SpiNNaker/eg_balance-network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Balanced network of excitatory and inhibitory neurons.\n",
    "\n",
    "An implementation of benchmarks from Brette et al. (2007) Journal of Computational Neuroscience 23: 349-398\n",
    "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2638500/ (see Fig. 24). \n",
    "\n",
    "In the paper, they compared simulations from NEST and NEURON. Here, we are going to simulate the balanced network with SpiNNaker.\n",
    "\n",
    "The network is based on the COBA models of Vogels & Abbott (J. Neurosci, 2005) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6725859/ .\n",
    "\n",
    "You can look also the explicit model in the supplementary material of Brette et al. (2007) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2638500/#APP2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./extra/VA_EXC_SPIKES.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step0: import the simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import socket\n",
    "import spynnaker8 as sim\n",
    "from pyNN.random import NumpyRNG, RandomDistribution\n",
    "from pyNN.utility.plotting import Figure, Panel\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step1: setup the simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-08 08:53:32 INFO: Caching data for segment -1\n",
      "Getting spikes for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:53:32 WARNING: Population: Excitatory_Cells Does not support units for spikes\n",
      "Getting v for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:53:32 INFO: Caching data for segment -1\n",
      "Getting spikes for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:53:32 WARNING: Population: Inhibitory_Cells Does not support units for spikes\n",
      "Getting v for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:53:32 INFO: Caching data for segment -1\n",
      "Getting spikes for expoisson\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:53:32 WARNING: Population: expoisson Does not support units for spikes\n",
      "2022-03-08 08:53:32 INFO: Resetting\n",
      "2022-03-08 08:53:32 WARNING: The callback queue for Excitatory_Cells:0:254 on 0, 0, 3 overloaded on 164 occasions. This is often a sign that the system is running too quickly for the number of neurons per core.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core.\n",
      "2022-03-08 08:53:32 WARNING: A Timer tick callback was still executing when the next timer tick callback was fired off for Excitatory_Cells:0:254 on 0, 0, 3, 1017 times. This is a sign of the system being overloaded and therefore the results are likely incorrect.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:53:32 WARNING: The timer for Excitatory_Cells:0:254 on 0, 0, 3 fell behind by up to 16 ticks. This is a sign of the system being overloaded and therefore the results are likely incorrect. Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:53:32 WARNING: The callback queue for Excitatory_Cells:255:509 on 0, 0, 4 overloaded on 164 occasions. This is often a sign that the system is running too quickly for the number of neurons per core.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core.\n",
      "2022-03-08 08:53:32 WARNING: A Timer tick callback was still executing when the next timer tick callback was fired off for Excitatory_Cells:255:509 on 0, 0, 4, 1020 times. This is a sign of the system being overloaded and therefore the results are likely incorrect.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:53:32 WARNING: The timer for Excitatory_Cells:255:509 on 0, 0, 4 fell behind by up to 16 ticks. This is a sign of the system being overloaded and therefore the results are likely incorrect. Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:53:32 WARNING: The callback queue for Excitatory_Cells:510:764 on 0, 0, 5 overloaded on 164 occasions. This is often a sign that the system is running too quickly for the number of neurons per core.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core.\n",
      "2022-03-08 08:53:32 WARNING: A Timer tick callback was still executing when the next timer tick callback was fired off for Excitatory_Cells:510:764 on 0, 0, 5, 1022 times. This is a sign of the system being overloaded and therefore the results are likely incorrect.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:53:32 WARNING: The timer for Excitatory_Cells:510:764 on 0, 0, 5 fell behind by up to 16 ticks. This is a sign of the system being overloaded and therefore the results are likely incorrect. Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:53:32 WARNING: The callback queue for Excitatory_Cells:765:1019 on 0, 0, 6 overloaded on 165 occasions. This is often a sign that the system is running too quickly for the number of neurons per core.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core.\n",
