{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Galves-Löcherbach Model in NESTML\n",
    "\n",
    "_In vivo_, neurons display variability in their spiking behavior: continuous fluctuations of the membrane potential can be observed, and they exhibit spontaneous firing and different responses to the same repeated input stimulus.\n",
    "\n",
    "To capture these phenomena, the Galves-Löcherbach (GL) model assumes that the firing of the neuron is a random event, whose probability of occurrence in any time step is a firing function $Φ(V_\\text{m})$ of membrane potential $V_\\text{m}$. By subsuming all sources of randomness into a single function, the GL neuron model simplifies the analysis and simulation of noisy spiking neural networks [1-3]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preliminaries\n",
    "-------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "              -- N E S T --\n",
      "  Copyright (C) 2004 The NEST Initiative\n",
      "\n",
      " Version: 3.9.0-post0.dev0\n",
      " Built: Oct 10 2025 14:26:32\n",
      "\n",
      " This program is provided AS IS and comes with\n",
      " NO WARRANTY. See the file LICENSE for details.\n",
      "\n",
      " Problems or suggestions?\n",
      "   Visit https://www.nest-simulator.org\n",
      "\n",
      " Type 'nest.help()' to find out more about NEST.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from typing import Dict, Optional\n",
    "\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rcParams['axes.grid'] = True\n",
    "mpl.rcParams['grid.color'] = 'k'\n",
    "mpl.rcParams['grid.linestyle'] = ':'\n",
    "mpl.rcParams['grid.linewidth'] = 0.5\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import nest\n",
    "import numpy as np\n",
    "\n",
    "from pynestml.codegeneration.nest_code_generator_utils import NESTCodeGeneratorUtils\n",
    "from pynestml.codegeneration.nest_tools import NESTTools\n",
    "\n",
    "\n",
    "import logging\n",
    "logging.getLogger('matplotlib.font_manager').disabled = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GL model with biophysical units\n",
    "-------------------------------\n",
    "\n",
    "We formulate the GL model as a linear integrate-and-fire model with membrane time constant $\\tau_\\text{m}$, resting potential $V_\\text{r}$ and an instantaneous jump in the postsynaptic membrane potential upon receiving a spike (Dirac delta function postsynaptic kernel)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Neuron parameters\n",
    "params = {\n",
    "    \"tau_m\"   : 10.0,\n",
    "    \"t_ref\"   : 2.0,\n",
    "    \"C_m\"     : 250.0,\n",
    "    \"V_r\"     : -65.0,\n",
    "    \"V_reset\" : -65.0,\n",
    "    \"a\"       : 1.2,\n",
    "    \"b\"       : 27.0,\n",
    "    \"V_b\"     : -51.3\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "nestml_gl_exp_model = \"\"\"\n",
    "model gl_exp_neuron:\n",
    "    state:\n",
    "        refr_spikes_buffer mV = 0 mV\n",
    "        refr_tick integer = 0    # Counts number of tick during the refractory period\n",
    "        V_m mV = V_r     # Membrane potential\n",
    "\n",
    "    equations:\n",
    "        kernel G = delta(t)\n",
    "        V_m' = -(V_m - V_r) / tau_m + (mV / ms) * convolve(G, spikes) + (I_e + I_stim) / C_m\n",
    "\n",
    "    parameters:\n",
    "        tau_m ms = 10 ms                  # Membrane time constant\n",
    "        C_m pF = 250 pF                   # Capacitance of the membrane\n",
    "        t_ref ms = 2 ms                   # Duration of refractory period\n",
    "        tau_syn ms = 0.5 ms               # Time constant of synaptic current\n",
    "        V_r mV = -65 mV                   # Resting membrane potential\n",
    "        V_reset mV = -65 mV               # Reset potential of the membrane\n",
    "        b real = 27                       # Parameter for the exponential curve\n",
    "        a mV = 1.2 mV                     # Parameter for the exponential curve\n",
    "        V_b mV = -51.3 mV                 # Membrane potential at which Phi(V)=1/b\n",
    "        I_e pA = 0 pA                     # Constant external input current\n",
    "        reset_after_spike boolean = true  # Whether to reset membrane potential after a spike is emitted\n",
    "\n",
    "    internals:\n",
    "        RefractoryCounts integer = steps(t_ref) # refractory time in steps\n",
    "\n",
    "    input:\n",
    "        spikes <- spike\n",
    "        I_stim pA <- continuous\n",
    "\n",
    "    output:\n",
    "        spike\n",
    "\n",
