{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hodgkin Huxley neuron\n", "\n", "[![Download JupyterNotebook](https://img.shields.io/badge/Download-Notebook-orange?style=for-the-badge&logo=Jupyter)](https://raw.githubusercontent.com/ANNarchy/ANNarchy.github.io/master/notebooks/HodgkinHuxley.ipynb) [![Download JupyterNotebook](https://img.shields.io/badge/Open_in-Colab-blue?style=for-the-badge&logo=Jupyter)](https://colab.research.google.com/github/ANNarchy/ANNarchy.github.io/blob/master/notebooks/HodgkinHuxley.ipynb)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#!pip install ANNarchy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simple Hodgkin-Huxley neuron." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ANNarchy 4.8 (4.8.2) on darwin (posix).\n", "Compiling ... OK \n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import ANNarchy as ann\n", "ann.clear()\n", "\n", "dt=0.01\n", "ann.setup(dt=dt)\n", "\n", "HH = ann.Neuron(\n", "\n", " parameters = \"\"\"\n", " C = 1.0 # Capacitance\n", " VL = -59.387 # Leak voltage\n", " VK = -82.0 # Potassium reversal voltage\n", " VNa = 45.0 # Sodium reveral voltage\n", " gK = 36.0 # Maximal Potassium conductance\n", " gNa = 120.0 # Maximal Sodium conductance\n", " gL = 0.3 # Leak conductance\n", " vt = 30.0 # Threshold for spike emission\n", " I = 0.0 # External current\n", " \"\"\",\n", "\n", " equations = \"\"\"\n", " # Previous membrane potential\n", " prev_V = V\n", "\n", " # Voltage-dependency parameters\n", " an = 0.01 * (V + 60.0) / (1.0 - exp(-0.1* (V + 60.0) ) )\n", " am = 0.1 * (V + 45.0) / (1.0 - exp (- 0.1 * ( V + 45.0 )))\n", " ah = 0.07 * exp(- 0.05 * ( V + 70.0 ))\n", "\n", " bn = 0.125 * exp (- 0.0125 * (V + 70.0))\n", " bm = 4.0 * exp (- (V + 70.0) / 80.0)\n", " bh = 1.0/(1.0 + exp (- 0.1 * ( V + 40.0 )) )\n", "\n", " # Alpha/Beta functions\n", " dn/dt = an * (1.0 - n) - bn * n : init = 0.3, midpoint\n", " dm/dt = am * (1.0 - m) - bm * m : init = 0.0, midpoint\n", " dh/dt = ah * (1.0 - h) - bh * h : init = 0.6, midpoint\n", "\n", " # Membrane equation\n", " C * dV/dt = gL * (VL - V ) + gK * n**4 * (VK - V) + gNa * m**3 * h * (VNa - V) + I : midpoint\n", "\n", " \"\"\",\n", "\n", " spike = \"\"\"\n", " # Spike is emitted when the membrane potential crosses the threshold from below\n", " (V > vt) and (prev_V <= vt) \n", " \"\"\",\n", "\n", " reset = \"\"\"\n", " # Nothing to do, it is built-in...\n", " \"\"\"\n", ")\n", "\n", "pop = ann.Population(neuron=HH, geometry=1)\n", "pop.V = -50.0\n", "\n", "ann.compile()\n", "\n", "m = ann.Monitor(pop, ['spike', 'V', 'n', 'm', 'h'])\n", "\n", "# Preparation\n", "ann.simulate(100.0)\n", "# Current impulse for 1 ms\n", "pop.I = 200.0\n", "ann.simulate(1.0)\n", "# Reset\n", "pop.I = 0.0\n", "ann.simulate(100.0)\n", "\n", "data = m.get()\n", "\n", "tstart = int(90.0/dt)\n", "tstop = int(120.0/dt)\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "plt.figure(figsize=(15, 10))\n", "plt.subplot(2,2,1)\n", "plt.plot(90.0 + dt*np.arange(tstop-tstart), data['V'][tstart:tstop, 0])\n", "plt.title('V')\n", "plt.subplot(2,2,2)\n", "plt.plot(90.0 + dt*np.arange(tstop-tstart), data['n'][tstart:tstop, 0])\n", "plt.title('n')\n", "plt.ylim((0.0, 1.0))\n", "plt.subplot(2,2,3)\n", "plt.plot(90.0 + dt*np.arange(tstop-tstart), data['m'][tstart:tstop, 0])\n", "plt.title('m')\n", "plt.ylim((0.0, 1.0))\n", "plt.subplot(2,2,4)\n", "plt.plot(90.0 + dt*np.arange(tstop-tstart), data['h'][tstart:tstop, 0])\n", "plt.title('h')\n", "plt.ylim((0.0, 1.0))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.13.0" } }, "nbformat": 4, "nbformat_minor": 4 }