      "2022-03-08 08:53:32 WARNING: A Timer tick callback was still executing when the next timer tick callback was fired off for Excitatory_Cells:765:1019 on 0, 0, 6, 1020 times. This is a sign of the system being overloaded and therefore the results are likely incorrect.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:53:32 WARNING: The timer for Excitatory_Cells:765:1019 on 0, 0, 6 fell behind by up to 16 ticks. This is a sign of the system being overloaded and therefore the results are likely incorrect. Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:53:32 WARNING: A Timer tick callback was still executing when the next timer tick callback was fired off for Excitatory_Cells:1020:1199 on 0, 0, 7, 15 times. This is a sign of the system being overloaded and therefore the results are likely incorrect.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:53:32 WARNING: The callback queue for Inhibitory_Cells:0:254 on 0, 0, 8 overloaded on 163 occasions. This is often a sign that the system is running too quickly for the number of neurons per core.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core.\n",
      "2022-03-08 08:53:32 WARNING: A Timer tick callback was still executing when the next timer tick callback was fired off for Inhibitory_Cells:0:254 on 0, 0, 8, 1020 times. This is a sign of the system being overloaded and therefore the results are likely incorrect.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:53:32 WARNING: The timer for Inhibitory_Cells:0:254 on 0, 0, 8 fell behind by up to 16 ticks. This is a sign of the system being overloaded and therefore the results are likely incorrect. Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:53:32 INFO: Read cfg files: /home/spinnaker/sPyNNaker/lib/python3.6/site-packages/spinn_front_end_common/interface/spinnaker.cfg, /home/spinnaker/sPyNNaker/lib/python3.6/site-packages/spynnaker/pyNN/spynnaker.cfg, /home/spinnaker/.spynnaker.cfg\n",
      "2022-03-08 08:53:32 INFO: Will search these locations for binaries: /home/spinnaker/sPyNNaker/lib/python3.6/site-packages/spinn_front_end_common/common_model_binaries : /home/spinnaker/sPyNNaker/lib/python3.6/site-packages/spynnaker/pyNN/model_binaries\n",
      "2022-03-08 08:53:32 WARNING: /home/spinnaker/CNT-2022/SpiNNaker/reports has 17 old reports that have not been closed\n",
      "2022-03-08 08:53:32 WARNING: /home/spinnaker/CNT-2022/SpiNNaker/application_generated_data_files has 17 old reports that have not been closed\n",
      "2022-03-08 08:53:32 INFO: Setting time scale factor to 1.\n",
      "2022-03-08 08:53:32 INFO: Setting machine time step to 1000 micro-seconds.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/home/spinnaker/sPyNNaker/lib/python3.6/site-packages/spinn_front_end_common/interface/spinnaker.cfg', '/home/spinnaker/sPyNNaker/lib/python3.6/site-packages/spynnaker/pyNN/spynnaker.cfg', '/home/spinnaker/.spynnaker.cfg']\n"
     ]
    }
   ],
   "source": [
    "dt = 1          # (ms) simulation timestep\n",
    "tstop = 1000      # (ms) simulaton duration\n",
    "delay = 2 # (ms) \n",
    "\n",
    "sim.setup(\n",
    "    timestep=dt, \n",
    "    min_delay=delay, \n",
    "    max_delay=delay) # [ms] # not that the max_delay supported by SpiNNaker is timestep * 144\n",
    "\n",
    "rngseed = 98766987\n",
    "rng = NumpyRNG(seed=rngseed, parallel_safe=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step2: decide the cell types and parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'tau_m': 20.0, 'tau_syn_E': 5.0, 'tau_syn_I': 10.0, 'v_rest': -60.0, 'v_reset': -60.0, 'v_thresh': -50.0, 'cm': 0.2, 'tau_refrac': 5.0, 'i_offset': 0, 'e_rev_E': 0.0, 'e_rev_I': -80.0}\n"
     ]
    }
   ],
   "source": [
    "# cell type\n",
    "celltype = sim.IF_cond_exp\n",
    "#  Leaky integrate and fire model with fixed threshold and exponentially-decaying post-synaptic conductance.\n",
    "#  http://neuralensemble.org/docs/PyNN/_modules/pyNN/standardmodels/cells.html#IF_cond_exp\n",
    "    \n",
    "# Cell parameters\n",
    "area = 20000.     # (µm²)\n",
    "tau_m = 20.       # (ms)\n",
    "cm = 1.           # (µF/cm²)\n",
    "g_leak = 5e-5     # (S/cm²)\n",
    "\n",
    "E_leak = None\n",
    "E_leak = -60.  # (mV)\n",
    "v_thresh = -50.   # (mV)\n",