    "    function Phi(V_m mV) real:\n",
    "        return (1 / b) * exp((V_m - V_b) / a)\n",
    "\n",
    "    update:\n",
    "        if refr_tick == 0:\n",
    "            # neuron is not refractory\n",
    "            integrate_odes()\n",
    "        else:\n",
    "            # neuron is absolute refractory\n",
    "            refr_tick -= 1\n",
    "            \n",
    "        if random_uniform(0, 1) <= 1E-3 * resolution() * Phi(V_m):    # 1E-3 to convert ms to s\n",
    "            # fire a spike!\n",
    "            refr_tick = RefractoryCounts\n",
    "            if reset_after_spike:\n",
    "                V_m = V_reset\n",
    "\n",
    "            emit_spike()\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "              -- N E S T --\n",
      "  Copyright (C) 2004 The NEST Initiative\n",
      "\n",
      " Version: 3.9.0-post0.dev0\n",
      " Built: Oct 10 2025 14:26:32\n",
      "\n",
      " This program is provided AS IS and comes with\n",
      " NO WARRANTY. See the file LICENSE for details.\n",
      "\n",
      " Problems or suggestions?\n",
      "   Visit https://www.nest-simulator.org\n",
      "\n",
      " Type 'nest.help()' to find out more about NEST.\n",
      "\n",
      "\n",
      "Feb 26 17:38:57 Install [Info]: \n",
      "    loaded module nestml_f5ac9d5fccad41dea9f58ffd89f5fcb9_module\n"
     ]
    }
   ],
   "source": [
    "module_name, neuron_model_name = NESTCodeGeneratorUtils.generate_code_for(\n",
    "    nestml_neuron_model=nestml_gl_exp_model,\n",
    "    logging_level=\"ERROR\"  # try \"DEBUG\" for more debug information\n",
    ")\n",
    "\n",
    "nest.Install(module_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Firing rate\n",
    "\n",
    "This should correspond to the $\\Phi(V_\\text{m})$ function in the neuron (see plot below for the theoretical curve). Please note that in the literature, $\\Phi$ is sometimes defined as a probability of firing function with values between 0 and 1, but we here define it as giving the firing rate (in units of s${}^{-1}$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure_numerical_Phi_function(neuron_model_name, module_name, V_min=0., V_max=10., neuron_model_params=None, neuron_membrane_potential_name=\"V_m\"):\n",
    "    nest.ResetKernel()\n",
    "    NESTTools.set_nest_verbosity(\"ERROR\")\n",
    "\n",
    "    nest.print_time = False\n",
    "    nest.Install(module_name)\n",
    "    nest.resolution = 1.   # check that results are independent of resolution...\n",
    "\n",
    "    t_stop = 25000.\n",
    "    \n",
    "    V_range = np.linspace(V_min, V_max, 12)\n",
    "    n_spikes = np.nan * np.ones_like(V_range)\n",
    "    for i, V_m in enumerate(V_range):\n",
    "        neuron = nest.Create(neuron_model_name)\n",
    "        if neuron_model_params:\n",
    "            neuron.set(neuron_model_params)\n",
    "            \n",
    "        neuron.set({neuron_membrane_potential_name: V_m})\n",
    "   \n",
    "        sr = nest.Create(\"spike_recorder\")\n",
    "        nest.Connect(neuron, sr)\n",
    "    \n",
    "        assert neuron.get(neuron_membrane_potential_name) == V_m\n",
    "        nest.Simulate(t_stop)\n",
    "        assert neuron.get(neuron_membrane_potential_name) == V_m\n",
    "    \n",
    "        n_spikes[i] = len(sr.events[\"times\"])\n",
    "    \n",
    "    spike_rate = n_spikes / (t_stop / 1E3)\n",
    "\n",
    "    return V_range, spike_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Firing rate [spikes/s]')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# theoretical Phi vs V_m\n",
    "V_range_theory = np.linspace(-60., -45., 100)\n",
    "Phi_of_V_theory = (1 / params[\"b\"]) * np.exp((V_range_theory - params[\"V_b\"]) / params[\"a\"])\n",
    "\n",
    "# numerical Phi vs V_m\n",
    "V_range_numeric, spike_rate_numeric = measure_numerical_Phi_function(neuron_model_name=neuron_model_name,\n",
    "                                                                     module_name=module_name,\n",
    "                                                                     V_min=-60.,\n",
    "                                                                     V_max=-45.,\n",
    "                                                                     neuron_model_params={\"reset_after_spike\": False,\n",
    "                                                                                          \"a\": params[\"a\"],\n",
    "                                                                                          \"b\": params[\"b\"],\n",
