SpiNNaker/eg_testing-STDP-model-network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1D testing STDP model network\n",
    "\n",
    "http://www.scholarpedia.org/article/Spike-timing_dependent_plasticity\n",
    "\n",
    "STDP can be seen as a spike-based formulation of a Hebbian learning rule. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two thalamic inputs are sent to two cells (one for each) such that there's a small time gap between the two inputs.\n",
    "\n",
    "Two cells are excitatory and the synapse from the first cells to the second is modelled with STDP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fileName\n",
    "\n",
    "fileName = 'eg_testing-STDP-model-network'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# libs\n",
    "\n",
    "import pyNN.spiNNaker as sim\n",
    "from pyNN import space \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-17 10:23:03 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",
      "2023-03-17 10:23:03 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",
      "2023-03-17 10:23:03 WARNING: /home/spinnaker/CNT-2023/SpiNNaker/reports has 38 old reports that have not been closed\n",
      "2023-03-17 10:23:03 WARNING: /home/spinnaker/CNT-2023/SpiNNaker/application_generated_data_files has 38 old reports that have not been closed\n",
      "2023-03-17 10:23:03 INFO: Setting time scale factor to 1.\n",
      "2023-03-17 10:23:03 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": [
    "# simulation settings\n",
    "\n",
    "sim.setup(\n",
    "        timestep=1, # [ms]\n",
    "        min_delay=1, # [ms]\n",
    "        max_delay=100) # [ms]\n",
    "\n",
    "simtime = 125 # [ms+]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## make the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-17 10:23:03 WARNING: Formal PyNN specifies that v should be set using initial_values not cell_params\n",
      "2023-03-17 10:23:03 WARNING: Formal PyNN specifies that isyn_exc should be set using initial_values not cell_params\n",
      "2023-03-17 10:23:03 WARNING: Formal PyNN specifies that isyn_inh should be set using initial_values not cell_params\n",
      "2023-03-17 10:23:03 WARNING: Formal PyNN specifies that v should be set using initial_values not cell_params\n",
      "2023-03-17 10:23:03 WARNING: Formal PyNN specifies that isyn_exc should be set using initial_values not cell_params\n",
      "2023-03-17 10:23:03 WARNING: Formal PyNN specifies that isyn_inh should be set using initial_values not cell_params\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['pre', 'post'])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define make_pop\n",
    "\n",
    "pops = {}\n",
    "\n",
    "pops['pre'] = sim.Population(\n",
    "                        1, # one cell in each cell model\n",
    "                        sim.IF_curr_exp,\n",
    "                        cellparams=sim.IF_curr_exp.default_initial_values, # std pars used\n",
    "                        structure = space.Line(dx=1.0, x0=0.0, y=0.0, z=0.0),\n",
    "                        initial_values=None,\n",
    "                        label='network',\n",
    "                        constraints=None,\n",
    "                        additional_parameters=None,\n",
    "                        )\n",
    "                        \n",
    "pops['post'] = sim.Population(\n",
    "                        1, # one cell in each cell model\n",
    "                        sim.IF_curr_exp,\n",
    "                        cellparams=sim.IF_curr_exp.default_initial_values, # std pars used\n",
    "                        structure = space.Line(dx=1.0, x0=0.0, y=0.0, z=0.0),\n",
    "                        initial_values=None,\n",
    "                        label='network',\n",
    "                        constraints=None,\n",
    "                        additional_parameters=None,\n",
    "                        )\n",
    "\n",
    "pops['pre'].record(['spikes', 'v', 'gsyn_exc', 'gsyn_inh'])\n",
    "pops['post'].record(['spikes', 'v', 'gsyn_exc', 'gsyn_inh'])\n",
    "\n",
    "\n",
    "\n",
    "pops.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## make the thalamic input stimulus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['pre', 'post', 'thalamus-pre', 'thalamus-post'])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pops['thalamus-pre'] = sim.Population(1, \n",