    "v_reset = -60.    # (mV)\n",
    "t_refrac = 5.     # (ms) (clamped at v_reset)\n",
    "v_mean = -60.     # (mV) mean membrane potential, for calculating CUBA weights\n",
    "tau_exc = 5.      # (ms)\n",
    "tau_inh = 10.     # (ms)\n",
    "Erev_exc = 0.     # (mV)\n",
    "Erev_inh = -80.   # (mV)\n",
    "\n",
    "# === Calculate derived parameters ===\n",
    "area = area * 1e-8                     # convert to cm²\n",
    "cm = cm * area * 1000                  # convert to nF\n",
    "Rm = 1e-6 / (g_leak * area)            # membrane resistance in MΩ\n",
    "assert tau_m == cm * Rm                # just to check\n",
    "\n",
    "cell_params = {'tau_m': tau_m,\n",
    "               'tau_syn_E': tau_exc,\n",
    "               'tau_syn_I': tau_inh,\n",
    "               'v_rest': E_leak,\n",
    "               'v_reset': v_reset,\n",
    "               'v_thresh': v_thresh,\n",
    "               'cm': cm,\n",
    "               'tau_refrac': t_refrac,\n",
    "               'i_offset': 0\n",
    "               }\n",
    "\n",
    "cell_params['e_rev_E'] = Erev_exc\n",
    "cell_params['e_rev_I'] = Erev_inh\n",
    "\n",
    "print(cell_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step3: making cell populations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200 300\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['exc', 'inh'])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 1500          # number of cells\n",
    "r_ei = 4.0        # number of excitatory cells:number of inhibitory cells\n",
    "n_exc = int(round((n * r_ei / (1 + r_ei))))  # number of excitatory cells\n",
    "n_inh = n - n_exc                            # number of inhibitory cells\n",
    "print(\"{} {}\".format(n_exc, n_inh))\n",
    "\n",
    "pops = {\n",
    "    'exc': sim.Population(\n",
    "                                n_exc, \n",
    "                                celltype(**cell_params), \n",
    "                                label=\"Excitatory_Cells\"),\n",
    "\n",
    "    'inh': sim.Population(\n",
    "                                n_inh, \n",
    "                                celltype(**cell_params), \n",
    "                                label=\"Inhibitory_Cells\")\n",
    "}\n",
    "\n",
    "pops['exc'].record([\"spikes\", 'v', 'gsyn_exc', 'gsyn_inh'])\n",
    "pops['inh'].record([\"spikes\", 'v', 'gsyn_exc', 'gsyn_inh'])\n",
    "\n",
    "\n",
    "uniformDistr = RandomDistribution('uniform', [v_reset, v_thresh], rng=rng)\n",
    "pops['exc'].initialize(v=uniformDistr)\n",
    "pops['inh'].initialize(v=uniformDistr)\n",
    "\n",
    "sim.set_number_of_neurons_per_core(sim.IF_cond_exp, 50)      # this will set\n",
    "# 50 neurons per core\n",
    "pops.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step4: define the synapse types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Synapse parameters\n",
    "Gexc = None\n",
    "Ginh = None\n",
    "Gexc = 4.     # (nS) #1\n",
    "Ginh = 51.    # (nS) #1\n",
    "w_exc = Gexc * 1e-3              # We convert conductances to uS\n",
    "w_inh = Ginh * 1e-3\n",
    "\n",
    "exc_synapses = sim.StaticSynapse(weight=w_exc, delay=delay)\n",
    "inh_synapses = sim.StaticSynapse(weight=w_inh, delay=delay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step5: select the connections algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pconn = 0.02      # connection probability (2%)\n",
    "\n",
    "exc_conn = sim.FixedProbabilityConnector(pconn, rng=rng)\n",
    "inh_conn = sim.FixedProbabilityConnector(pconn, rng=rng)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step6: make the projections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['e2e', 'e2i', 'i2e', 'i2i'])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connections = {\n",
    "    \n",
    "    'e2e': sim.Projection(\n",
    "        pops['exc'],\n",
    "        pops['exc'], \n",
    "        exc_conn, \n",
    "        receptor_type='excitatory',\n",
    "        synapse_type=exc_synapses),\n",
    "    \n",
    "    'e2i': sim.Projection(\n",
    "        pops['exc'], \n",
    "        pops['inh'], \n",
    "        exc_conn, \n",
    "        receptor_type='excitatory',\n",
    "        synapse_type=exc_synapses),\n",
    "    \n",
    "    'i2e': sim.Projection(\n",
    "        pops['inh'], \n",
    "        pops['exc'], \n",
    "        inh_conn, \n",
    "        receptor_type='inhibitory',\n",
    "        synapse_type=inh_synapses),\n",
    "    \n",
    "    'i2i': sim.Projection(\n",
    "        pops['inh'],\n",