    "                                                                                          \"V_b\": params[\"V_b\"],\n",
    "                                                                                          \"tau_m\": 1E99})\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(V_range_theory, Phi_of_V_theory, label=\"theory\")\n",
    "ax.plot(V_range_numeric, spike_rate_numeric, marker=\"o\", label=\"numeric\")\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"$U$\")\n",
    "ax.set_ylabel(\"Firing rate [spikes/s]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing reliability of the single neuron model\n",
    "\n",
    "Here we plot the spike times for the neurons as a raster plot to study the stochastic behavior of the neuron model. First, we give the neurons a constant input current and then a frozen Poissonian input. Frozen Poissonian means that the spike times are random, but are exactly the same between trials.\n",
    "\n",
    "We observe that with the constant input current, the spike times across trials are highly variable, and the firing rate at the bottom is \"flat\" indicating that the spike times are not reliably reproduced over trials. With the frozen Poissonian input, the corresponding firing rate panel shows several peaks indicating that the spike times tend to be more repeatable across trials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_neuron(neuron_name, module_name, nneurons=1, neuron_parms=None, stimulus_type=\"constant\", poisson_fr=0.0,\n",
    "                    mu=500., sigma=0., t_sim=300., plot=False, rseed=1000, dt=0.1, input_freq=0.0):\n",
    "    \"\"\"\n",
    "    Run a simulation in NEST for the specified neuron. Inject a stepwise\n",
    "    current and plot the membrane potential dynamics and spikes generated.\n",
    "    \"\"\"\n",
    "    nest.ResetKernel()\n",
    "    NESTTools.set_nest_verbosity(\"ERROR\")\n",
    "    nest.print_time = False\n",
    "    nest.Install(module_name)\n",
    "    nest.SetKernelStatus({'rng_seed': rseed,\n",
    "                          'resolution': dt})\n",
    "    neuron = nest.Create(neuron_name, nneurons)\n",
    "    if neuron_parms:\n",
    "        for k, v in neuron_parms.items():\n",
    "            nest.SetStatus(neuron, k, v)\n",
    "    nest.SetStatus(neuron,{'V_m':np.random.uniform(-65.0, -50.0)})\n",
    "\n",
    "    if stimulus_type == \"noise\":\n",
    "        # Create a noise generator\n",
    "        noise = nest.Create(\"noise_generator\")\n",
    "        # Set the parameters of the noise generator\n",
    "        noise_params = {\"mean\": mu, \n",
    "                        \"std\": sigma, \n",
    "                        \"dt\": dt,\n",
    "                        \"frequency\":input_freq}\n",
    "        nest.SetStatus(noise, noise_params)\n",
    "        nest.Connect(noise, neuron)\n",
    "\n",
    "    elif stimulus_type == \"poisson_spikes\":\n",
    "        # Create a Poisson generator device\n",
    "        poisson = nest.Create(\"poisson_generator\", params={\"rate\": poisson_fr})\n",
    "\n",
    "        # Create a parrot neuron\n",
    "        parrot = nest.Create(\"parrot_neuron\")\n",
    "\n",
    "        # Connect the Poisson generator to the parrot neuron\n",
    "        nest.Connect(poisson, parrot)\n",
    "\n",
    "        # Connect the parrot neuron to each neuron\n",
    "        nest.Connect(parrot, neuron)\n",
    "    else:\n",
    "        assert stimulus_type == \"constant\"\n",
    "        nest.SetStatus(neuron, \"I_e\", mu)\n",
    "\n",
    "    multimeter = nest.Create(\"multimeter\")\n",
    "    multimeter.set({\"record_from\": [\"V_m\"],\n",
    "                    \"interval\": dt})\n",
    "    spike_recorder = nest.Create(\"spike_recorder\")\n",
    "    nest.Connect(multimeter, neuron)\n",
    "    nest.Connect(neuron, spike_recorder)\n",
    "\n",
    "    nest.Simulate(t_sim)\n",
    "\n",
    "    dmm = nest.GetStatus(multimeter)[0]\n",
    "    Voltages = dmm[\"events\"][\"V_m\"]\n",
    "    tv = dmm[\"events\"][\"times\"]\n",
    "                        \n",
    "    dSD = nest.GetStatus(spike_recorder, keys=\"events\")[0]\n",
    "    ns = dSD[\"senders\"]\n",
    "    ts = dSD[\"times\"]\n",
    "\n",
    "    _idx = [np.argmin((tv - spike_time)**2) - 1 for spike_time in ts]\n",
    "    V_m_at_spike_times = Voltages[_idx]\n",
    "\n",
    "    if plot:\n",
    "        fig, ax = plt.subplots()\n",
    "        ax.plot(tv, Voltages)\n",
    "        ax.scatter(ts, V_m_at_spike_times)\n",
    "        ax.set_xlabel(\"Time [ms]\")\n",
    "        ax.set_ylabel(\"V_m [mV]\")\n",