    "                            sim.SpikeSourceArray([30, 40, 50, 60, 70, 80, 90]),\n",
    "                            structure = space.Line(dx=1.0, x0=0.0, y=0.0, z=0.0),\n",
    "                            )    \n",
    "\n",
    "pops['thalamus-post'] = sim.Population(1, \n",
    "                             sim.SpikeSourceArray([32, 42, 52, 62, 72, 82, 92]),\n",
    "                             structure = space.Line(dx=1.0, x0=0.0, y=0.0, z=0.0))\n",
    "\n",
    "pops['thalamus-post'].record('spikes')\n",
    "pops['thalamus-pre'].record('spikes')\n",
    "\n",
    "\n",
    "pops.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## make learning rule between pre and post syn cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([('pre', 'post')])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timing = sim.SpikePairRule(tau_plus=10.0, \n",
    "                           tau_minus=1.0, \n",
    "                           A_plus=0.5, \n",
    "                           A_minus=0.5)\n",
    "\n",
    "weight = sim.AdditiveWeightDependence(w_max=5.0, w_min=0.0) # range of learnig #nA\n",
    "\n",
    "starting_weight = 0.1 # uS\n",
    "projs = {}\n",
    "projs['pre', 'post'] = sim.Projection(pops['pre'], pops['post'], \n",
    "                                      sim.OneToOneConnector(), \n",
    "                                      sim.STDPMechanism(\n",
    "                                          timing_dependence=timing, \n",
    "                                          weight_dependence=weight, \n",
    "                                          weight=starting_weight, \n",
    "                                          delay=5.0))\n",
    "\n",
    "projs.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## make the thalamic - pops projections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([('pre', 'post'), ('thalamus-pre', 'pre'), ('thalamus-post', 'post')])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "projs['thalamus-pre', 'pre'] = sim.Projection(\n",
    "                                    pops['thalamus-pre'],\n",
    "                                    pops['pre'],\n",
    "                                    sim.OneToOneConnector(),\n",
    "                                    synapse_type=sim.StaticSynapse(weight=5),#, delay=None),\n",
    "                                    receptor_type = 'excitatory',\n",
    "                                    space = space.Space(axes = 'x'),\n",
    "                                    label=None,\n",
    "                                )\n",
    "\n",
    "\n",
    "projs['thalamus-post', 'post'] = sim.Projection(\n",
    "                                    pops['thalamus-post'],\n",
    "                                    pops['post'],\n",
    "                                    sim.OneToOneConnector(),\n",
    "                                    synapse_type=sim.StaticSynapse(weight=5),#, delay=1.0),\n",
    "                                    receptor_type = 'excitatory',\n",
    "                                    space = space.Space(axes = 'x'),\n",
    "                                    label=None,\n",
    "                                )\n",
    "\n",
    "\n",
    "\n",
    "projs.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## run the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-17 10:23:03 INFO: Simulating for 125 1.0ms timesteps using a hardware timestep of 1000us\n",
      "2023-03-17 10:23:03 INFO: Starting execution process\n",
      "2023-03-17 10:23:07 INFO: Time 0:00:03.148789 taken by SpallocMaxMachineGenerator\n",
      "Pre allocating resources for Extra Monitor support vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:16 INFO: Time 0:00:09.124047 taken by PreAllocateResourcesForExtraMonitorSupport\n",
      "Partitioning graph vertices\n",
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      " ============================================================\n",
      "Partitioning graph edges\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:21 INFO: Time 0:00:04.988478 taken by PartitionAndPlacePartitioner\n",
      "Created spalloc job 7076729\n",
      "2023-03-17 10:23:21 INFO: Created spalloc job 7076729\n",
      "Waiting for board power commands to complete.\n",
      "2023-03-17 10:23:21 INFO: Waiting for board power commands to complete.\n",
      "2023-03-17 10:23:26 INFO: Time 0:00:05.050873 taken by SpallocAllocator\n",