    "        pops['inh'],\n",
    "        inh_conn, \n",
    "        receptor_type='inhibitory',\n",
    "        synapse_type=inh_synapses)\n",
    "    \n",
    "        }\n",
    "\n",
    "connections.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step7: setting the thalamic stimulus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dict_keys(['e2e', 'e2i', 'i2e', 'i2i', 'ext2e', 'ext2i']),\n",
       " dict_keys(['exc', 'inh', 'thalamus']))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_thalamic_cells = 20 \n",
    "stim_dur = 50.    # (ms) duration of random stimulation\n",
    "rate = 100.       # (Hz) frequency of the random stimulation\n",
    "\n",
    "exc_conn = None\n",
    "\n",
    "pops['thalamus'] = sim.Population(\n",
    "    n_thalamic_cells, \n",
    "    sim.SpikeSourcePoisson(rate=rate, duration=stim_dur),\n",
    "    label=\"expoisson\")\n",
    "pops['thalamus'].record(\"spikes\")\n",
    "\n",
    "\n",
    "rconn = 0.01\n",
    "ext_conn = sim.FixedProbabilityConnector(rconn)\n",
    "\n",
    "connections['ext2e'] = sim.Projection(\n",
    "    pops['thalamus'], \n",
    "    pops['exc'], \n",
    "    ext_conn, \n",
    "    receptor_type='excitatory',\n",
    "    synapse_type=sim.StaticSynapse(weight=0.1))\n",
    "\n",
    "connections['ext2i'] = sim.Projection(\n",
    "    pops['thalamus'], \n",
    "    pops['inh'], \n",
    "    ext_conn, \n",
    "    receptor_type='excitatory',\n",
    "    synapse_type=sim.StaticSynapse(weight=0.1))\n",
    "\n",
    "connections.keys(), pops.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step8: run simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-08 08:53:37 INFO: Simulating for 1000 1.0ms timesteps using a hardware timestep of 1000us\n",
      "2022-03-08 08:53:37 INFO: Starting execution process\n",
      "2022-03-08 08:53:40 INFO: Time 0:00:03.159208 taken by SpallocMaxMachineGenerator\n",
      "Pre allocating resources for Extra Monitor support vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:53:49 INFO: Time 0:00:08.397431 taken by PreAllocateResourcesForExtraMonitorSupport\n",
      "Partitioning graph vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Partitioning graph edges\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:53:53 INFO: Time 0:00:04.762891 taken by PartitionAndPlacePartitioner\n",
      "Created spalloc job 6319411\n",
      "2022-03-08 08:53:53 INFO: Created spalloc job 6319411\n",
      "Waiting for board power commands to complete.\n",
      "2022-03-08 08:53:53 INFO: Waiting for board power commands to complete.\n",
      "2022-03-08 08:53:58 INFO: Time 0:00:05.048610 taken by SpallocAllocator\n",
      "2022-03-08 08:53:58 INFO: Creating transceiver for 10.11.193.145\n",
      "2022-03-08 08:53:58 INFO: Working out if machine is booted\n",
      "2022-03-08 08:54:02 INFO: Attempting to boot machine\n",
      "2022-03-08 08:54:08 INFO: Found board with version [Version: SC&MP 3.2.5 at SpiNNaker:0:0:0 (built Thu Aug  1 08:15:06 2019)]\n",
      "2022-03-08 08:54:08 INFO: Machine communication successful\n",
      "2022-03-08 08:54:08 INFO: Detected a machine on IP address 10.11.193.145 which has 856 cores and 120.0 links\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:09.634009 taken by MachineGenerator\n",
      "Generating partitioner report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.009354 taken by PartitionerReport\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.000215 taken by NetworkSpecificationReport\n",
      "Allocating virtual identifiers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.028497 taken by MallocBasedChipIDAllocator\n",
      "Inserting extra monitors into graphs\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.023144 taken by InsertExtraMonitorVerticesToGraphs\n",
      "Writing the board chip report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.002093 taken by BoardChipReport\n",
      "Filtering edges\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.030360 taken by GraphEdgeFilter\n",
      "Placing graph vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.038640 taken by OneToOnePlacer\n",
      "Inserting edges between vertices which require FR speed up functionality.\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.081783 taken by InsertEdgesToExtraMonitorFunctionality\n",
      "Generating routing tables for data in system processes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.019537 taken by DataInMulticastRoutingGenerator\n",
