{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# On-off control\n", "Here we will see how to set up a minimum, working closed loop with a very simple threshold-triggered control scheme.\n", "\n", "Preamble:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import brian2.only as b2\n", "from brian2 import np, ms, Mohm, mA, mV, Hz, nA\n", "import matplotlib.pyplot as plt\n", "import cleo\n", "\n", "cleo.utilities.style_plots_for_docs()\n", "\n", "# the default cython compilation target isn't worth it for\n", "# this trivial example\n", "b2.prefs.codegen.target = \"numpy\"\n", "b2.seed(16320829)\n", "cleo.utilities.set_seed(17041028)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up network\n", "We will use a simple leaky integrate-and-fire network with Poisson spike train input. We use Brian's standard `SpikeMonitor` to view resulting spikes here for simplicity, but see the electrodes tutorial for a more realistic electrode recording scheme." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Recorded population's equations:\n" ] }, { "data": { "text/latex": [ "\\begin{align*}\\frac{\\mathrm{d}v}{\\mathrm{d}t} &= \\frac{I Rm - 70 mV - v}{\\tau} && \\text{(unit of $v$: $\\mathrm{V}$)}\\\\\n", "\\tau &&& \\text{(unit: $\\mathrm{s}$)}\\\\\n", "Rm &&& \\text{(unit: $\\mathrm{ohm}$)}\\\\\n", "I &&& \\text{(unit: $\\mathrm{A}$)}\\end{align*}" ], "text/plain": [ "dv/dt = (I*Rm - 70*mV - v)/tau : volt\n", "tau : second\n", "Rm : ohm\n", "I : amp\n" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n = 10\n", "population = b2.NeuronGroup(\n", " n,\n", " \"\"\"\n", " dv/dt = (-v - 70*mV + Rm*I) / tau : volt\n", " tau: second\n", " Rm: ohm\n", " I: amp\"\"\",\n", " threshold=\"v>-50*mV\",\n", " reset=\"v=-70*mV\",\n", ")\n", "population.tau = 10 * ms\n", "population.Rm = 100 * Mohm\n", "population.I = 0 * mA\n", "population.v = -70 * mV\n", "\n", "input_group = b2.PoissonGroup(n, np.linspace(0, 100, n) * Hz + 10 * Hz)\n", "\n", "S = b2.Synapses(input_group, population, on_pre=\"v+=5*mV\")\n", "S.connect(condition=\"abs(i-j)<=3\")\n", "\n", "pop_mon = b2.SpikeMonitor(population)\n", "\n", "net = b2.Network([population, input_group, S, pop_mon])\n", "\n", "print(\"Recorded population's equations:\")\n", "population.user_equations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run simulation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO No numerical integration method specified for group 'neurongroup', using method 'exact' (took 0.27s). [brian2.stateupdaters.base.method_choice]\n" ] } ], "source": [ "net.run(200 * ms)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Text(0.5, 1.0, 'population spiking'),\n", " Text(0, 0.5, 'neuron index'),\n", " Text(0.5, 0, 'time (ms)')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sptrains = pop_mon.spike_trains()\n", "fig, ax = plt.subplots()\n", "ax.eventplot([t / ms for t in sptrains.values()], lineoffsets=list(sptrains.keys()))\n", "ax.set(title=\"population spiking\", ylabel=\"neuron index\", xlabel=\"time (ms)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because lower neuron indices receive very little input, we see no spikes for neuron 0. Let's change that with closed-loop control." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## IO processor setup\n", "We use the `IOProcessor` class to define interactions with the network.\n", "To achieve our goal of making neuron 0 fire, we'll use a contrived, simplistic setup where\n", "1. the recorder reports the voltage of a given neuron (of index 5 in our case),\n", "2. the controller outputs a pulse whenever that voltage is below a certain threshold, and\n", "3. the stimulator applies that pulse to the specified neuron. \n", "\n", "So if everything is wired correctly, we'll see bursts of activity in just the first neuron." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CLSimulator(io_processor=None, devices={StateVariableSetter(name='stim', save_history=True, value=0, variable_to_ctrl='I', unit=namp, neuron_groups=[]), VoltageRecorder(name='rec', save_history=True, voltage_var_name='v', mon=)})" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "i_rec = int(n / 2)\n", "i_ctrl = 0\n", "sim = cleo.CLSimulator(net)\n", "v_rec = cleo.recorders.VoltageRecorder(name=\"rec\")\n", "sim.inject(v_rec, population[i_rec])\n", "sim.inject(\n", " cleo.stimulators.StateVariableSetter(name=\"stim\", variable_to_ctrl=\"I\", unit=nA),\n", " population[i_ctrl],\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to implement the {class}`~cleo.ioproc.LatencyIOProcessor` object.\n", "Especially important is the {meth}`~cleo.ioproc.LatencyIOProcessor.process` function:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CLSimulator(io_processor=ReactivePulseIOProcessor(sample_period=1. * msecond, sampling='fixed', processing='parallel'), devices={StateVariableSetter(name='stim', save_history=True, value=0, variable_to_ctrl='I', unit=namp, neuron_groups=[]), VoltageRecorder(name='rec', save_history=True, voltage_var_name='v', mon=)})" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class ReactivePulseIOProcessor(cleo.ioproc.LatencyIOProcessor):\n", " def __init__(self, pulse_current=1):\n", " super().__init__(sample_period=1 * ms)\n", " self.pulse_current = pulse_current\n", " self.trigger_threshold = -60 * mV\n", " self.out = {}\n", "\n", " def process(self, state_dict, time_ms):\n", " v = state_dict[\"rec\"]\n", " if v is not None and v < self.trigger_threshold:\n", " self.out[\"stim\"] = self.pulse_current\n", " else:\n", " self.out[\"stim\"] = 0\n", "\n", " return (self.out, time_ms)\n", "\n", "\n", "sim.set_io_processor(ReactivePulseIOProcessor(pulse_current=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And run the simulation:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sim.run(200 * ms)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n", "ax1.plot(pop_mon.t / ms, pop_mon.i, \"|\")\n", "ax1.plot(\n", " pop_mon.t[pop_mon.i == i_ctrl] / ms,\n", " pop_mon.i[pop_mon.i == i_ctrl],\n", " \"|\",\n", " c=\"#C500CC\",\n", ")\n", "ax1.set(title=\"population spiking\", ylabel=\"neuron index\", xlabel=\"time (ms)\")\n", "ax2.fill_between(\n", " v_rec.mon.t / ms,\n", " (v_rec.mon.v.T < sim.io_processor.trigger_threshold)[:, 0],\n", " color=(0.0, 0.72, 1.0),\n", ")\n", "ax2.set(title=\"pulses\", xlabel=\"time (ms)\", ylabel=\"pulse on/off (1/0)\", yticks=[0, 1])\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Yes, we see the IO processor triggering pulses as expected.\n", "And here's a plot of neuron 5's voltage to confirm that those pulses\n", "are indeed where we expect them to be, whenever the voltage is\n", "below -60 mV." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.set(title=f\"Voltage for neuron {i_rec}\", ylabel=\"v (mV)\", xlabel=\"time (ms)\")\n", "ax.plot(v_rec.mon.t / ms, v_rec.mon.v.T / mV)\n", "ax.hlines(-60, 0, 400, color=\"#c500cc\")\n", "ax.legend([\"v\", \"threshold\"], loc=\"upper right\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "In this tutorial we've seen the basics of configuring a {class}`~cleo.ioproc.LatencyIOProcessor` to implement a closed-loop intervention on a Brian network simulation." ] } ], "metadata": { "kernelspec": { "display_name": "python3", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 2 }