      "2023-03-17 10:23:26 INFO: Creating transceiver for 10.11.194.129\n",
      "2023-03-17 10:23:26 INFO: Working out if machine is booted\n",
      "2023-03-17 10:23:30 INFO: Attempting to boot machine\n",
      "2023-03-17 10:23:35 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",
      "2023-03-17 10:23:35 INFO: Machine communication successful\n",
      "2023-03-17 10:23:35 INFO: Detected a machine on IP address 10.11.194.129 which has 854 cores and 120.0 links\n",
      "2023-03-17 10:23:35 INFO: Time 0:00:09.637433 taken by MachineGenerator\n",
      "Generating partitioner report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:35 INFO: Time 0:00:00.013330 taken by PartitionerReport\n",
      "2023-03-17 10:23:35 INFO: Time 0:00:00.000268 taken by NetworkSpecificationReport\n",
      "Allocating virtual identifiers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:35 INFO: Time 0:00:00.035054 taken by MallocBasedChipIDAllocator\n",
      "Inserting extra monitors into graphs\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:35 INFO: Time 0:00:00.026167 taken by InsertExtraMonitorVerticesToGraphs\n",
      "Writing the board chip report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:35 INFO: Time 0:00:00.006510 taken by BoardChipReport\n",
      "Filtering edges\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:35 INFO: Time 0:00:00.031980 taken by GraphEdgeFilter\n",
      "Placing graph vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:35 INFO: Time 0:00:00.028356 taken by OneToOnePlacer\n",
      "Inserting edges between vertices which require FR speed up functionality.\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.043067 taken by InsertEdgesToExtraMonitorFunctionality\n",
      "Generating routing tables for data in system processes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.007210 taken by DataInMulticastRoutingGenerator\n",
      "Generating fixed router routes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.006404 taken by FixedRouteRouter\n",
      "Generating placement report\n",
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      " ============================================================\n",
      "Generating placement by core report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.063600 taken by PlacerReportWithApplicationGraph\n",
      "Routing\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.029198 taken by NerRoute\n",
      "Discovering tags\n",
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      " ============================================================\n",
      "Allocating tags\n",
      "|0%                          50%                         100%|\n",
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      "2023-03-17 10:23:36 INFO: Time 0:00:00.038952 taken by BasicTagAllocator\n",
      "Reporting Tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.007507 taken by TagReport\n",
      "Getting number of keys required by each edge using application graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.026964 taken by EdgeToNKeysMapper\n",
      "Getting constraints for application graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.027985 taken by ProcessPartitionConstraints\n",
      "Allocating routing keys\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.016380 taken by MallocBasedRoutingInfoAllocator\n",
      "Generating Routing info report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.026709 taken by routingInfoReports\n",
      "Generating routing tables\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.025298 taken by BasicRoutingTableGenerator\n",
      "Finding executable start types\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.026720 taken by LocateExecutableStartType\n",
      "Initialising buffers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.038445 taken by BufferManagerCreator\n",
      "Generating data specifications\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.071712 taken by SpynnakerDataSpecificationWriter\n",
      "Preparing Routing Tables\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.012855 taken by RoutingSetup\n",
      "Finding binaries\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:36 INFO: Time 0:00:00.033761 taken by GraphBinaryGatherer\n",
      "Running routing table compression on chip\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:41 INFO: Time 0:00:04.520197 taken by MundyOnChipRouterCompression\n",