      "Generating fixed router routes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.014646 taken by FixedRouteRouter\n",
      "Generating placement report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Generating placement by core report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.066983 taken by PlacerReportWithApplicationGraph\n",
      "Routing\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.032457 taken by NerRoute\n",
      "Discovering tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Allocating tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.041929 taken by BasicTagAllocator\n",
      "Reporting Tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.017339 taken by TagReport\n",
      "Getting number of keys required by each edge using application graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.030584 taken by EdgeToNKeysMapper\n",
      "Getting constraints for application graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:08 INFO: Time 0:00:00.040795 taken by ProcessPartitionConstraints\n",
      "Allocating routing keys\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:09 INFO: Time 0:00:00.017950 taken by MallocBasedRoutingInfoAllocator\n",
      "Generating Routing info report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:09 INFO: Time 0:00:00.035477 taken by routingInfoReports\n",
      "Generating routing tables\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:09 INFO: Time 0:00:00.031100 taken by BasicRoutingTableGenerator\n",
      "Finding executable start types\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:09 INFO: Time 0:00:00.034885 taken by LocateExecutableStartType\n",
      "Initialising buffers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:09 INFO: Time 0:00:00.035096 taken by BufferManagerCreator\n",
      "Generating data specifications\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:09 INFO: Time 0:00:00.408366 taken by SpynnakerDataSpecificationWriter\n",
      "Preparing Routing Tables\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:09 INFO: Time 0:00:00.007403 taken by RoutingSetup\n",
      "Finding binaries\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:09 INFO: Time 0:00:00.063650 taken by GraphBinaryGatherer\n",
      "Running routing table compression on chip\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:14 INFO: Time 0:00:04.516383 taken by MundyOnChipRouterCompression\n",
      "Generating Router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:14 INFO: Time 0:00:00.004735 taken by unCompressedRoutingTableReports\n",
      "loading fixed routes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:14 INFO: Time 0:00:00.049648 taken by LoadFixedRoutes\n",
      "Executing data specifications and loading data for system vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:14 INFO: Time 0:00:00.149872 taken by HostExecuteSystemDataSpecification\n",
      "Loading system executables onto the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:20 INFO: Time 0:00:05.720390 taken by LoadSystemExecutableImages\n",
      "Clearing tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Loading Tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:20 INFO: Time 0:00:00.022256 taken by TagsLoader\n",
      "Writing data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:20 INFO: Time 0:00:00.025387 taken by WriteMemoryIOData\n",
      "Executing data specifications and loading data for application vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:20 INFO: Time 0:00:00.301476 taken by HostExecuteApplicationDataSpecification\n",
      "Expanding Synapses\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:24 INFO: Time 0:00:04.014421 taken by SynapseExpander\n",
      "Writing fixed route report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:24 INFO: Time 0:00:00.044363 taken by FixedRouteFromMachineReport\n",
      "Loading executables onto the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:31 INFO: Time 0:00:06.881042 taken by LoadApplicationExecutableImages\n",
      "Reading Routing Tables from Machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:31 INFO: Time 0:00:00.017475 taken by RoutingTableFromMachineReport\n",
      "Generating compressed router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:31 INFO: Time 0:00:00.011943 taken by compressedRoutingTableReports\n",
      "Generating comparison of router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:31 INFO: Time 0:00:00.010141 taken by comparisonOfRoutingTablesReport\n",
      "2022-03-08 08:54:31 INFO: Running for 1 steps for a total of 1000.0ms\n",
