sorbonne/course_notebook.ipynb | 1165 ++++++++++++++++++ 1 file changed, 1165 insertions(+) create mode 100644 2019-TD-Brian-Sorbonne/course_notebook.ipynb diff --git a/2019-TD-Brian-Sorbonne/course_notebook.ipynb b/2019-TD-Brian-Sorbonne/course_notebook.ipynb new file mode 100644 index 0000000..914dd0e --- /dev/null +++ b/2019-TD-Brian-Sorbonne/course_notebook.ipynb @@ -0,0 +1,1165 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulating neural computation with Brian 2\n", + "## Preliminary remarks: the jupyter notebook\n", + "The [jupyter notebook](http://jupyter.readthedocs.io/) is a tool to mix text, code, and results/figures generated by the code in a single document that can be shared and remains editable.\n", + "\n", + "We here use it with Python, but support for other programming languages exists.\n", + "\n", + "Code written in a cell will be executed when you press `Ctrl+Enter` or `Shift+Enter` (this will also jump to the next code cell, or open a new code cell if none exists):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3 + 4 -3" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "24" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "6 * 4" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "x = 7" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that cells can be executed in arbitrary order, which can potentially get confusing. E.g. changing the value of `x` above after executing the cell below will make the two cells inconsistent. You can determine the order of evaluations by looking at the number in square brackets in front of each cell. However, this is not foolprof, e.g. if you change the content of a cell without re-executing it, the number does not change. To make sure that a document does not have any such oddities in it, you can run it from scratch by chosing `Kernel --> Restart & Run All` in the menu." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "11" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x + 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Text can be formatted using the light-way formatting language [markdown](https://daringfireball.net/projects/markdown/syntax). You can have for example text in *italics* or **bold**. It also allows you to write mathematical expressions using LaTeX syntax: $\\sqrt 2$ (double click a formatted cell to see its \"source code\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Brian – Neuron models\n", + "Let's import \"everything\" from the Brian 2 package.\n", + "\n", + "This also provides access to the scientific computing package [numpy](http://www.numpy.org/) (imported as `np`), and to the package `pyplot` from the plotting library [matplotlib](http://matplotlib.org) (imported as `plt`)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from brian2 import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also switch off Brian's \"code generation\" mechanism, which improves the performance for complex models by generating/compiling/executing C++ code \"behind the scenes\". For the simple models that we are covering in this tutorial, this is not necessary – it even slows down things due to the need for compilation." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "prefs.codegen.target = 'numpy'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also ask the notebook to include plots directly in the notebook (instead of showing them in a separate window). Note that lines starting with `%` are specific commands for the jupyter notebook, they won't work in a Python script, for example. (We use this specific command here because the matplotlib version installed on the machines in the computer room is quite old. In general, a better choice is `%matplotlib notebook` which enables plots that are zoomable, etc.)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start using Brian! Brian provides a system for physical units:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "Rm = 1*Mohm\n", + "I = 50*nA" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$1.0\\,\\mathrm{M}\\,\\mathrm{ohm}$" + ], + "text/plain": [ + "1. * Mohm" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Rm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Operations with physical quantities lead to new quantities with potentially different units:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$0.5\\,\\mathrm{m}\\,\\mathrm{A}$" + ], + "text/plain": [ + "0.5 * mamp" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "I*10000" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$50.0\\,\\mathrm{m}\\,\\mathrm{V}$" + ], + "text/plain": [ + "50. * mvolt" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Rm * I" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Brian will complain if operations do not make sense:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "ename": "DimensionMismatchError", + "evalue": "Cannot calculate 1. Mohm + 50. nA, units do not match (units are ohm and amp).", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mDimensionMismatchError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mRm\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mI\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/mnt/data/anaconda2/envs/brian2_cours/lib/python3.7/site-packages/brian2/units/fundamentalunits.py\u001b[0m in \u001b[0;36m__add__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 1427\u001b[0m