      "Generating Router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:41 INFO: Time 0:00:00.008583 taken by unCompressedRoutingTableReports\n",
      "loading fixed routes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:41 INFO: Time 0:00:00.041461 taken by LoadFixedRoutes\n",
      "Executing data specifications and loading data for system vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:41 INFO: Time 0:00:00.163082 taken by HostExecuteSystemDataSpecification\n",
      "Loading system executables onto the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:46 INFO: Time 0:00:05.719176 taken by LoadSystemExecutableImages\n",
      "Clearing tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Loading Tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:47 INFO: Time 0:00:00.018791 taken by TagsLoader\n",
      "Writing data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:47 INFO: Time 0:00:00.021532 taken by WriteMemoryIOData\n",
      "Executing data specifications and loading data for application vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:47 INFO: Time 0:00:00.153176 taken by HostExecuteApplicationDataSpecification\n",
      "Expanding Synapses\n",
      "|0%                          50%                         100%|\n",
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      "2023-03-17 10:23:47 INFO: Time 0:00:00.536582 taken by SynapseExpander\n",
      "Writing fixed route report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:47 INFO: Time 0:00:00.045482 taken by FixedRouteFromMachineReport\n",
      "Loading executables onto the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:58 INFO: Time 0:00:10.299407 taken by LoadApplicationExecutableImages\n",
      "Reading Routing Tables from Machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:58 INFO: Time 0:00:00.013404 taken by RoutingTableFromMachineReport\n",
      "Generating compressed router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:58 INFO: Time 0:00:00.007995 taken by compressedRoutingTableReports\n",
      "Generating comparison of router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:58 INFO: Time 0:00:00.008590 taken by comparisonOfRoutingTablesReport\n",
      "2023-03-17 10:23:58 INFO: Running for 1 steps for a total of 125.0ms\n",
      "2023-03-17 10:23:58 INFO: Run 1 of 1\n",
      "Generating SDRAM usage report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:58 INFO: Time 0:00:00.063345 taken by SdramUsageReportPerChip\n",
      "Updating run time\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:58 INFO: Time 0:00:00.026101 taken by ChipRuntimeUpdater\n",
      "2023-03-17 10:23:58 INFO: creating live event connection database in /home/spinnaker/CNT-2023/SpiNNaker/application_generated_data_files/2023-03-17-10-23-03-783341/run_1/input_output_database.db\n",
      "Creating graph description database\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:58 INFO: Time 0:00:00.057033 taken by DatabaseInterface\n",
      "2023-03-17 10:23:58 INFO: ** Notifying external sources that the database is ready for reading **\n",
      "2023-03-17 10:23:58 INFO: Time 0:00:00.001215 taken by NotificationProtocol\n",
      "2023-03-17 10:23:58 INFO: *** Running simulation... *** \n",
      "Loading buffers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:58 INFO: ** Awaiting for a response from an external source to state its ready for the simulation to start **\n",
      "2023-03-17 10:23:58 INFO: ** Sending start / resume message to external sources to state the simulation has started or resumed. **\n",
      "2023-03-17 10:23:58 INFO: ** Awaiting for a response from an external source to state its ready for the simulation to start **\n",
      "2023-03-17 10:23:58 INFO: Application started; waiting 0.225s for it to stop\n",
      "2023-03-17 10:23:58 INFO: ** Sending pause / stop message to external sources to state the simulation has been paused or stopped. **\n",
      "2023-03-17 10:23:58 INFO: Time 0:00:00.312231 taken by ApplicationRunner\n",
      "Extracting buffers from the last run\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:58 INFO: Time 0:00:00.129119 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",
      "2023-03-17 10:23:58 INFO: Time 0:00:00.083629 taken by GraphProvenanceGatherer\n",
      "Getting provenance data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:58 INFO: Time 0:00:00.030594 taken by PlacementsProvenanceGatherer\n",