      "2022-03-08 08:54:31 INFO: Run 1 of 1\n",
      "Generating SDRAM usage report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:31 INFO: Time 0:00:00.082202 taken by SdramUsageReportPerChip\n",
      "Updating run time\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:31 INFO: Time 0:00:00.028836 taken by ChipRuntimeUpdater\n",
      "2022-03-08 08:54:31 INFO: creating live event connection database in /home/spinnaker/CNT-2022/SpiNNaker/application_generated_data_files/2022-03-08-08-53-32-256987/run_1/input_output_database.db\n",
      "Creating graph description database\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:31 INFO: Time 0:00:00.078145 taken by DatabaseInterface\n",
      "2022-03-08 08:54:31 INFO: ** Notifying external sources that the database is ready for reading **\n",
      "2022-03-08 08:54:31 INFO: Time 0:00:00.001416 taken by NotificationProtocol\n",
      "2022-03-08 08:54:31 INFO: *** Running simulation... *** \n",
      "Loading buffers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:31 INFO: ** Awaiting for a response from an external source to state its ready for the simulation to start **\n",
      "2022-03-08 08:54:31 INFO: ** Sending start / resume message to external sources to state the simulation has started or resumed. **\n",
      "2022-03-08 08:54:31 INFO: ** Awaiting for a response from an external source to state its ready for the simulation to start **\n",
      "2022-03-08 08:54:31 INFO: Application started; waiting 1.1s for it to stop\n",
      "2022-03-08 08:54:32 INFO: ** Sending pause / stop message to external sources to state the simulation has been paused or stopped. **\n",
      "2022-03-08 08:54:32 INFO: Time 0:00:01.187236 taken by ApplicationRunner\n",
      "Extracting buffers from the last run\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:36 INFO: Time 0:00:03.874612 taken by BufferExtractor\n",
      "Getting provenance data from machine graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting provenance data from application graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:36 INFO: Time 0:00:00.108118 taken by GraphProvenanceGatherer\n",
      "Getting provenance data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:36 INFO: Time 0:00:00.046110 taken by PlacementsProvenanceGatherer\n",
      "Getting Router Provenance\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:36 INFO: Time 0:00:00.057183 taken by RouterProvenanceGatherer\n",
      "Getting profile data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2022-03-08 08:54:36 INFO: Time 0:00:00.043660 taken by ProfileDataGatherer\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1000.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tstop = 1000 # (ms)\n",
    "sim.run(tstop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step9: save results in a folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting spikes for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n"
     ]
    }
   ],
   "source": [
    "outputs = {}\n",
    "\n",
    "for layer in ['exc', 'inh']:\n",
    "    \n",
    "    # save on the notebook space\n",
    "    outputs[layer] = pops[layer].get_data()\n",
    "    \n",
    "    # save in the folder space\n",
    "    for recording in ['v', 'gsyn_inh', 'gsyn_exc', 'spikes']:\n",
    "        pops[layer].write_data(str(layer) + '_' + str(recording) + '.pkl')\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step10: recover results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exc v\n",
      "exc gsyn_exc\n",
      "exc gsyn_inh\n",
      "inh v\n",
      "inh gsyn_exc\n",
      "inh gsyn_inh\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys([('exc', 'v'), ('exc', 'gsyn_exc'), ('exc', 'gsyn_inh'), ('exc', 'spikes'), ('inh', 'v'), ('inh', 'gsyn_exc'), ('inh', 'gsyn_inh'), ('inh', 'spikes')])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def recover_results(outputs):\n",
    "    results = {}\n",
    "    for key in outputs.keys(): # to extract the name of the layer, e.g., Exc, Inh, Thalamus, etc  \n",
    "        \n",
    "        # to get voltage and conductances\n",
    "        for analogsignal in outputs[key].segments[0].analogsignals:\n",
    "            print(key, analogsignal.name)\n",
    "            results[key, analogsignal.name] = analogsignal\n",
    "\n",
    "        # to get spikes\n",
    "        results[key, 'spikes'] = outputs[key].segments[0].spiketrains\n",
    "    return results\n",
    "\n",
    "# check results\n",
    "results = recover_results(outputs)\n",