return self._binary_operation(other, operator.add,\n\u001b[1;32m 1428\u001b[0m \u001b[0mfail_for_mismatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1429\u001b[0;31m operator_str='+')\n\u001b[0m\u001b[1;32m 1430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1431\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__radd__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/mnt/data/anaconda2/envs/brian2_cours/lib/python3.7/site-packages/brian2/units/fundamentalunits.py\u001b[0m in \u001b[0;36m_binary_operation\u001b[0;34m(self, other, operation, dim_operation, fail_for_mismatch, operator_str, inplace)\u001b[0m\n\u001b[1;32m 1367\u001b[0m _, other_dim = fail_for_dimension_mismatch(self, other, message,\n\u001b[1;32m 1368\u001b[0m \u001b[0mvalue1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1369\u001b[0;31m value2=other)\n\u001b[0m\u001b[1;32m 1370\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1371\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mother_dim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/mnt/data/anaconda2/envs/brian2_cours/lib/python3.7/site-packages/brian2/units/fundamentalunits.py\u001b[0m in \u001b[0;36mfail_for_dimension_mismatch\u001b[0;34m(obj1, obj2, error_message, **error_quantities)\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mDimensionMismatchError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_message\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mDimensionMismatchError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_message\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 187\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 188\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdim1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDimensionMismatchError\u001b[0m: Cannot calculate 1. Mohm + 50. nA, units do not match (units are ohm and amp)." + ] + } + ], + "source": [ + "Rm + I" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Python error messages are very verbose: have a look at the *end* of the error message first!)\n", + "\n", + "Let's define a simplified integrate-and-fire model (note that during most of the course, we used the model with a `-` missing, i.e. with a leak current driving the membrane potential *away* from the leak current instead towards it..., this has been fixed in this notebook): " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO No numerical integration method specified for group 'neurongroup', using method 'exact' (took 0.20s). [brian2.stateupdaters.base.method_choice]\n" + ] + } + ], + "source": [ + "Cm = 200*pF\n", + "G_leak = 10*nS\n", + "E_leak = -70*mV\n", + "I_e = 1*nA\n", + "eqs = 'dVm/dt = (-G_leak*(Vm - E_leak) + I_e)/Cm : volt'\n", + "neurons = NeuronGroup(1, eqs, threshold='Vm>-55*mV',\n", + " reset='Vm = E_leak')\n", + "run(100*ms)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now look a the membrane potential after the 100ms:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "neurons.Vm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to look at the development of the membrane potential over time, we'll have to tell Brian to record the values of `V` during the simulation. From now on, we'll also add call to `start_scope()` at the beginning of each new model. `start_scope` is a Brian function that is only needed when executing many models in a jupyter notebook. It tells Brian to forget about all previous simulations. This is not necessary all the time, e.g. in the following code blocks we will overwrite the neurons variable, therefore there is no ambiguity as to which model should be simulated. As soon as models get more complex and have multiple components, it can become messy otherwise.\n", + "\n", + "Let's also get rid of the information about the integration algorithm: Brian emits this message because we did not explicitly say how the equations should be integrated. In this case, Brian decides which integration algorithm to use and tells us about its choice. In our case, the equations are simple enough to be solved analytically, we therefore chose the method 'exact' (other options would be for example 'euler' to integrate with the --fast but not accurate-- forward Euler, or 'rk4' to integrate with a fourth-order Runge-Kutta method):" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "start_scope()\n", + "Cm = 200*pF\n", + "G_leak = 10*nS\n", + "E_leak = -70*mV\n", + "I_e = 1*nA\n", + "eqs = 'dVm/dt = (-G_leak*(Vm - E_leak) + I_e)/Cm : volt'\n", + "neurons = NeuronGroup(1, eqs, threshold='Vm>-55*mV',\n", + " reset='Vm = E_leak', method='exact')\n", + "neurons.Vm = E_leak\n", + "monitor = StateMonitor(neurons, 'Vm', record=True)\n", + "run(100*ms)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can plot the values using Python's plotting library. Note that we divide the physical quantitities by the scale we are interested in to get values \"in ms\" or \"in mV\":" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(monitor.t/ms, monitor.Vm[0]/mV)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the neuron fires regularly in response to the constant current injection.\n", + "(Note that for a visualization in a paper, you'd probably add a vertical line for each spike to make the membrane potential look \"more realistic\")\n", + "\n", + "To get rid of the distracting \"Out\" line, we can add a semicolon at the end of the line (something that is only relevant to the last line of a code cell when running things in an interactive environment like the jupyter notebook -- you *don't* have to add a semicolon to every line!)