      "Getting Router Provenance\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:58 INFO: Time 0:00:00.064264 taken by RouterProvenanceGatherer\n",
      "Getting profile data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:23:58 INFO: Time 0:00:00.027804 taken by ProfileDataGatherer\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "125.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.run(simtime) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting spikes for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for SpikeSourceArrayVertex_2\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for SpikeSourceArrayVertex_2\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for SpikeSourceArrayVertex_2\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for SpikeSourceArrayVertex_2\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for SpikeSourceArrayVertex_2\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for SpikeSourceArrayVertex_3\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for SpikeSourceArrayVertex_3\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for SpikeSourceArrayVertex_3\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for SpikeSourceArrayVertex_3\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for SpikeSourceArrayVertex_3\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n"
     ]
    }
   ],
   "source": [
    "# save the results\n",
    "\n",
    "outputs = {}\n",
    "\n",
    "for syn in ['pre', 'post', 'thalamus-pre', 'thalamus-post']:\n",
    "    \n",
    "    outputs[syn] = pops[syn].get_data()\n",
    "    \n",
    "    for recording in ['v', 'gsyn_inh', 'gsyn_exc', 'spikes']:\n",
    "        pops[syn].write_data(fileName + '_' + str(recording) + '.pkl')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## recover results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v\n",
      "gsyn_exc\n",
      "gsyn_inh\n",
      "v\n",
      "gsyn_exc\n",
      "gsyn_inh\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys([('pre', 'v'), ('pre', 'gsyn_exc'), ('pre', 'gsyn_inh'), ('pre', 'spikes'), ('post', 'v'), ('post', 'gsyn_exc'), ('post', 'gsyn_inh'), ('post', 'spikes'), ('thalamus-pre', 'spikes'), ('thalamus-post', 'spikes')])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make the recover results function\n",
    "def recover_results(outputs):\n",
    "    results = {}\n",
    "    for key in outputs.keys(): \n",
    "        \n",
    "        # to get voltage and conductances\n",
    "        for analogsignal in outputs[key].segments[0].analogsignals:\n",
    "            print(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",
    "# recover results\n",
    "results = recover_results(outputs)\n",
    "results.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## check the spikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(27.125, 0.5, 'cells ID')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,1)\n",
    "fig.tight_layout(pad=5)\n",
    "axes_list = fig.axes\n",
    "\n",
    "\n",
    "axes_list[0].eventplot(results['thalamus-pre', 'spikes'], label='thalamus-pre', color='r')\n",
    "axes_list[0].eventplot(results['thalamus-post', 'spikes'], label='thalamus-post', color='b')\n",
    "axes_list[0].set_title('thalamic spikes')\n",
    "axes_list[0].set_xlabel('[ms]')\n",
    "axes_list[0].set_ylabel('cells ID')\n",
    "axes_list[0].set_xlim(0,simtime)\n",
    "#axes_list[0].set_ylim(0,2)\n",
    "axes_list[0].legend()\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(1,1)\n",
    "fig.tight_layout(pad=5)\n",
    "axes_list = fig.axes\n",
    "axes_list[0].eventplot(results['pre', 'spikes'], label='pre', color='k')\n",
    "axes_list[0].eventplot(results['post', 'spikes'], label='post', color='g')\n",
    "axes_list[0].set_xlabel('[ms]')\n",
    "axes_list[0].set_title('cells spikes')\n",
    "axes_list[0].set_xlim(0,simtime)\n",
    "axes_list[0].legend()\n",
    "axes_list[0].set_xlabel('[ms]')\n",
    "axes_list[0].set_ylabel('cells ID')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                     Version\n",
      "--------------------------- ---------\n",
      "aedat                       1.0.1\n",
      "appdirs                     1.4.4\n",
      "attrs                       19.3.0\n",
      "backcall                    0.2.0\n",
      "bleach                      3.1.5\n",
      "Brian2                      2.4.2\n",