    "        ax.grid()\n",
    "\n",
    "    return ts, ns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = 0.1\n",
    "nneurons = 50\n",
    "\n",
    "ts_const_input, ns_const = evaluate_neuron(neuron_model_name,\n",
    "                                     module_name,\n",
    "                                     nneurons=nneurons,\n",
    "                                     neuron_parms=params,\n",
    "                                     stimulus_type=\"constant\",\n",
    "                                     mu=550.,\n",
    "                                     t_sim=500.0,\n",
    "                                     dt=dt,\n",
    "                                     poisson_fr=0.0)\n",
    "\n",
    "ts_poisson_input, ns_noise = evaluate_neuron(neuron_model_name,\n",
    "                                     module_name,\n",
    "                                     nneurons=nneurons,\n",
    "                                     neuron_parms=params,\n",
    "                                     stimulus_type=\"poisson_spikes\",\n",
    "                                     t_sim=500.0,\n",
    "                                     poisson_fr=2000.0)\n",
    "\n",
    "# rate averaging parameters\n",
    "t_start = 0.0\n",
    "t_stop = 500.0\n",
    "t_step = 5.0\n",
    "t_bins = np.arange(t_start, t_stop + t_step, t_step)\n",
    "\n",
    "# Calculate the average rate\n",
    "rate_const_input, _ = np.histogram(ts_const_input, bins=t_bins)\n",
    "rate_const_input = rate_const_input / nneurons / (t_step * 1e-3)      # per trial, per second\n",
    "\n",
    "rate_poisson_input, _ = np.histogram(ts_poisson_input, bins=t_bins)\n",
    "rate_poisson_input = rate_poisson_input / nneurons / (t_step * 1e-3)  # per trial, per second\n",
    "\n",
    "min_rate = min(np.amin(rate_const_input), np.amin(rate_poisson_input))\n",
    "max_rate = max(np.amax(rate_const_input), np.amax(rate_poisson_input))\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "\n",
    "# Raster plots\n",
    "plt.subplot2grid((3,2),(0,0), rowspan=2)\n",
    "plt.plot(ts_const_input, ns_const, \".\")\n",
    "plt.xlim(100, 500)\n",
    "plt.ylabel(\"#Trial\")\n",
    "plt.title(\"Constant input current\")\n",
    "\n",
    "plt.subplot2grid((3,2),(0,1), rowspan=2)\n",
    "plt.plot(ts_poisson_input, ns_noise, \".\")\n",
    "plt.xlim(100, 500)\n",
    "plt.ylabel(\"#Trial\")\n",
    "plt.title(\"Frozen Poisson input\")\n",
    "\n",
    "# Firing rate plots\n",
    "plt.subplot2grid((3,2),(2,0), rowspan=1)\n",
    "plt.plot(t_bins[:-1], rate_const_input)\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"Firing rate [spikes/s]\")\n",
    "plt.xlim(100, 500)\n",
    "plt.ylim(min_rate, max_rate)\n",
    "\n",
    "plt.subplot2grid((3,2),(2,1), rowspan=1)\n",
    "plt.plot(t_bins[:-1], rate_poisson_input)\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"Firing rate [spikes/s]\")\n",
    "plt.xlim(100, 500)\n",
    "plt.ylim(min_rate, max_rate)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acknowledgements\n",
    "\n",
    "The authors would like to thank Renan Shimoura, Christophe Pouzat, Antonio Roque and Antonio Galves for their kind and helpful discussion and feedback.\n",
    "\n",
    "\n",
    "## References\n",
    "\n",
    "[1] Brochini, L., de Andrade Costa, A., Abadi, M. et al. Phase transitions and self-organized criticality in networks of stochastic spiking neurons. Sci Rep 6, 35831 (2016). https://doi.org/10.1038/srep35831\n",
    "\n",
    "[2] Lima, V., Pena, R. F. O., Shimoura, R. O., Kamiji, N. L., Ceballos, C. C., Borges, F. S., Higa, G. S. V., de Pasquale, R., & Roque, A. C. (2021). Modeling and characterizing stochastic neurons based on in vitro voltage-dependent spike probability functions. The European Physical Journal Special Topics 2021 230:14, 230(14), 2963–2972. https://doi.org/10.1140/EPJS/S11734-021-00160-7\n",
    "\n",
    "[3] Christophe Pouzat (2020). Spike Train Analysis and Modeling. (Syllabus) Latin-American School on Computational Neuroscience (LASCON) 2020, São Paulo, Brazil.\n",
    "\n",
    "\n",
    "## Copyright\n",
    "\n",
    "This file is part of NEST.\n",
    "\n",
    "Copyright (C) 2004 The NEST Initiative\n",
    "\n",
    "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.\n",
    "\n",
    "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.\n",
    "\n",
    "You should have received a copy of the GNU General Public License along with NEST.  If not, see <http://www.gnu.org/licenses/>.\n"
   ]
  }
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