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(monitor.t/ms, monitor.Vm[0]/mV);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Constants defined outside of the equations (e.g. $\\tau_m$ in our case) are the same for all neurons in a group. If we want to have neuron-specific values, we'll have to include them in the equations. We do this for a constant current input $I_{ext}$ that can have differing strengths for each neuron. We can set this value in the same way that we set the initial value of the membrane potential; by declaring $I_{ext}$ in the model equations it becomes accessible as an attribute of `neurons`:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "start_scope()\n", + "Cm = 200*pF\n", + "G_leak = 10*nS\n", + "E_leak = -70*mV\n", + "eqs = '''dVm/dt = (-G_leak*(Vm - E_leak) + I_ext)/Cm : volt\n", + " I_ext : amp'''\n", + "neurons = NeuronGroup(3, eqs, threshold='Vm>-55*mV',\n", + " reset='Vm = E_leak', method='exact')\n", + "neurons.Vm = E_leak\n", + "neurons.I_ext = [0.1, 0.5, 1.0]*nA\n", + "monitor = StateMonitor(neurons, 'Vm', record=True)\n", + "run(100*ms)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(monitor.t/ms, monitor.Vm[0]/mV)\n", + "plt.plot(monitor.t/ms, monitor.Vm[1]/mV)\n", + "plt.plot(monitor.t/ms, monitor.Vm[2]/mV);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To plot the voltages of all neurons at once, we can plot `monitor.Vm` (instead of `monitor.Vm[0]` etc.). However, matplotlib wants the first dimension (i.e., the number of rows) of `monitor.Vm` to be the same as the dimension of `monitor.t`, but the first dimension of `monitor.Vm` is the neuron index in Brian. We therefore have to transpose the matrix, i.e. use `monitor.Vm.T`:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(monitor.t/ms, monitor.Vm.T/mV);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let usintroduce a new monitor, a `pikeMonitor` that only records the spiking activity the cells, i.e. the time of spikes and the index of the neuron that emitted it." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "start_scope()\n", + "Cm = 200*pF\n", + "G_leak = 10*nS\n", + "E_leak = -70*mV\n", + "eqs = '''dVm/dt = (-G_leak*(Vm - E_leak) + I_ext)/Cm : volt\n", + " I_ext : amp'''\n", + "neurons = NeuronGroup(3, eqs, threshold='Vm>-55*mV',\n", + " reset='Vm = E_leak', method='exact')\n", + "neurons.Vm = E_leak\n", + "neurons.I_ext = [0.25, 0.5, 1.0]*nA\n", + "monitor = StateMonitor(neurons, 'Vm', record=True)\n", + "spike_monitor = SpikeMonitor(neurons)\n", + "run(100*ms)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we plot the indices of the neurons that spiked against the time of the spike, we get a classical \"raster plot\":" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(spike_monitor.t/ms, spike_monitor.i, 'o');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The neuron with the weakest input (on the bottom) emits fewer spikes than the neurons with stronger inputs. To quantitfy this more systematically, let's use 100 neurons and vary the input current between 0nA and 0.5nA in 100 steps (using numpy's [`linspace` function](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html)):" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "start_scope()\n", + "Cm = 200*pF\n", + "G_leak = 10*nS\n", + "E_leak = -70*mV\n", + "eqs = '''dVm/dt = (-G_leak*(Vm - E_leak) + I_ext)/Cm : volt\n", + " I_ext : amp'''\n", + "neurons = NeuronGroup(100, eqs, threshold='Vm>-55*mV',\n", + " reset='Vm = E_leak', method='exact')\n", + "neurons.Vm = E_leak\n", + "neurons.I_ext = np.linspace(0, 0.5, 100)*nA\n", + "monitor = StateMonitor(neurons, 'Vm', record=True)\n", + "spike_monitor = SpikeMonitor(neurons)\n", + "run(100*ms)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(spike_monitor.t/ms, spike_monitor.i, '.');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The raster plot shows some interesting patterns, but to more clearly see the relationship between the firing rate $f$ and the input current $I$, we'll plot a so-called \"f/I curve\", i.e. the firing rate over the input current. For this, we can use the total spike count that is stored for every neuron in the variable `count` of the `SpikeMonitor`:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(neurons.I_ext/nA, spike_monitor.count/(100*ms) ,'.');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This shows that a current of around 0.15nA is needed to elicit spikes, and stronger currents elicit more spikes in an approximately proportional fashion. Note that there are a weird steps in the plot -- this is simply because of our short simulation time: we cannot have \"half a spike\", so e.g. a current that is stronger than a current that elicits 9 spikes during our 100ms simulation, can only either also elicit 9 spikes (no change in the plot) or 10 spikes (jump by 10 Hz). There are various ways to make this estimation better (e.g. run multiple simulations with the same current but use different initial values for the membrane potential), but the easiest is to simply run a longer simulation:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "start_scope()\n", + "Cm = 200*pF\n", + "G_leak = 10*nS\n", + "E_leak = -70*mV\n", + "eqs = '''dVm/dt = (-G_leak*(Vm - E_leak) + I_ext)/Cm : volt\n", + " I_ext : amp'''\n", + "neurons = NeuronGroup(100, eqs, threshold='Vm>-55*mV',\n", + " reset='Vm = E_leak', method='exact')\n", + "neurons.Vm = E_leak\n", + "neurons.I_ext = np.linspace(0, 0.5, 100)*nA\n", + "monitor = StateMonitor(neurons, 'Vm', record=True)\n", + "spike_monitor = SpikeMonitor(neurons)\n", + "run(1000*ms)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(neurons.I_ext/nA, spike_monitor.count/(1000*ms) ,'.');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How does this curve change with changes in the capacitance ($Cm$) and the leak conductance ($G_\\text{leak}$) of the cell? We simply tried this out during the course, but a slightly more systematic evaluation could look like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "start_scope()\n", + "G_leak = 10*nS\n", + "E_leak = -70*mV\n", + "eqs = '''dVm/dt = (-G_leak*(Vm - E_leak) + I_ext)/Cm_variable : volt\n", + " I_ext : amp\n", + " Cm_variable : farad'''\n", + "neurons = NeuronGroup(3*100, eqs, threshold='Vm>-55*mV',\n", + " reset='Vm = E_leak', method='exact')\n", + "# We use three different values for Cm (called Cm_variable here), and\n", + "# use the full range of current values for each\n", + "neurons.Vm = E_leak\n", + "# We can refer to a subset of the neuron's with Python's \"slicing notation\"\n", + "neurons[0:100].Cm_variable = 100*pF # first 100 neurons\n", + "neurons[0:100].I_ext = np.linspace(0, 0.5, 100)*nA\n", + "neurons[100:200].Cm_variable = 200*pF # next 100 neurons\n", + "neurons[100:200].I_ext = np.linspace(0, 0.5, 100)*nA\n", + "neurons[200:300].Cm_variable = 400*pF # last 100 neurons\n", + "neurons[200:300].I_ext = np.linspace(0, 0.5, 100)*nA\n", + "monitor = StateMonitor(neurons, 'Vm', record=True)\n", + "spike_monitor = SpikeMonitor(neurons)\n", + "run(1000*ms)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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S0cApmr4oLSncpXT6dg67pi9KGxTuUhp9PYc96fa6CnQJKNylFPqqFNPu+aOavigRFO5SCn1TitH5o5IShbsUVqMMM7ZsHmPL5lW7FKPzRytt4sAE4/vHGT1hFCDy8coFK1P9TIW7FFJzGWbzujE2rxs7HPaVKMXo/NFKawT6ccPH8dmHPstkbZKhgXrkTs9MH/F4eHCYG8+5MdWAV7hLITWXYbbtPMiGs06uRqiDzh+tqKhAH7ABZnyGGWaYmpkCwPGjHo/vH1e4S3VVckZMtwOkCvWeiyurrFywsmWg4zBgAxjGoA0CUPPaEY/nDMw5/N5pUbhLYVRyRowGSEtv4sAEH/7hh48qqwwPDnPV6Ve1DPQ5A3O46vSreG7yOdXcpb9UcldHDZCWRqvBzr0v7mWyNhlZVrnn6XsOPzdboDcHd/jntEO9QeEuPVWZXR3j9kvXAGkhtTPYOTQwxNDAUGQp5V1L3sXD+x9mamaqZaDnTeEuPVWJXR3DpZfm/dI1QFoYnQ521rzGhSsuZOGxCyNLKSvmrsistNINhbv0RGl3dWw1OBq1X7oGSDPXqrTS7WDnmuVrYkspKxesLFSoNyjcJXelHThNOjiq/dJz0U5ppWiDnXlQuEvuSreVQKeDowr1VET1ytstrRRtsDMPCnfJXSm2EtDq0Z5pDvOoaYidlFaKNNiZh67C3cx2AS8ANWDa3UfN7LXAt4ERYBdwsbsf6q6ZUgXhvWIKvZWAVo/molWPfHhwmDXL10ROQ+yX0ko30ui5n+Xuvwv9fA1wr7tfZ2bXBD9fncLnSIlF7RVTiFKMVo9mrlWIx/XIp2amMIzhwWGmZqba6olXpbTSjSzKMmuBM4PHtwA/RuHe96L2iul5j12rRzPTarAzrk7e3CNfs3wNa5avUU+8A92GuwM/NDMH/t3dNwEnuPs+AHffZ2YLon7RzNYD6wGWLFnSZTOkqAq5V4xWj6am08HOuDp5XI9cPfH2dRvu73D3vUGA/8jMnkj6i8FfBJsARkdHvct2SAEVasqjBki70mlpJWmIN95TPfL0dBXu7r43+OcBM/susBrYb2YLg177QuBACu2UEirMlEcNkHYkrdJK0hBXqKer43A3s1cBA+7+QvD4HOBfgK3AZcB1wT/vTKOhUh6FKcVElV80QHqUvEorDQrxfHTTcz8B+K6ZNd7nNnf/gZk9BGwxs8uBp4H3dt9MKYuel2JalV80QAq01ytXaaWcOg53d98JnBZx/SBwdjeNkvLqSSkmKtBnK79UONCTnNUJLy8MUmmlurRCVVKV++rTuHp6q/JLhbR7Vmd4YZBKK9WlcJdUrVo6N5/Vp0nq6RUuv3RzVmd4YZBKK9WlcJdUhLcWWLV0bjah3of19HYGO9vZvja8MEi98mpSuEvXorYWSD3ck05nrFCgtzvY2e4eKwrxalO4S9cy21ogvO/LrvuTT2cssE4Plej37WulfQp36Vqqg6hRpZdGuSV8ulGJyi9pHCrR79vXSvsU7tKx1LfwjSu91CbrQX7Z1tKsIO10wLPTKYgizRTu0pFUt/BNMvOlEegFCvXZSixR88jTOFRCoS5JKdylI13X2Us686VViWW2eeQ6VELypHCXjnRUZy/RStJuzu2cbR65BjwlLwp36Ujbi5UKupI0i61sW80jF8mDwl06lmixUgFXkua1la1CXXpJ4S7ZaT7GLud6uraylX6mcJe2NG8zECmqt55TPV1b2YrUKdwlsUTbDMzWW0+xnp5Gr1xb2UqVKdwlsVmnP+bQW8+iV64Ql6pSuEtisdMfU+ytt7swSKs7RaIp3CWxo6Y/DvwP3N99b72bhUHqlYtEU7hLWw5Pf+yyt97u3is6YEKkPQp36Ux4C95ZeutpHDahAyZE2qdwl86MnHHkFrwRvfWJAxOH6+TdHjahhUEi7VG4S3vCB2iEtuCdOGaY8V/ddEQo731x7+E6eRqHTYhIcgp3SS5cZx8crof7GVfG9tCHBoYYGhhqe1tbEemewl2SC9fZa5NMPHEH479/JLaHXvMaF664kIXHLtTAp0jOFO6S3MgZTLzilYwPD3CcG5/9vx8xufcHs/bQ1yxfo21tRXpA4S6JTRwzzIcXLmByZqo+KDpTY4YZ9dBFCkjhLomN7x9ncqbGDBw1KKoeukixZBbuZnYe8CVgELjJ3a/L6rMkH6MnjCY+YUhEeiuTcDezQeAG4C+APcBDZrbV3R9L+7Pu/MlNbN91N6tGzgUo1OO171xX6PZ10tYbz7lRJReREjB3T/9Nzd4GfNrdzw1+vhbA3f816vWjo6M+Pj7e9ufc+ZOb+MzOLzJlMOgOGLWCPJ7jcPExb2fLSz8vZPs6aesch39edgVr37mu7e9KRNJnZtvdfTTquYGMPnMR8Ezo5z3BtXCj1pvZuJmNP/vssx19yPZddzNlMGPGtBnThXoMEy+MF7h9nbS1/r+5iBRfVuFuEdeO+E8Ed9/k7qPuPjp//vyOPmTVyLnM8XrvcsidoUI9hpWvHi1w+zppK4fLNCJSbFkNqO4BFod+PhHYm/aHNMoD23fdzaqTzi3c47XvXMcbG3XsgrSp27aqJCNSDlnV3IeA3wBnA78FHgLe7+6PRr2+05q7iEg/m63mnknP3d2nzeyjwN3Up0LeHBfsIiKSvszmubv7XcBdWb2/iIjEy2pAVUREekjhLiJSQQp3EZEKUriLiFRQJlMh226E2bPA7i7e4njgdyk1pwz67X5B99wvdM/tWerukatACxHu3TKz8bi5nlXUb/cLuud+oXtOj8oyIiIVpHAXEamgqoT7pl43IGf9dr+ge+4XuueUVKLmLiIiR6pKz11EREIU7iIiFVSacDez88zsSTPbYWbXRDxvZvbl4PlHzOytvWhnmhLc85vM7AEze8nMPt6LNqYtwT1/IPh+HzGzn5vZab1oZ5oS3PPa4H4ngtPL/rwX7UxTq3sOve50M6uZ2UV5ti8LCb7nM83sueB7njCzT3b1ge5e+D/Utw3+X2AZMAz8Ejil6TXnA9+nfgrUGPCLXrc7h3teAJwObAQ+3us253TPbwfmBo//sk++52N5eXzszcATvW531vccet1/U99d9qJetzuH7/lM4HtpfWZZeu6rgR3uvtPdJ4HbgbVNr1kL3Op124DXmNnCvBuaopb37O4H3P0hYKoXDcxAknv+ubsfCn7cRv2UrzJLcs8vevBvP/Aqmo6sLKEk/z4D/ANwB3Agz8ZlJOk9p6Ys4d7ywO2ErymTqt1PEu3e8+XU/2utzBLds5ldYGZPAP8F/E1ObctKy3s2s0XABcDXc2xXlpL+f/ttZvZLM/u+mZ3azQeWJdxbHrid8DVlUrX7SSLxPZvZWdTD/epMW5S9RPfs7t919zcB7wE+k3mrspXknr8IXO3utRzak4ck9/ww9b1iTgP+DfjPbj6wLOGe5MDtXA7lzlHV7ieJRPdsZm8GbgLWuvvBnNqWlba+Z3f/KbDczI7PumEZSnLPo8DtZrYLuAj4qpm9J5/mZaLlPbv78+7+YvD4LmBON99zWcL9IWCFmZ1kZsPAJcDWptdsBT4UzJoZA55z9315NzRFSe65alres5ktAb4DfNDdf9ODNqYtyT2fbGYWPH4r9QG5Mv+l1vKe3f0kdx9x9xHgP4C/d/euerI9luR7fl3oe15NPZ87/p4zO0M1TR5z4LaZ/W3w/Nepj6ifD+wA/gD8da/am4Yk92xmrwPGgT8BZszsCuoj8M/3rOFdSPg9fxKYR70nBzDtJd5FMOE9X0i94zIF/BF4X2iAtXQS3nOlJLzni4C/M7Np6t/zJd18z9p+QESkgspSlhERkTYo3EVEKkjhLiJSQQp3EZEKUriLiFSQwl1EpIIU7iIiFfT/rHTGrqhVUdwAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(neurons.I_ext[0:100]/nA, spike_monitor.count[0:100]/(1000*ms) ,'.')