      "cached-property             1.5.2\n",
      "certifi                     2020.6.20\n",
      "chardet                     3.0.4\n",
      "csa                         0.1.12\n",
      "cycler                      0.10.0\n",
      "Cython                      0.29.33\n",
      "decorator                   4.4.2\n",
      "defusedxml                  0.6.0\n",
      "Deprecated                  1.2.13\n",
      "entrypoints                 0.3\n",
      "enum-compat                 0.0.3\n",
      "enum34                      1.1.10\n",
      "future                      0.18.2\n",
      "h5py                        3.1.0\n",
      "idna                        2.9\n",
      "importlib-metadata          1.6.1\n",
      "ipykernel                   5.3.0\n",
      "ipympl                      0.5.6\n",
      "ipython                     7.15.0\n",
      "ipython-genutils            0.2.0\n",
      "ipywidgets                  7.5.1\n",
      "jedi                        0.17.1\n",
      "Jinja2                      2.11.2\n",
      "joblib                      0.17.0\n",
      "jsonschema                  3.2.0\n",
      "jupyter-client              6.1.3\n",
      "jupyter-core                4.6.3\n",
      "kiwisolver                  1.2.0\n",
      "lazyarray                   0.3.3\n",
      "loris                       0.5.0\n",
      "lxml                        4.5.1\n",
      "MarkupSafe                  1.1.1\n",
      "matplotlib                  3.2.2\n",
      "mistune                     0.8.4\n",
      "mne                         0.23.4\n",
      "mpmath                      1.2.1\n",
      "nbconvert                   5.6.1\n",
      "nbformat                    5.0.7\n",
      "neo                         0.6.1\n",
      "nixio                       1.4.9\n",
      "notebook                    6.0.3\n",
      "numpy                       1.19.0\n",
      "packaging                   21.3\n",
      "pandas                      1.1.3\n",
      "pandocfilters               1.4.2\n",
      "parso                       0.7.0\n",
      "patsy                       0.5.1\n",
      "pexpect                     4.8.0\n",
      "pickleshare                 0.7.5\n",
      "pip                         20.1.1\n",
      "prometheus-client           0.8.0\n",
      "prompt-toolkit              3.0.5\n",
      "psutil                      5.9.0\n",
      "ptyprocess                  0.6.0\n",
      "pyemd                       0.5.1\n",
      "Pygments                    2.6.1\n",
      "pymongo                     4.0.1\n",
      "PyNN                        0.9.4\n",
      "pyparsing                   2.4.7\n",
      "pyphi                       1.2.0\n",
      "pyrsistent                  0.16.0\n",
      "python-dateutil             2.8.1\n",
      "pytz                        2020.1\n",
      "PyYAML                      6.0\n",
      "pyzmq                       19.0.1\n",
      "quantities                  0.12.4\n",
      "redis                       4.1.0\n",
      "requests                    2.24.0\n",
      "scikit-learn                0.23.2\n",
      "scipy                       1.5.0\n",
      "seaborn                     0.11.0\n",
      "Send2Trash                  1.5.0\n",
      "setuptools                  47.3.1\n",
      "six                         1.15.0\n",
      "sklearn                     0.0\n",
      "sortedcollections           1.2.1\n",
      "sortedcontainers            2.2.2\n",
      "spalloc                     2.0.1\n",
      "SpiNNaker-DataSpecification 1!5.1.0\n",
      "SpiNNaker-PACMAN            1!5.1.0\n",
      "SpiNNFrontEndCommon         1!5.1.0\n",
      "SpiNNMachine                1!5.1.0\n",
      "SpiNNMan                    1!5.1.0\n",
      "SpiNNStorageHandlers        1!5.1.0\n",
      "SpiNNUtilities              1!5.1.0\n",
      "sPyNNaker                   1!5.1.0\n",
      "sPyNNaker8                  1!5.1.0\n",
      "statsmodels                 0.12.2\n",
      "sympy                       1.9\n",
      "tblib                       1.7.0\n",
      "terminado                   0.8.3\n",
      "testpath                    0.4.4\n",
      "threadpoolctl               2.1.0\n",
      "tonic                       0.2.2\n",
      "tornado                     6.0.4\n",
      "tqdm                        4.54.1\n",
      "traitlets                   4.3.3\n",
      "tzlocal                     2.1\n",
