Examples_1.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fixed-script",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time, sys, os, subprocess, pickle, shutil, itertools, string, warnings\n",
    "import numpy as np\n",
    "from scipy.stats import pearsonr\n",
    "from cClasses import cClassOne\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "import networkx as nx\n",
    "from sklearn.cluster import SpectralClustering\n",
    "from scipy.sparse import csgraph\n",
    "\n",
    "from sknetwork.clustering import Louvain, modularity\n",
    "from sknetwork.linalg import normalize\n",
    "from sknetwork.utils import bipartite2undirected, membership_matrix\n",
    "\n",
    "ARG = 7402\n",
    "\n",
    "df_fnanme = f'mod_big.txt'\n",
    "T_ms = 14000000\n",
    "stride_ms = 1000\n",
    "dt = 0.01\n",
    "\n",
    "set_evolved_weights = True\n",
    "\n",
    "\n",
    "U = 0.2\n",
    "SET = 0\n",
    "\n",
    "NE = 2500\n",
    "NI = 500\n",
    "\n",
    "\n",
    "df = pd.read_pickle('df.pkl')\n",
    "\n",
    "# the order of keys IS IMPORTANT for the cClasses not to break down\n",
    "params = {\n",
    "    \"alpha\": 50.0,    # Degree of log-STDP (50.0)\n",
    "    \"usd\": 0.1,       # Release probability of a synapse (0.05 - 0.5)\n",
    "    \"JEI\": 0.15,      # 0.15 or 0.20\n",
    "\n",
    "    \"T\": 1800*1000.0,   # simulation time, ms\n",
    "    \"h\": 0.01,          # time step, ms ??????\n",
    "\n",
    "    # probability of connection\n",
    "    \"cEE\": 0.2, # \n",
    "    \"cIE\": 0.2, #\n",
    "    \"cEI\": 0.5, #\n",
    "    \"cII\": 0.5, #\n",
    "\n",
    "    # Synaptic weights\n",
    "    \"JEE\": 0.15, #\n",
    "    \"JEEinit\": 0.16, # ?????????????\n",
    "    \"JIE\": 0.15, # \n",
    "    \"JII\": 0.06, #\n",
    "    \n",
    "    #initial conditions of synaptic weights\n",
    "    \"JEEh\": 0.15, # Standard synaptic weight E-E\n",
    "    \"sigJ\": 0.3,  #\n",
    "\n",
    "    \"Jtmax\": 0.25, # J_maxˆtot\n",
    "    \"Jtmin\": 0.01, # J_minˆtot # ??? NOT IN THE PAPER\n",
    "\n",
    "    # Thresholds of update\n",
    "    \"hE\": 1.0, # Threshold of update of excitatory neurons\n",
    "    \"hI\": 1.0, # Threshold of update of inhibotory neurons\n",
    "\n",
    "    \"IEex\": 2.0, # Amplitude of steady external input to excitatory neurons\n",
    "    \"IIex\": 0.5, # Amplitude of steady external input to inhibitory neurons\n",
    "    \"mex\": 0.3,        # mean of external input\n",
    "    \"sigex\": 0.1,      # variance of external input\n",
    "\n",
    "    # Average intervals of update, ms\n",
    "    \"tmE\": 5.0,  #t_Eud EXCITATORY\n",
    "    \"tmI\": 2.5,  #t_Iud INHIBITORY\n",
    "    \n",
    "    #Short-Term Depression\n",
    "    \"trec\": 600.0,     # recovery time constant (tau_sd, p.13 and p.12)\n",
    "    \"Jepsilon\": 0.001, # ????????\n",
    "    \n",
    "    # Time constants of STDP decay\n",
    "    \"tpp\": 20.0,  # tau_p\n",
    "    \"tpd\": 40.0,  # tau_d\n",
    "    \"twnd\": 500.0, # STDP window lenght, ms\n",
    "    \n",
    "    \"g\": 1.25,        # ??????\n",
    "    \n",
    "    #homeostatic\n",
    "    \"itauh\": 100,       # decay time of homeostatic plasticity, (100s)\n",
    "    \"hsd\": 0.1,\n",
    "    \"hh\": 10.0,  # SOME MYSTERIOUS PARAMETER\n",
    "    \"Ip\": 1.0, # External current applied to randomly chosen excitatory neurons\n",
    "    \"a\": 0.20, # Fraction of neurons to which this external current is applied\n",
    "    \n",
    "    \"xEinit\": 0.02, # the probability that an excitatory neurons spikes at the beginning of the simulation\n",
    "    \"xIinit\": 0.01, # the probability that an inhibitory neurons spikes at the beginning of the simulation\n",
    "    \"tinit\": 100.00, # period of time after which STDP kicks in (100.0)\n",
    "    \"U\": 0.6,\n",
    "    \"taustf\": 200,\n",
    "    \"taustd\": 500,\n",
    "    \"Cp\": 0.01875,\n",
    "    \"Cd\": 0.0075,\n",
    "    \"HAGA\": True,\n",
    "    \"asym\": True,\n",
    "    \"stimIntensity\": 0.55} \n",
    "\n",
    "\n",
    "params['asym'] = 0\n",
    "params['HAGA'] = 1   \n",
    "params['JEE'] = 0.15\n",
    "\n",
    "params[\"U\"] = df.iloc[ARG]['U']\n",
    "params['g'] = 2.5\n",
    "params[\"tinit\"] = 100\n",
    "params[\"JEEinit\"] = 0.16\n",
    "params[\"Cp\"] = df.iloc[ARG]['Cp']\n",
    "params[\"Cd\"] = df.iloc[ARG]['Cd']\n",
    "params[\"tpp\"] = df.iloc[ARG]['tpp']\n",
    "params[\"tpd\"] = df.iloc[ARG]['tpd']\n",
    "params[\"taustf\"] = df.iloc[ARG]['taustf']\n",
    "params[\"taustd\"] = df.iloc[ARG]['taustd']\n",
    "params[\"alpha\"] = 50.00\n",
    "params[\"itauh\"] = 100\n",
    "\n",
    "\n",
    "params[\"stimIntensity\"] = 0.28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "civil-lunch",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate model\n",
    "m = cClassOne(NE, NI, ARG)\n",
    "\n",
    "# set parameters\n",
    "m.setParams(params)\n",
    "\n",
    "# get parameters\n",
    "ret = m.getState()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "temporal-story",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.9 s, sys: 68.5 ms, total: 12 s\n",
      "Wall time: 12.4 s\n"
     ]
    }
   ],
   "source": [
    "# simulate a given number of time steps\n",
    "\n",
    "%%time\n",
    "m.sim(int(stride_ms//dt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "patient-charlotte",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dump spike states to files\n",
    "\n",
    "m.dumpSpikeStates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "shaped-confidentiality",
   "metadata": {},
   "outputs": [],
   "source": [
    "# toggle saving spike times (1 for on, 0 for off)\n",
    "m.saveSpikes(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "closed-recycling",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "increased-candy",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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