\n", + "plt.plot(neurons.I_ext[100:200]/nA, spike_monitor.count[100:200]/(1000*ms) ,'.')\n", + "plt.plot(neurons.I_ext[200:300]/nA, spike_monitor.count[200:300]/(1000*ms) ,'.');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A change in capacitance changes the slope of the f/I curve, while the minimal current to elicit a spike is unchanged.\n", + "\n", + "We do the same analysis for the leak conductance:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "start_scope()\n", + "Cm = 200*pF\n", + "E_leak = -70*mV\n", + "eqs = '''dVm/dt = (-G_leak_variable*(Vm - E_leak) + I_ext)/Cm : volt\n", + " I_ext : amp\n", + " G_leak_variable : siemens'''\n", + "neurons = NeuronGroup(3*100, eqs, threshold='Vm>-55*mV',\n", + " reset='Vm = E_leak', method='exact')\n", + "neurons.Vm = E_leak\n", + "neurons[0:100].G_leak_variable = 5*nS # first 100 neurons\n", + "neurons[0:100].I_ext = np.linspace(0, 0.5, 100)*nA\n", + "neurons[100:200].G_leak_variable = 10*nS # next 100 neurons\n", + "neurons[100:200].I_ext = np.linspace(0, 0.5, 100)*nA\n", + "neurons[200:300].G_leak_variable = 20*nS # last 100 neurons\n", + "neurons[200:300].I_ext = np.linspace(0, 0.5, 100)*nA\n", + "monitor = StateMonitor(neurons, 'Vm', record=True)\n", + "spike_monitor = SpikeMonitor(neurons)\n", + "run(1000*ms)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(neurons.I_ext[0:100]/nA, spike_monitor.count[0:100]/(1000*ms) ,'.')\n", + "plt.plot(neurons.I_ext[100:200]/nA, spike_monitor.count[100:200]/(1000*ms) ,'.')\n", + "plt.plot(neurons.I_ext[200:300]/nA, spike_monitor.count[200:300]/(1000*ms) ,'.');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A change in the leak conductance has a very different effect, it leaves the slope of the curve unaffected, but shifts the curve along the x axis: with a stronger leak conductance, a stronger current is needed to elicit a spike." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Brian – Synapse models\n", + "\n", + "The \"input\" our cell received in the previous model was a constant current input. While we can inject constant currents into neurons during an experiment, the membrane potential of neurons under normal conditions is mostly affect by the temporary currents arising from the impact of chemical synapses. As a first step towards modeling such synapses, we make our current temporary (decaying explonentionally to zero). We'll also record the evolution of this current over time by including it in our `StateMonitor`:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "start_scope()\n", + "Cm = 200*pF\n", + "G_leak = 10*nS\n", + "E_leak = -70*mV\n", + "tau_syn = 5*ms\n", + "eqs = '''dVm/dt = (-G_leak*(Vm - E_leak) + I_syn)/Cm : volt\n", + " dI_syn/dt = -I_syn/tau_syn : amp'''\n", + "neurons = NeuronGroup(1, eqs, threshold='Vm>-55*mV',\n", + " reset='Vm = E_leak', method='exact')\n", + "neurons.Vm = E_leak\n", + "neurons.I_syn = 0.5*nA\n", + "monitor = StateMonitor(neurons, ['Vm', 'I_syn'], record=True)\n", + "spike_monitor = SpikeMonitor(neurons)\n", + "run(100*ms)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use the `subplots` function to create two rows (and one colum) of plots, the ax variable is now a list of two axes that we can index with `[..]` (remember that indices in Python start at 0):" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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vzY8AF3da1mUbnd/t+cAkZ5v7nO+5E+aZIKBDl9iq2gy0d4kdMVT1QPugPqpaS+jLIZtQOx91VnsUuNKdCsNDRHKAzwMPdlgcsW0WkWRgDvAQgKo2q2oVEdxmRwCIE5EAEE+oT7KIarOqvgdUdlrcXRvnAU+papOq7iLUO8PpJ/O5XgqC7rq7jkgikg9MB1YAQ9r7b3Kee7czc/fdC/wICHZYFsltHgmUAX9wDoc9KCIJRHCbVXU/cA+wl1AX9dWq+hoR3OYOumtjr32neSkIjqu760ggIonAc8B3VbXG7XrCSUQuA0pVdbXbtfShADADuF9VpwN1DPxDIkflHBefB4wAhgEJIrLA3apc12vfaV4KAk90dy0iUYRC4E+q+ryzuEREspzXs4BSt+oLg9nAFSKym9DhvvNE5HEiu81FQJGqrnDmnyUUDJHc5guAXapapqotwPPALCK7ze26a2Ovfad5KQiOp0vsAU1CA+g+BGxS1V91eGkJcL0zfT2wuK9rCxdVvVNVc1Q1n9DP9C1VXUBkt/kgsE9ExjmLzic0/GvEtpnQIaGZIhLv/D8/n9A5sEhuc7vu2rgEmC8iMSIygtC47ytP6hNU1TMPQt1dbwV2AD9xu54wtO8sQruGa4E1zuNSIJ3Q1QbbnOc0t2sNU/vnAi850xHdZmAaUOj8rF8EBnmgzT8DNgPrgceAmEhrM/AkoXMgLYT+4v/G0doI/MT5PtsCXHKyn2tdTBhjjMd56dCQMcaYLlgQGGOMx1kQGGOMx4V1zOJwycjI0Pz8fLfLMMaYAWX16tXlqprZefmADIL8/HwKCwvdLsMYYwYUEdnT1XI7NGSMMR43IPcI+jtV5WBNI3sq6mlqDZISF0XuoDjSE2PcLs0YYz7DgqAXFR2q57Fle1i8ppiDNY2feX1kZgJzxmRy1YwcJmcnE7pB0hhj3GVB0Ata24L85q3tPPDuDoJBZe64TG6eO4pRmYnERvmoqm9hR9lhlu+s4ImVe3lk6W6m5KTw7fPGcP6EwRYIxhhX9fjOYhG5DbiV0KAof1XVH4nIhcBdQDTQDPxQVd/qYtt/A75JqEtdgH9S1ZeP9ZkFBQXaX04WVze08M1HC1m5u5Irpw3jRxePZ1hq3FHXX7JmP79/fxd7K+uZmpPCv8+bzNTc1D6s2hjjRSKyWlULPrO8J0EgIucS6uvi86raJCKDVbVURKYDJapaLCKTgVdV9TP9ZDtBcFhV7zmRz+0vQVBZ18xXHlrBtpLD3H31FK6cfvxdgbe0BXnho/3896tbKD/cxHWn53HHJeNJjo0KY8XGGC/rLgh6etXQzcBdqtoEoKqlzvNHqtreHeoGIFZEIupMaXNrkG89Vsj20sMs+uqpJxQCAFF+H9cU5PLW7edww6wRPLVyL5fc+z4rd