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test-copies/notebooks/BrodyHopfield.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Spike synchronization through subthreshold oscillation\n\nThis script reproduces the spike synchronization behavior\nof integrate-and-fire neurons in response to a subthreshold\noscillation. This phenomenon is shown in Fig. 1 of [1]_\n\nNeurons receive a weak 35 Hz oscillation, a gaussian noise current\nand an increasing DC. The time-locking capability is shown to\ndepend on the input current given. The result is then plotted using\nmatplotlib. All parameters are taken from the above paper.\n\n## References\n\n.. [1] Brody CD and Hopfield JJ (2003). Simple networks for\n       spike-timing-based computation, with application to olfactory\n       processing. Neuron 37, 843-852.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, we import all necessary modules for simulation, analysis, and plotting.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport nest\nimport nest.raster_plot"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Second, the simulation parameters are assigned to variables.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "N = 1000  # number of neurons\nbias_begin = 140.0  # minimal value for the bias current injection [pA]\nbias_end = 200.0  # maximal value for the bias current injection [pA]\nT = 600  # simulation time (ms)\n\n# parameters for the alternating-current generator\ndriveparams = {\"amplitude\": 50.0, \"frequency\": 35.0}\n# parameters for the noise generator\nnoiseparams = {\"mean\": 0.0, \"std\": 200.0}\nneuronparams = {\n    \"tau_m\": 20.0,  # membrane time constant\n    \"V_th\": 20.0,  # threshold potential\n    \"E_L\": 10.0,  # membrane resting potential\n    \"t_ref\": 2.0,  # refractory period\n    \"V_reset\": 0.0,  # reset potential\n    \"C_m\": 200.0,  # membrane capacitance\n    \"V_m\": 0.0,\n}  # initial membrane potential"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Third, the nodes are created using ``Create``. We store the returned handles\nin variables for later reference.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "neurons = nest.Create(\"iaf_psc_alpha\", N)\nsr = nest.Create(\"spike_recorder\")\nnoise = nest.Create(\"noise_generator\")\ndrive = nest.Create(\"ac_generator\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Set the parameters specified above for the generators using ``set``.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "drive.set(driveparams)\nnoise.set(noiseparams)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Set the parameters specified above for the neurons. Neurons get an internal\ncurrent. The first neuron additionally receives the current with amplitude\n``bias_begin``, the last neuron with amplitude ``bias_end``.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "neurons.set(neuronparams)\nneurons.I_e = [(n * (bias_end - bias_begin) / N + bias_begin) for n in range(1, len(neurons) + 1)]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Connect alternating current and noise generators as well as\nspike_recorders to neurons\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "nest.Connect(drive, neurons)\nnest.Connect(noise, neurons)\nnest.Connect(neurons, sr)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Simulate the network for time ``T``.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "nest.Simulate(T)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot the raster plot of the neuronal spiking activity.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "nest.raster_plot.from_device(sr, hist=True)\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "EBRAINS-23.09",
+      "language": "python",
+      "name": "ebrains-23.09"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.13"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

test-copies/notebooks/BrodyHopfield.py

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+# -*- coding: utf-8 -*-
+#
+# BrodyHopfield.py
+#
+# This file is part of NEST.
+#
+# Copyright (C) 2004 The NEST Initiative
+#
+# NEST is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# NEST is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+
+
+"""
+Spike synchronization through subthreshold oscillation
+------------------------------------------------------
+
+This script reproduces the spike synchronization behavior
+of integrate-and-fire neurons in response to a subthreshold
+oscillation. This phenomenon is shown in Fig. 1 of [1]_
+
+Neurons receive a weak 35 Hz oscillation, a gaussian noise current
+and an increasing DC. The time-locking capability is shown to
+depend on the input current given. The result is then plotted using
+matplotlib. All parameters are taken from the above paper.
+
+References
+~~~~~~~~~~
+
+.. [1] Brody CD and Hopfield JJ (2003). Simple networks for
+       spike-timing-based computation, with application to olfactory
+       processing. Neuron 37, 843-852.
+
+"""
+
+#################################################################################
+# First, we import all necessary modules for simulation, analysis, and plotting.
+
+import matplotlib.pyplot as plt
+import nest
+import nest.raster_plot
+
+###############################################################################
+# Second, the simulation parameters are assigned to variables.
+
+N = 1000  # number of neurons
+bias_begin = 140.0  # minimal value for the bias current injection [pA]
+bias_end = 200.0  # maximal value for the bias current injection [pA]
+T = 600  # simulation time (ms)
+
+# parameters for the alternating-current generator
+driveparams = {"amplitude": 50.0, "frequency": 35.0}
+# parameters for the noise generator
+noiseparams = {"mean": 0.0, "std": 200.0}
+neuronparams = {
+    "tau_m": 20.0,  # membrane time constant
+    "V_th": 20.0,  # threshold potential
+    "E_L": 10.0,  # membrane resting potential
+    "t_ref": 2.0,  # refractory period
+    "V_reset": 0.0,  # reset potential
+    "C_m": 200.0,  # membrane capacitance
+    "V_m": 0.0,
+}  # initial membrane potential
+
+###############################################################################
+# Third, the nodes are created using ``Create``. We store the returned handles
+# in variables for later reference.
+
+neurons = nest.Create("iaf_psc_alpha", N)
+sr = nest.Create("spike_recorder")
+noise = nest.Create("noise_generator")
+drive = nest.Create("ac_generator")
+
+###############################################################################
+# Set the parameters specified above for the generators using ``set``.
+
+drive.set(driveparams)
+noise.set(noiseparams)
+
+###############################################################################
+# Set the parameters specified above for the neurons. Neurons get an internal
+# current. The first neuron additionally receives the current with amplitude
+# ``bias_begin``, the last neuron with amplitude ``bias_end``.
+
+neurons.set(neuronparams)
+neurons.I_e = [(n * (bias_end - bias_begin) / N + bias_begin) for n in range(1, len(neurons) + 1)]
+
+###############################################################################
+# Connect alternating current and noise generators as well as
+# spike_recorders to neurons
+
+nest.Connect(drive, neurons)
+nest.Connect(noise, neurons)
+nest.Connect(neurons, sr)
+
+###############################################################################
+# Simulate the network for time ``T``.
+
+nest.Simulate(T)
+
+###############################################################################
+# Plot the raster plot of the neuronal spiking activity.
+
+nest.raster_plot.from_device(sr, hist=True)
+plt.show()