    "results.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step11: postprocessing (looking at the results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: module://ipympl.backend_nbagg\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15969717af33435daef03515de7401f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<pyNN.utility.plotting.Figure at 0x7f8c60885160>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyNN.utility.plotting import Figure, Panel\n",
    "%matplotlib\n",
    "Figure(\n",
    "    # raster plot of the presynaptic neuron spike times\n",
    "    Panel(results['exc', 'spikes'], xlabel=\"Time/ms\", xticks=True,\n",
    "          yticks=True, markersize=0.2, xlim=(0, tstop)),\n",
    "    title=\"Vogels-Abbott benchmark: excitatory cells spikes\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: module://ipympl.backend_nbagg\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f0c8a9486754ef391e2cc0525b42f90",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<pyNN.utility.plotting.Figure at 0x7f8c60de75f8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### from pyNN.utility.plotting import Figure, Panel\n",
    "%matplotlib\n",
    "Figure(\n",
    "    # raster plot of the presynaptic neuron spike times\n",
    "    Panel(results['inh', 'spikes'], xlabel=\"Time (ms)\", xticks=True,\n",
    "          yticks=True, markersize=0.2, xlim=(0, tstop)),\n",
    "    title=\"Vogels-Abbott benchmark: inhibitory cells spikes\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: module://ipympl.backend_nbagg\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aae0f0e07a9d4e1e8c61aa89c1464839",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Excitatory cells'), Text(0, 0.5, 'Inhibitory cells')]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# classic matplotlib rasterplot function\n",
    "\n",
    "%matplotlib\n",
    "fig, ax = plt.subplots(2,1, sharex=True, figsize=(9,5))\n",
    "p0=ax[0].eventplot(results['exc', 'spikes'], color='green')\n",
    "p1=ax[1].eventplot(results['inh', 'spikes'], color='red')\n",
    "[ax[i].set_xlabel('Time (ms)', fontsize=14) for i in [0,1]]\n",
    "[ax[i].set_ylabel(feat, fontsize=14) for i, feat in enumerate(['Excitatory cells', 'Inhibitory cells'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting spikes for Excitatory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for Inhibitory_Cells\n",
      "|0%                          50%                         100%|\n",
      " ============================================================"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exc cell 200 : 30 spikes/1000ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# check spikes\n",
    "\n",
    "spike_rate = {\n",
    "    'exc': pops['exc'].get_spike_counts(),\n",
    "    'inh': pops['inh'].get_spike_counts()\n",
    "}\n",
    "\n",
    "idx=200\n",
    "pop = 'exc'\n",
    "print(pop + ' cell ' + str(idx) + ' :', spike_rate[pop][idx], 'spikes/' + str(tstop) + 'ms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: module://ipympl.backend_nbagg\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4ebebba7c4b4300899e549418810926",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'membrane potential [mV]'),\n",
       " Text(0.5, 0, 'gsyn_exc [uS]'),\n",
       " Text(0.5, 0, 'gsyn_inh [uS]')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check conductances and membrane voltage potentials\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib\n",
    "fig, ax = plt.subplots(1,3,figsize=(11,4), sharey=True, sharex=False)\n",
    "ax = ax.flatten()\n",
    "p=ax[0].hist(np.asarray(results['exc', 'v']).ravel(), 100, label='exc', log=False)\n",
    "p=ax[1].hist(np.asarray(results['exc', 'gsyn_exc']).ravel(), 100, label='exc')\n",
    "p=ax[2].hist(np.asarray(results['exc', 'gsyn_inh']).ravel(), 100, label='exc')\n",
    "\n",
    "p=ax[0].hist(np.asarray(results['inh', 'v']).ravel(), 100, label='inh')\n",
    "p=ax[1].hist(np.asarray(results['inh', 'gsyn_exc']).ravel(), 100, label='inh')\n",
    "p=ax[2].hist(np.asarray(results['inh', 'gsyn_inh']).ravel(), 100, label='inh')\n",
    "\n",
    "ax[0].set_xlim(-90,-40)\n",
    "ax[1].set_xlim(0,0.2)\n",
    "ax[2].set_xlim(0,0.5)\n",
    "\n",
    "[ax[i].legend() for i in [0,1,2]]\n",
    "\n",
    "[ax[i].set_ylabel('excitatory cells', fontsize=14) for i in [0,1,2]]\n",
    "[ax[idx].set_xlabel(feat, fontsize=14) for idx, feat in enumerate(['membrane potential [mV]', 'gsyn_exc [uS]', 'gsyn_inh [uS]'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f0072fe5bd74292b24a1b88dfe77ece",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "dict_keys([0, 1, 2])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# voltage in excitatory pop\n",