3UenMgYY8Krp0EwFjhbRFaIyLsicloX61wFfNQeFl24VUTWOoM2dzvwtIjcJCKFIlJYVlbW3Wp95md/2cCq3Ye450tTmTvu5AdFSoqN4qeXT+TZm2fh9wnzFy3jnle30NoWPPbGxhjTC44ZBCLyhois7+Ixj9DJ5kGEBkr/IfCMdDjzKSKTgF8A3+rm7e8HRhHqUvcA8Mvu6lDVRapaoKoFmZmfuTGuT721uYQ/rdjLt+aM5PKpw3rlPWfkDeLl75zNVTNy+L+3t3P9H1ZSWdfcK+9tjDFHc8wgUNULVHVyF4/FhPrLfl5DVhIaMzYDjgwo/gLwVVXd0c17l6hqm6oGgd9zkgMv96XaxhZ+8sJ6xg5J5PbPjTv2BicgMSbAf39pKndfPYVVuw9x+f9+wPr91b36GcYY01lPDw29CJwHICJjCV0lVC4iqcBfgTtV9e/dbdw+/JrjC4QGnOjXfv3GNg7WNHLXVVOIDoTnxuxrCnJ5duGZqCpX3b+UJR9H3Iiaxph+pKffZA8DI0VkPaHxYq/X0GVItwKjgX8RkTXOYzCAiDwoIu1nre8WkXUishY4F/heD+sJq+KqBv64fA9XzchhRl63pzN6xZScVJbcdhZTc1L59pMf8du3t7ePSGSMMb1qQI5Q5tbloz9+di0vfLSft384l+yj3CvQm5pa2/jRs2tZvKaYawty+fkXJhPlty6ijDEnrrvLR+3O4uNUdKieZz8s4iszh/dZCADEBPzce+00hqfF85u3tlNc3cBvvzzD7jcwxvQa+9PyOP1xWaj31pvmjOzzzxYRvv+5cdx99RSW7ajgmgeWUdpFX0bGGHMyLAiOQ11TK0+u3Mulp2QdtfuIcLumIJc/3HAaeyvrufqBZeypqHOtFmNM5LAgOA7PfVhEbWMrX5+d73YpnD0mkye+OZPaxhauun8ZG4rt8lJjTM9YEByHp1ftY3J2MtPDfKXQ8ZqWm8qfF55JlF+Y/7vlLN9Z4XZJxpgBzILgGDYfrGFDcQ1Xz8hxu5RPGT04iedunsXg5Bi++vBKXttw0O2SjDEDlAXBMbzw4X4CPum1riR607DUOP68cBYTspJZ+Phqnlm1z+2SjDEDkAXBUbQFlRc+2s/ccYP77TCTaQnRPHHjGcwencGPnlvL/e/ssBvPjDEnxILgKAp3V1Ja28S8af1vb6CjhJgAD11/GpdNyeIXr2zm/728iWDQwsAYc3zshrKjeH1jCdF+H+eOP/lupvtKdMDHr+dPJy0hmt+/v4vKuhbuuuoUuwvZGHNMFgTdUFVe21jC7NHpJMYMjH8mv0/42RWTSEuI5t43tlFV38z//cMM4qL9bpdmjOnH7M/FbmwpqWVvZT2fmzTU7VJOiIjw3QvG8h/zJvHWllK+8tAKqutb3C7LGNOPWRB047UNJYjA+RP6/2GhrnzlzHz+97rpfFxUxbWLllFiXVIYY7phQdCNd7eWMSU7hcFJsW6XctIumzKMP3ztdPZW1nPV/UvZVW5dUhhjPsuCoAu1jS2s2VfFWWMy3C6lx84ak8GT35xJfXMbV9+/1EY8M8Z8hgVBF1bsrKQtqJw12t2xkXvL1NxUnvnWmcRG+Zm/aDlLd5S7XZIxph+xIOjCB9vLiY3yMWN4qtul9JrRgxN59uYzyUqJ5WsPr+KV9QfcLskY009YEHTh79vLOX1EOjGByLrsMisljj8vPJPJ2cn8458+5E8r9rhdkjGmH7Ag6KS0ppFtpYc5a3S626WERWp8NI/feAbnjM3kJy+s57/+ZnchG+N1PQ4CEblNRLaIyAYRudtZli8iDR0Grn+gm23TROR1EdnmPLvez/Oq3YcAOGNEZAYBQHx0gN9/tYAvn5HH797dyW1PfkRjS5vbZRljXNKjW2ZF5FxgHjBFVZtEpONF9ztUddox3uIO4E1VvUtE7nDmf9yTmnqqcE8lsVE+Jg5LdrOMsAv4ffz8ysnkpyfw//62iQPVDfz+qwX9tnM9Y0z49HSP4GbgLlVtAlDV0hPcfh7wqDP9KHBlD+vpsdV7DjE1J9UTffSICN+cM5L7/mEGG4pr+MJ9S9lRdtjtsowxfayn33ZjgbNFZIWIvCsip3V4bYSIfOQsP7ub7Yeo6gEA57nb23hF5CYRKRSRwrKysh6W3bX65lY2FNdQkO/6Eao+dckpWTx500zqmlr54n1LWWEjnhnjKccMAhF5Q0TWd/GYR+jQ0iBgJvBD4BkREeAAkKeq04HvA0+ISI+OtajqIlUtUNWCzMzwXN+/Zl8VbUGlYHhaWN6/P5uRN4gXb5lNRmI0Cx5awVMr97pdkjGmjxwzCFT1AlWd3MVjMVAEPK8hK4EgkKGqTapa4Wy/GthBaO+hsxIRyQJwnk/00FKv+nBP6ETx9LzIuX/gROSmxfP8zbM5c1QGdzy/jp8uXk9LW9DtsowxYdbTQ0MvAucBiMhYIBooF5FMEfE7y0cCY4CdXWy/BLjemb4eWNzDenrkw71VjB6cSGp8tJtluColPoo/fO00vjVnJH9ctocFD66g4nCT22UZY8Kop0HwMDBSRNYDTwHXa2icxDnAWhH5GHgWWKiqlQAi8qCIFDjb3wVcKCLbgAudedes21/NlOwUN0voF/w+4c5LJ3DvtdNYs6+KK/7v72wotj6KjIlUPbp8VFWbgQVdLH8OeK6bbW7sMF0BnN+TGnpLSU0jZbVNTLYgOOLK6dmMzEzgW4+t5qr7l/LfV0/l8qn9e9hOY8yJi/xrJI/TuqLQX7yn5FgQdDQlJ5Ult57F5GEp3PbkR/zr4vU0tdrNZ8ZEEgsCx7r91YjAxKzIvpHsZGQmxfDEN2dy41kjeHTZHq55YBn7KuvdLssY00ssCBwbiqsZlZlIwgAZn7ivRQd8/PNlE3lgwansLK/j8795n9c3lrhdljGmF1gQONbtr+YUOz9wTBdPHspLt51FXno83/xjIf/18ia7xNSYAc6CACitbaSkpolJEd6/UG8Znp7AswtnsWBmHr97bydX37+UndY1hTEDlgUBsOlALQCThtkewfGKjfLz8ytP4b4vz2B3RT2f/80HPLFiL6Grh40xA4kFAbD1YCgIxg1NcrmSgefSU7J49btzmDE8lX96YR3f/ONquwHNmAHGggDYfLCWzKQY0hK8e0dxTwxNieWxr5/BP39+Au9tLeOie9/n7c2u9hZijDkBFgTA1pJaxg2xvYGe8PmEG88eyeJbZ5OeEM0Nj6zi9mc+pqq+2e3SjDHH4PkgaAsq20pr7bBQL5mQlcyS22Zz67mjeXHNfi741Xu8sv6g22UZY47C80Gwt7Kexpag7RH0opiAnx9cNI7Ft8xmcFIMCx9fzS1/+pCyWjt3YEx/5Pkg2OKcKB5rewS9bnJ2Cotvnc0PLxrH6xtLuPB/3uXpVXsJBu3KImP6E88HwdaSUBCMGZzociWRKcrv45ZzR/PXb5/F6MxEfvzcOq5+YCnr91tvpsb0F54Pgi0lteSmxVnXEmE2ZkgSz3zrTO750lT2VNRzxf99wL8uXk91Q4vbpRnjeZ4Pgh2lhxkz2A4L9QWfT7j61Bze+sFcFswczmPL93D+L9/hz4X77HCRMS7ydBAEg8qu8jpGZCS4XYqnpMRF8e/zJrPk1rPITYvnh8+u5bL//YCl28vdLs0YT/J0EBRXN9DUGmRkpgWBGyZnp/Dcwln8ev40qhta+IcHV/CNR1axvbTW7dKM8RRPB8Gu8joARmbYiWK3+HzCvGnZvHn7OdxxyXhW7qrkonvf5ycvrKO0ptHt8ozxBE8Hwc6yUBCMsj0C18VG+Vl4zije+eFcFpyRx9Or9nH23W/z85c2Um59FxkTVh4PgsMkRPvJTIpxuxTjSE+M4WfzJvPm7efw+SlZPPz3Xcy5+21+8cpmDtVZdxXGhEOPg0BEbhORLSKyQUTudpZ9WUTWdHgERWRaF9v+m4js77DepT2t50TsLK9jZGYiItKXH2uOw/D0BH51zTRe+945XDBhCA+8u4Oz736be17dYr2bGtPLenTxvIicC8wDpqhqk4gMBlDVPwF/ctY5BVisqmu6eZv/UdV7elLHydpZVkdB/iA3Ptocp9GDE/nNddO55dzR3PvGVn77znYe/GAn1xbkcuPZI8lNi3e7RGMGvJ7eRXUzcJeqNgGoald9D18HPNnDz+l1jS1tFFc3MDIj1+1SzHEYNzSJ+xecyvbSwyx6bwdPrNzL4yv2csXUYSw8Z5R1GmhMD/T00NBY4GwRWSEi74rIaV2scy1HD4JbRWStiDwsIt3+eS4iN4lIoYgUlpWV9bBs2F1RhyqMsBPFA8rowYncffVU3vvRudwwK59XNxzkonvf46sPr+StzSV2Y5oxJ+GYQSAib4jI+i4e8wjtUQwCZgI/BJ6RDgfcReQMoF5V13fz9vcDo4BpwAHgl93VoaqLVLVAVQsyMzOPu4Hd2VNRD0B+uh1aGIiyUuL458smsvSO87j9wrFsPlDD1x8p5LxfvsNDH+yiptG6rjDmeB3z0JCqXtDdayJyM/C8hgaqXSkiQSADaP+TfT5H2RtQ1ZIO7/V74KXjrLvH9lWGgmB4mu0RDGSp8dHcdv4YFs4dxSvrD/LI0t38x0sb+eVrW/jijGy+MjPfDhsZcww9PUfwInAe8I6IjAWigXIAEfEBXwLmdLexiGSp6gFn9gtAd3sOvW5vZT3JsQFS4qP66mR6lIUAAAyaSURBVCNNGEX5fVw+dRiXTx3G+v3VPLJ0N88UFvH48r1MzU3l2oJcLp+aRVKs/byN6ayn5wgeBkaKyHrgKeB6Z+8AQgFQpKo7O24gIg+KSIEze7eIrBORtcC5wPd6WM9x21tZT54dFopIk7NTuOdLU1l+5/n8y2UTaWxu459eWMfp//kmtz/zMSt3VfLJf1NjjAzEX4iCggItLCzs0Xuc98t3GD80ifu+fGovVWX6K1Xl46Jqnl61j798XMzhplaGp8dzxdRhzJs2jNHW+6zxCBFZraoFnZd7shP+YFApqmzgwolD3C7F9AERYVpuKtNyU/mXyybw8rqDvPjRfn779nb+963tTMxK5oppocNK2alxbpdrTJ/zZBCU1DbS3BYkd5AdGvKa+OgAV5+aw9Wn5lBa28hf1x5gycfF3PW3zdz1t82clj+IiyYN5aJJQ+1mNeMZngyCvc6lo3n2i+5pg5NiuWH2CG6YPYK9FfX8ZW0xf/m4mJ//dRM//+smJmQl87mJQ7ho0lAmZCVZVyQmYnkzCCotCMyn5aXHc8u5o7nl3NHsrajntY0HeXXDQX7z1jZ+/eY2ctPiOH/8EOaOy2TmyHRio/xul2xMr/FkEOyrrMcnMMyOB5su5KXHc+PZI7nx7JGU1Tbx5qYSXt1wkCdW7uWRpbuJCfg4Y2Q6c8dmcs64TEZmJNjeghnQPBkEeyvryUqJIzrg6V64zXHITIph/ul5zD89j4bmNlbsquDdrWW8u7WMf39pI7wEOYPiOHtMBjNHpnPmyHQGJ8e6XbYxJ8STQbDvUAO5abY3YE5MXLSfueMGM3fcYCC0Z/nu1jLe2VLGSx8f4MmV+wAYmZnAzJHpoceINAsG0+95MgiKqxo4c1S622WYAS43LZ4FM4ezYOZw2oLKxuIalu0sZ/nOSpasKeaJFXsBGJmRwIzhg5iRN4gZw1MZMzgJv88OJZn+w3NB0NoWpKSm0a4XN73K7xNOyUnhlJwUbpozita2IBsP1LB8ZwUrdlby1uZSnl1dBEBiTICpuSnMyBvE9LxUpucOYlBCtMstMF7muSAoqW0iqKHeK40Jl4Dfx5ScVKbkpHLTnFGoKnsq6vlw7yE+2lvFh3sPcd87O2hzus3OTo1j0rBkJmenMDk7mcnDUuyQkukznguC4qoGAIal2i+Z6TsiQn5GAvkZCXxxRg4A9c2trC2qZs2+KjYU17BhfzWvbTzSIS8ZiTFMzk5m0rBkxg5JYtzQJEZkJBATsEtXTe/ycBDYHoFxV3x04MhJ5XaHm1rZdKCG9furWb+/hg3F1by/rfzInoPfJ+SnxzN2SFKHRyL5GQlE+e0qOHNyPBcEB6obAchKsT0C0/8kxgQ4LT+N0/LTjixram1jZ1kdW0tq2VZymK0ltWw6UMMrGw7S3mdkwCfkpsWTnx5PfkYCIzISyE8PPQ9LjbOT0+aoPBcExVUNJMUGrF96M2DEBPxMyEpmQlbyp5Y3trSxvfQw20pDAbG7oo5d5fUs31lJQ0vbkfWi/T7y0uPJT08gPz2e7EFx5AyKJzs1jpy0OJLtd8HzPBgEdsWQiQyxUX7n5HLKp5arKqW1Tewqr2NXeR27258r6vhgexmNLcFPrZ8UG/gkGAaFHtmpcQxJiWVociyZSTF22CnCeTAIGuywkIloIsKQ5FiGJMd+6vwDhEKioq6Z/YcaKDrUwP6q+iPTRYfqWb6zgsNNrZ3eD9ITYhiSHHPkfdunhybHMjg5hszEGAYlRFtgDFCeC4ID1Q1Mz0t1uwxjXCEiZCTGkJEYw9Tcz/4eqCo1Da0UVdVTWtPEwZpGSmoaKalpcp4bWVtURfnh5i7fPzU+irSEaDISYkhPjCY9MZq0hBgyEqNJb1+WEE1aQjQpcVEELDj6BU8FQUNzG4fqW+yKIWO6ISKkxEeREp/CpGHdr9fSFqSs1gmK6kbK65qpPNxMRV0TFc7z9tLDrNjVzKH6ZrobCDExJkBKXNSRR2r8J9MpznRqXPSR15Jjo0iI8ZMYG7DLaHuRp4KguNruITCmN0T5fQxLjTuuP6ragsqh+uYjAVFxuJnKumaqG1qoqm+huqGF6obQ/PbSw6HlDS00twaP+r7Rfh+JsQESYzo8Yj95TuqwLCEmNB8X7Scuyk98dIC4aB+x7dNRfmKjfJ7tRbZHQSAiTwPjnNlUoEpVpzmv3Ql8A2gDvq2qr3axfRrwNJAP7AauUdVDPanpaNrvIbC7io3pO37fJ4ej4PjGh1ZVGluCTig0U10fCofaxlYON7ZQ19wWmm5q4XBjK4ebQo/S2kZ2ln0y3/nE+LGEQsLvBIT/SHDERX+yPC7KT0zAT3TAR0zAR0yUj2i/j5gof2j+yKPDOkdZP+AT1wOoR0Ggqte2T4vIL4FqZ3oiMB+YBAwD3hCRsara1ukt7gDeVNW7ROQOZ/7HPanpaA5Uhe4hsKuGjOnfRCT0JRztZ2gPLu5oaQtS19RKbWMrdc2t1De30djcRn1zGw0tbTQ4z/XNbTQ0t34y3em1yrpmig6FljW2tNHcGqSpNUhz24kFTVd8EtrDivb7iAr4iPLLJ/N+H1GB0Hz7sh9cNI5pXZzf6YleOTQkoTi7BjjPWTQPeEpVm4BdIrIdOB1Y1mnTecBcZ/pR4B3CGAT7qxoQgSHWh4sxnhDl95EaH01qfHg69QsGlea2UCg0tX4SEE0toZBoamkLBUZrF+s4040tQVqCQVpalZa2IC1toW1b2pSW1iCtwSDNznRjSxva3QmXHuitcwRnAyWqus2ZzwaWd3i9yFnW2RBVPQCgqgdEZHB3HyAiNwE3AeTl5Z1UkQerG8lIjLEBaYwxvcLnE2J9fmfo0oF7Y94xg0BE3gCGdvHST1R1sTN9HfBkx826WL9HMaaqi4BFAAUFBSf1Xv/1xVOobmjpSRnGGBNxjhkEqnrB0V4XkQDwReDUDouLgNwO8zlAcRebl4hIlrM3kAWUHrvkk+fzifX7bowxnfTGMZILgM2qWtRh2RJgvojEiMgIYAywsottlwDXO9PXA4u7WMcYY0wY9UYQzOfTh4VQ1Q3AM8BG4BXglvYrhkTkQREpcFa9C7hQRLYBFzrzxhhj+pCE4wx0uIlIGbDnJDfPAMp7sZyBwNrsDdZmb+hJm4erambnhQMyCHpCRApVteDYa0YOa7M3WJu9IRxttusojTHG4ywIjDHG47wYBIvcLsAF1mZvsDZ7Q6+32XPnCIwxxnyaF/cIjDHGdGBBYIwxHuepIBCRi0Vki4hsd7q9jigikisib4vIJhHZICLfcZanicjrIrLNeR7kdq29TUT8IvKRiLzkzEd0m0UkVUSeFZHNzs/7TA+0+XvO/+v1IvKkiMRGWptF5GERKRWR9R2WddtGEbnT+T7bIiIXnezneiYIRMQP/Ba4BJgIXOeMmxBJWoHbVXUCMBO4xWlj+7gPY4A3nflI8x1gU4f5SG/zr4FXVHU8MJVQ2yO2zSKSDXwbKFDVyYCfUK8GkdbmR4CLOy3rso2dxn25GLjP+Z47YZ4JAkLjIWxX1Z2q2gw8RWg8hIihqgdU9UNnupbQl0M2oXY+6qz2KHClOxWGh4jkAJ8HHuywOGLbLCLJwBzgIQBVbVbVKiK4zY4AEOd0dBlPqCPLiGqzqr4HVHZa3F0bj4z7oqq7gPZxX06Yl4IgG9jXYb67MRIigojkA9OBFXQa9wHodtyHAepe4EdAx+GiIrnNI4Ey4A/O4bAHRSSBCG6zqu4H7gH2AgeAalV9jQhucwfdtbHXvtO8FAS9PkZCfyUiicBzwHdVtcbtesJJRC4DSlV1tdu19KEAMAO4X1WnA3UM/EMiR+UcF58HjCA0/G2CiCxwtyrX9dp3mpeC4HjHSBjQRCSKUAj8SVWfdxaXOOM90BfjPvSx2cAVIrKb0OG+80TkcSK7zUVAkaqucOafJRQMkdzmC4Bdqlqmqi3A88AsIrvN7bprY699p3kpCFYBY0RkhIhEEzrJssTlmnqVM3b0Q8AmVf1Vh5cidtwHVb1TVXNUNZ/Qz/QtVV1AZLf5ILBPRMY5i84n1OV7xLaZ0CGhmSIS7/w/P5/QObBIbnO77tp4vOO+HJuqeuYBXApsBXYQGmrT9Zp6uX1nEdo1XAuscR6XAumErjbY5jynuV1rmNo/F3jJmY7oNgPTgELnZ/0iMMgDbf4ZsBlYDzwGxERamwmN7XIAaCH0F/83jtZG4CfO99kW4JKT/VzrYsIYYzzOS4eGjDHGdMGCwBhjPM6CwBhjPM6CwBhjPM6CwBhjPM6CwBhjPM6CwBhjPO7/A5uJs8giB+oAAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 1)\n", + "ax[0].plot(monitor.t/ms, monitor.I_syn[0]/nA)\n", + "ax[1].plot(monitor.t/ms, monitor.Vm[0]/mV);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's leave aside our neural model for a moment and look at other ways to generate spikes, useful to serve as input to a network of neurons. One useful way of generating inputs is the `PoissonGroup`, which generates spikes generated by a Poisson process. You can provide individual firing rates for each neuron, or a single firing rate for all neurons:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "start_scope()\n", + "input_spikes = PoissonGroup(10, rates=100*Hz)\n", + "input_monitor = SpikeMonitor(input_spikes)\n", + "run(100*ms)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(input_monitor.t/ms, input_monitor.i, 'o');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All neurons fire with the same rate (the total number of spikes recorded over a long time will be the same), but each firing pattern is stochastic and independent of the other neurons. We are now going to connect this source of spikes to our neuron model defined previously. We do this using the `Synapses` class which in addition to the source and target groups, requires us to specify the action that the arrival of a presynaptic spike (hence: `on_pre`) causes. Creating such a `Synapses` object will not yet create synapses (there are many ways in which you could connect two groups), we will have to call the `connect` function to do this. Calling `connect()` without any arguments means: \"connect everything in the source group to everything in the target group\" (i.e., \"all-to-all\"):" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "start_scope()\n", + "input_spikes = PoissonGroup(10, rates=100*Hz)\n", + "input_monitor = SpikeMonitor(input_spikes)\n", + "Cm = 200*pF\n", + "G_leak = 10*nS\n", + "E_leak = -70*mV\n", + "tau_syn = 5*ms\n", + "eqs = '''dVm/dt = (-G_leak*(Vm - E_leak) + I_syn)/Cm : volt\n", + " dI_syn/dt = -I_syn/tau_syn : amp'''\n", + "neurons = NeuronGroup(1, eqs, threshold='Vm>-55*mV',\n", + " reset='Vm = E_leak', method='exact')\n", + "neurons.Vm = E_leak\n", + "\n", + "synapses = Synapses(input_spikes, neurons, '',\n", + " on_pre='I_syn += 0.05*nA')\n", + "synapses.connect()\n", + "\n", + "monitor = StateMonitor(neurons, ['Vm', 'I_syn'], record=True)\n", + "spike_monitor = SpikeMonitor(neurons)\n", + "run(100*ms)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 1)\n", + "ax[0].plot(monitor.t/ms, monitor.I_syn[0]/nA)\n", + "ax[1].plot(monitor.t/ms, monitor.Vm[0]/mV);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The current plotted in the top row jumps up by a small amount each time a spike from one of the 10 Poisson neurons arrives. The effect of these spikes is the same (a jump of 0.05nA), regardless of which Poisson neuron emitted the spike. This is not the most interesting situation – in fact, 10 Poisson neurons spiking at 100Hz are mathematically identical to a single Poisson neuron spiking at 1000Hz. Synapses in the brain can greatly vary in efficacy, and the pattern of synaptic connections and strengths is what is widely believed to be the major way that the brain stores memories. We are far from addressing questions like this here, but as a tiny first step, let us make the synaptic strength part of the synaptic model (which was previously empty, i.e. the empty string `''`) and set it to values between 0 and 0.1nA:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "start_scope()\n", + "input_spikes = PoissonGroup(10, rates=100*Hz)\n", + "input_monitor = SpikeMonitor(input_spikes)\n", + "Cm = 200*pF\n", + "G_leak = 10*nS\n", + "E_leak = -70*mV\n", + "tau_syn = 5*ms\n", + "eqs = '''dVm/dt = (-G_leak*(Vm - E_leak) + I_syn)/Cm : volt\n", + " dI_syn/dt = -I_syn/tau_syn : amp'''\n", + "neurons = NeuronGroup(1, eqs, threshold='Vm>-55*mV',\n", + " reset='Vm = E_leak', method='exact')\n", + "neurons.Vm = E_leak\n", + "\n", + "synapses = Synapses(input_spikes, neurons, 'w : amp',\n", + " on_pre='I_syn += w')\n", + "synapses.connect()\n", + "synapses.w = np.linspace(0, 0.1, 10)*nA\n", + "\n", + "monitor = StateMonitor(neurons, ['Vm', 'I_syn'], record=True)\n", + "spike_monitor = SpikeMonitor(neurons)\n", + "run(100*ms)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 1)\n", + "ax[0].plot(monitor.t/ms, monitor.I_syn[0]/nA)\n", + "ax[1].plot(monitor.t/ms, monitor.Vm[0]/mV);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you look closely at the synaptic current in the top row, you'll now notice that some synaptic events are very small, while others are relatively big." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a somewhat \"realistic\" input (random spike events instead of a constant current injection), we can do a similar analysis to the f/I curve earlier. We change our model to have 100 Poisson neurons and 100 integrate-and-fire neurons, connected in a one-to-one pattern. We then vary the firing rate of the Poisson neurons systematically, so that we can see how the \"output\" firing rate of a neuron depends on its \"input\" firing rate (but note that the synaptic strength of course matters as well)." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "start_scope()\n", + "input_spikes = PoissonGroup(100, rates=np.linspace(0, 100, 100)*Hz)\n", + "input_monitor = SpikeMonitor(input_spikes)\n", + "Cm = 200*pF\n", + "G_leak = 10*nS\n", + "E_leak = -70*mV\n", + "tau_syn = 5*ms\n", + "eqs = '''dVm/dt = (-G_leak*(Vm - E_leak) + I_syn)/Cm : volt\n", + " dI_syn/dt = -I_syn/tau_syn : amp'''\n", + "neurons = NeuronGroup(100, eqs, threshold='Vm>-55*mV',\n", + " reset='Vm = E_leak', method='exact')\n", + "neurons.Vm = E_leak\n", + "\n", + "synapses = Synapses(input_spikes, neurons, 'w : amp',\n", + " on_pre='I_syn += w')\n", + "synapses.connect(j='i') # one-to-one connection pattern\n", + "synapses.w = 0.5*nA\n", + "\n", + "monitor = StateMonitor(neurons, ['Vm', 'I_syn'], record=True)\n", + "spike_monitor = SpikeMonitor(neurons)\n", + "run(1000*ms)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(spike_monitor.t/ms, spike_monitor.i, '.');" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(input_spikes.rates, spike_monitor.count, '.');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This looks similar to the f/I curve we created earlier (there's a minimal value needed to elicit spikes, output firing rate grows approximately proportionally with input firing rate), but also important differences. Note that the curve looks much noiser than the previous curve, this is mostly because we are using the \"theoretical\" firing rate on the x axis: a Poisson neuron with a requested firing rate of 50Hz might well elicit only 48 spikes during our simulation, while a Poisson neuron with a requested firing rate of 49Hz might elicit 51 spikes. Running the simulation for longer, averaging over multiple runs, or simply using the actual number of elicited spikes on the x axis would all make the curve smoother." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python3", + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}