    "\n",
    "fig, ax = plt.subplots(1,3, figsize=(13,4), sharey=True, sharex=True)\n",
    "ax = ax.flatten()\n",
    "\n",
    "p = {}\n",
    "for idx, feat in enumerate(['v', 'gsyn_exc', 'gsyn_inh']):\n",
    "    p[idx] = ax[idx].imshow(np.asarray(results['exc', feat]).T,  norm=colors.PowerNorm(gamma= 0.75))\n",
    "\n",
    "[fig.colorbar(p[idx], ax=ax[idx], shrink=0.5) for idx in [0,1,2]]\n",
    "\n",
    "[ax[i].set_xlabel('time [ms]', fontsize=14) for i in [0,1,2]]\n",
    "[ax[i].set_ylabel('excitatory cells', fontsize=14) for i in [0,1,2]]\n",
    "[ax[idx].set_title(feat, fontsize=14) for idx, feat in enumerate(['membrane potential [mV]', 'gsyn_exc [uS]', 'gsyn_inh [uS]'])]\n",
    "\n",
    "p.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c6ede6a501e4aeaac4f6782a052c6ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# voltage in inhibitory pop\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(3,1, figsize=(11,11), sharey=True, sharex=False)\n",
    "ax = ax.flatten()\n",
    "\n",
    "p = {}\n",
    "for idx, feat in enumerate(['v', 'gsyn_exc', 'gsyn_inh']):\n",
    "    p[idx] = ax[idx].imshow(np.asarray(results['inh', feat]).T,  norm=colors.PowerNorm(gamma= 0.75))\n",
    "\n",
    "[fig.colorbar(p[idx], ax=ax[idx], shrink=0.6) for idx in [0,1,2]]\n",
    "\n",
    "[ax[i].set_xlabel('time [ms]', fontsize=14) for i in [0,1,2]]\n",
    "[ax[i].set_ylabel('inhibitory cells', fontsize=14) for i in [0,1,2]]\n",
    "[ax[idx].set_title(feat, fontsize=14) for idx, feat in enumerate(['membrane potential [mV]', 'gsyn_exc [uS]', 'gsyn_inh [uS]'])]\n",
    "\n",
    "rasterplot=True\n",
    "if rasterplot:\n",
    "    for i in [0,1,2]:\n",
    "        ax[i].eventplot(results['inh', 'spikes'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# signal processing step, statistics, information theory tools, etc\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step12: close simulation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-08 08:54:44 WARNING: A Timer tick callback was still executing when the next timer tick callback was fired off for Excitatory_Cells:0:254 on 0, 0, 3, 4 times. This is a sign of the system being overloaded and therefore the results are likely incorrect.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:54:44 WARNING: A Timer tick callback was still executing when the next timer tick callback was fired off for Excitatory_Cells:255:509 on 0, 0, 4, 5 times. This is a sign of the system being overloaded and therefore the results are likely incorrect.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:54:44 WARNING: A Timer tick callback was still executing when the next timer tick callback was fired off for Excitatory_Cells:510:764 on 0, 0, 5, 5 times. This is a sign of the system being overloaded and therefore the results are likely incorrect.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:54:44 WARNING: A Timer tick callback was still executing when the next timer tick callback was fired off for Excitatory_Cells:765:1019 on 0, 0, 6, 4 times. This is a sign of the system being overloaded and therefore the results are likely incorrect.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n",
      "2022-03-08 08:54:44 WARNING: A Timer tick callback was still executing when the next timer tick callback was fired off for Inhibitory_Cells:0:254 on 0, 0, 8, 4 times. This is a sign of the system being overloaded and therefore the results are likely incorrect.  Please increase the machine time step or time_scale_factor or decrease the number of neurons per core\n"
     ]
    }
   ],
   "source": [
    "# if you run it, you cannot reload some cells, but it is needed to restart the overall simulation\n",
    "\n",
    "sim.end()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1: on the self sustained acitivity conditions\n",
    "\n",
    "- in the image below, the panel a) shows the sustained activity conditions of the network. <br>\n",
    "  Try different set of weights combinations for discovering different functional network configurations\n",
    "   \n",
    "<img src=\"./extra/VA_EXC_BALANCE.png\">\n",
    "\n",
    "- image taken from Vogels & Abbott (J. Neurosci, 2005) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6725859/,\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sPyNNaker",
   "language": "python",
   "name": "spynnaker"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}