      "urllib3                     1.25.9\n",
      "utils                       1.0.1\n",
      "watermark                   2.0.2\n",
      "wcwidth                     0.2.4\n",
      "webencodings                0.5.1\n",
      "wheel                       0.34.2\n",
      "widgetsnbextension          3.5.1\n",
      "wrapt                       1.13.3\n",
      "zipp                        3.1.0\n",
      "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 21.3.1 is available.\n",
      "You should consider upgrading via the '/home/spinnaker/sPyNNaker/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## check the voltage signature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, sharex=True, sharey=False)#, figsize=(11,7))\n",
    "fig.tight_layout(pad=3)\n",
    "fig.suptitle('voltage signature in pre and post synaptic cells')\n",
    "\n",
    "axes_list = fig.axes\n",
    "for idx, syn in enumerate(['pre', 'post']):\n",
    "    axes_list[idx].plot(results[syn, 'v'], label=str(syn))\n",
    "    axes_list[idx].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, sharex=True, sharey=False)#, figsize=(11,7))\n",
    "fig.tight_layout(pad=3)\n",
    "fig.suptitle('gsyn_exc signature in pre and post synaptic cells')\n",
    "\n",
    "axes_list = fig.axes\n",
    "idx = 0\n",
    "for idx, syn in enumerate(['pre', 'post']):\n",
    "    axes_list[idx].plot(results[syn, 'gsyn_exc'], label=str(syn))\n",
    "    axes_list[idx].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mZjnnIDAzyzkHgZlZzjkIzMxyzkFgZpZzDgIzs5xzEJiZ5ZyDwMws5xwEZmY55yAwM8u5qgSBpCmSVkhqlXReieG7SroxDX9QUlPBsPNT/xWSPlyN+piZWfkqDgJJg4DLgBOA8cCpksYXFTsT2BwRY4BLgYvTuOOBGcAEYApweZqemZntJNU4IzgCaI2IVRHxCnADMK2ozDTg6vR+AXCsJKX+N0TEyxHxBNCapmdmZjtJNR5Msx+wtqC7DTiyszIR8ZqkLUBD6v9A0bj7VaFOJX3jV8tY/tTzvTV5M7NeNX7f3bnwIxOqPt1+c7FY0lmSWiS1DJTHyZmZ9QXVOCNYB4ws6G5M/UqVaZNUBwwD2sscF4CImAvMBWhubt6hJ0z3RpKamfV31TgjWAKMlTRa0i5kF38XFpVZCMxK76cDd0dEpP4z0l1Fo4GxwOIq1MnMzMpU8RlBavP/PHA7MAj4aUQsk3QR0BIRC4F5wLWSWoFNZGFBKncTsBx4DfhcRLxeaZ3MzKx8yr6Y9y/Nzc3R0tJS62qYmfUrkpZGRHNx/35zsdjMzHqHg8DMLOccBGZmOecgMDPLOQeBmVnOOQjMzHLOQWBmlnMOAjOznHMQmJnlnIPAzCznHARmZjnnIDAzyzkHgZlZzjkIzMxyzkFgZpZzDgIzs5xzEJiZ5ZyDwMws5xwEZmY55yAwM8s5B4GZWc45CMzMcs5BYGaWcw4CM7OccxCYmeWcg8DMLOccBGZmOecgMDPLOQeBmVnOVRQEkvaUdKekx9PfPTopNyuVeVzSrIL+90haIenP6fXOSupjZmY9V+kZwXnAoogYCyxK3duRtCdwIXAkcARwYVFgzIyIQ9Lr2QrrY2ZmPVRpEEwDrk7vrwY+WqLMh4E7I2JTRGwG7gSmVDhfMzOrkkqDYO+IWJ/ePw3sXaLMfsDagu621K/DlalZ6D8kqcL6mJlZD9V1V0DSXcC7Sgy6oLAjIkJS9HD+MyNinaShwC3A6cA1ndTjLOAsgFGjRvVwNmZm1plugyAijutsmKRnJO0TEesl7QOUauNfBxxT0N0I3JOmvS793SppPtk1hJJBEBFzgbkAzc3NPQ0cMzPrRKVNQwuBjruAZgG/LFHmduBDkvZIF4k/BNwuqU7SCABJg4GpwCMV1sfMzHpIETv+5VpSA3ATMAp4EvhERGyS1AzMiYhPp3Kzga+m0b4VEVdKGgLcBwwGBgF3AedExOtlzHdDmt+OGAFs3MFx+wovQ9/gZegbvAzl2z8i9iruWVEQ9EeSWiKiudb1qISXoW/wMvQNXobK+T+LzcxyzkFgZpZzeQyCubWuQBV4GfoGL0Pf4GWoUO6uEZiZ2fbyeEZgZmYFHARmZjnnIDAzyzkHgZlZzjkIzMxyzkFgZpZzDgIzs5xzEJiZ5ZyDwMws5xwEZmY55yAwM8s5B4GZWc45CMzMcs5BYGaWcw4CM7OccxCYmeWcg8DMLOccBGZmOecgMDPLOQeBmVnOOQjMzHLOQWBmlnMOAjOznHMQmJnlnIPAzCznHARmZjnnIDAzyzkHgZlZzjkIzMxyzkFgZpZzDgIzs5xzEJiZ5ZyDwMws5xwEZmY5V1frCuyIESNGRFNTU62rYWbWryxdunRjROxV3L8qQSBpCvB9YBDwk4j4dtHwXYFrgMOBduCUiFgtqQl4FFiRij4QEXO6m19TUxMtLS3VqLqZWW5IerJU/4qDQNIg4DLgeKANWCJpYUQsLyh2JrA5IsZImgFcDJyShq2MiEMqrYeZme2YalwjOAJojYhVEfEKcAMwrajMNODq9H4BcKwkVWHeZmZWoWoEwX7A2oLuttSvZJmIeA3YAjSkYaMlPSzpXknv62wmks6S1CKpZcOGDVWotpmZQe0vFq8HRkVEu6TDgVslTYiI54sLRsRcYC5Ac3Nz7OR6mtkA8eqrr9LW1sa2bdtqXZVeU19fT2NjI4MHDy6rfDWCYB0wsqC7MfUrVaZNUh0wDGiPiABeBoiIpZJWAgcAvhJsZr2ira2NoUOH0tTUxEBsoY4I2tvbaWtrY/To0WWNU42moSXAWEmjJe0CzAAWFpVZCMxK76cDd0dESNorXWxG0ruBscCqKtTJzKykbdu20dDQMCBDAEASDQ0NPTrjqfiMICJek/R54Hay20d/GhHLJF0EtETEQmAecK2kVmATWVgAvB+4SNKrwN+BORGxqdI6mZl1ZaCGQIeeLl9VrhFExK+BXxf1+1rB+23AySXGuwW4pRp1MDOzHeOfmDAz64duvfVWli9f3n3BMjgIzMz6IQeBmVk/tnr1ag466CBmzpzJuHHjmD59Oi+99BKLFi3i0EMP5eCDD2b27Nm8/PLLAJx33nmMHz+eiRMn8sUvfpE//elPLFy4kC996UsccsghrFy5sqL61Pr/CMzMauYbv1rG8qfe8m9LFRm/7+5c+JEJ3ZZbsWIF8+bNY/LkycyePZtLLrmEK664gkWLFnHAAQfwqU99ih/96Eecfvrp/OIXv+Cxxx5DEs899xzDhw/npJNOYurUqUyfPr3iOvuMwMysBkaOHMnkyZMBOO2001i0aBGjR4/mgAMOAGDWrFncd999DBs2jPr6es4880x+/vOfs9tuu1W9Lj4jMLPcKuebe28pvsVz+PDhtLe3v6VcXV0dixcvZtGiRSxYsIAf/vCH3H333VWti88IzMxqYM2aNdx///0AzJ8/n+bmZlavXk1raysA1157LR/4wAd44YUX2LJlCyeeeCKXXnopf/nLXwAYOnQoW7durUpdHARmZjVw4IEHctlllzFu3Dg2b97MF77wBa688kpOPvlkDj74YN72trcxZ84ctm7dytSpU5k4cSJHH300l1xyCQAzZszgO9/5DoceeqgvFpuZ9Ud1dXVcd9112/U79thjefjhh7frt88++7B48eK3jD958mTfPmpmZtXhIDAz28mampp45JFHal2NNzgIzCx3sl/AH7h6unwOAjPLlfr6etrb2wdsGHQ8j6C+vr7scXyx2MxypbGxkba2NgbyI287nlBWLgeBmeXK4MGDy35yV164acjMLOccBGZmOecgMDPLOQeBmVnOOQjMzHLOQWBmlnMOAjOznHMQmJnlnIPAzCznHARmZjnnIDAzyzkHgZlZzjkIzMxyzkFgZpZzVQkCSVMkrZDUKum8EsN3lXRjGv6gpKaCYeen/iskfbga9TEzs/JVHASSBgGXAScA44FTJY0vKnYmsDkixgCXAhencccDM4AJwBTg8jQ9MzPbSarxYJojgNaIWAUg6QZgGrC8oMw04Ovp/QLgh5KU+t8QES8DT0hqTdO7vwr1eotv/GoZy596vjcmbWbW68bvuzsXfmRC1adbjaah/YC1Bd1tqV/JMhHxGrAFaChzXAAknSWpRVLLQH7EnJnZztZvHlUZEXOBuQDNzc079NTp3khSM7P+rhpnBOuAkQXdjalfyTKS6oBhQHuZ45qZWS+qRhAsAcZKGi1pF7KLvwuLyiwEZqX304G7IyJS/xnprqLRwFhgcRXqZGZmZaq4aSgiXpP0eeB2YBDw04hYJukioCUiFgLzgGvTxeBNZGFBKncT2YXl14DPRcTrldbJzMzKp+yLef/S3NwcLS0tta6GmVm/ImlpRDQX9/d/FpuZ5ZyDwMws5xwEZmY55yAwM8s5B4GZWc45CMzMcs5BYGaWcw4CM7OccxCYmeWcg8DMLOccBGZmOecgMDPLOQeBmVnOOQjMzHLOQWBmlnMOAjOznHMQmJnlnIPAzCznHARmZjnnIDAzyzkHgZlZzjkIzMxyzkFgZpZzDgIzs5xzEJiZ5ZyDwMws5xwEZmY55yAwM8s5B4GZWc5VFASS9pR0p6TH0989Oik3K5V5XNKsgv73SFoh6c/p9c5K6mNmZj1X6RnBecCiiBgLLErd25G0J3AhcCRwBHBhUWDMjIhD0uvZCutjZmY9VGkQTAOuTu+vBj5aosyHgTsjYlNEbAbuBKZUOF8zM6uSSoNg74hYn94/Dexdosx+wNqC7rbUr8OVqVnoPySpsxlJOktSi6SWDRs2VFhtMzPrUNddAUl3Ae8qMeiCwo6ICEnRw/nPjIh1koYCtwCnA9eUKhgRc4G5AM3NzT2dj5mZdaLbIIiI4zobJukZSftExHpJ+wCl2vjXAccUdDcC96Rpr0t/t0qaT3YNoWQQmJlZ76i0aWgh0HEX0CzglyXK3A58SNIe6SLxh4DbJdVJGgEgaTAwFXikwvqYmVkPKWLHW1kkNQA3AaOAJ4FPRMQmSc3AnIj4dCo3G/hqGu1bEXGlpCHAfcBgYBBwF3BORLxexnw3pPntiBHAxh0ct6/wMvQNXoa+wctQvv0jYq/inhUFQX8kqSUimmtdj0p4GfoGL0Pf4GWonP+z2Mws5xwEZmY5l8cgmFvrClSBl6Fv8DL0DV6GCuXuGoGZmW0vj2cEZmZWIDdBIGlK+qXTVklv+XG8vkjSSEm/k7Rc0jJJZ6f+Zf3qa18iaZCkhyXdlrpHS3owbY8bJe1S6zp2R9JwSQskPSbpUUmT+tu2kPSFtC89Iul6SfV9fVtI+qmkZyU9UtCv5HpX5gdpWf4q6bDa1fxNnSzDd9K+9FdJv5A0vGDY+WkZVkj6cG/XLxdBIGkQcBlwAjAeOFXS+NrWqiyvAedGxHjgKOBzqd7d/uprH3Q28GhB98XApRExBtgMnFmTWvXM94HfRsRBwD+QLU+/2RaS9gP+J9AcEe8h+/+dGfT9bXEVb/2hys7W+wnA2PQ6C/jRTqpjd67irctwJ/CeiJgI/A04HyAd4zOACWmcy9NnWK/JRRCQ/XRFa0SsiohXgBvIfjm1T4uI9RHxUHq/leyDZz/K+9XXPkNSI/DPwE9St4APAgtSkf6wDMOA9wPzACLilYh4jn62Lch+VubtkuqA3YD19PFtERH3AZuKene23qcB10TmAWB4+vmbmiq1DBFxR0S8ljofIPv5HciW4YaIeDkingBayT7Dek1egqC7X0Dt8yQ1AYcCD1Ler772Jd8Dvgz8PXU3AM8VHAT9YXuMBjaQ/Vruw5J+kv47vt9si/TbXt8F1pAFwBZgKf1vW0Dn672/Huuzgd+k9zt9GfISBP2apHeQ/Trrv0XE84XDIrvtq8/e+iVpKvBsRCytdV0qVAccBvwoIg4FXqSoGagfbIs9yL5tjgb2BYYwAJ4N0tfXe3ckXUDWDPyzWtUhL0GwDhhZ0N2Y+vV56Qf5bgF+FhE/T72f6Tjd7eJXX/uKycBJklaTNcl9kKytfXhqnoD+sT3agLaIeDB1LyALhv60LY4DnoiIDRHxKvBzsu3T37YFdL7e+9WxLukMsh/cnBlv3su/05chL0GwBBib7o7YhexCzMIa16lbqS19HvBoRFxSMKicX33tEyLi/IhojIgmsvV+d0TMBH4HTE/F+vQyAETE08BaSQemXscCy+lH24KsSegoSbulfatjGfrVtkg6W+8LgU+lu4eOArYUNCH1KZKmkDWZnhQRLxUMWgjMkLSrpNFkF74X92plIiIXL+BEsivzK4ELal2fMut8NNkp71+BP6fXiWRt7IuAx8l+tXXPWte1zOU5BrgtvX932rlbgZuBXWtdvzLqfwjQkrbHrcAe/W1bAN8AHiP7yfdrgV37+rYArie7pvEq2ZnZmZ2td0BkdwiuBP4v2R1SfXUZWsmuBXQc2z8uKH9BWoYVwAm9XT//Z7GZWc7lpWnIzMw64SAwM8s5B4GZWc45CMzMcs5BYGaWcw4CM7OccxCYmeWcg8DMLOf+P9Wwf4YmYdf5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, sharex=True, sharey=False)#, figsize=(11,7))\n",
    "fig.tight_layout(pad=3)\n",
    "fig.suptitle('gsyn_inh signature in pre and post synaptic cells')\n",
    "\n",
    "axes_list = fig.axes\n",
    "idx = 0\n",
    "for idx, syn in enumerate(['pre', 'post']):\n",
    "    axes_list[idx].plot(results[syn, 'gsyn_inh'], label=str(syn))\n",
    "    axes_list[idx].legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## check the STDP between pre and post cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-17 10:24:01 WARNING: getWeights is deprecated.  Use get('weight') instead\n",
      "Getting ['weight']s for projection between network and network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting weight pre stdp:  0.1  [uS]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([5.])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "projs.keys()\n",
    "#projs['pre', 'post'].get(['source', 'target', 'weight', 'delay'], \"list\")\n",
    "print('starting weight pre stdp: ', starting_weight, ' [uS]')\n",
    "projs['pre', 'post'].getWeights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-17 10:24:01 WARNING: getWeights is deprecated.  Use get('weight') instead\n",
      "Getting ['weight']s for projection between network and network1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final weight after stdp:  [5.]  [uS]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print('final weight after stdp: ', projs['pre', 'post'].getWeights(), ' [uS]')\n",
    "\n",
    "# the weight between pre and post neuron was set to zero before the learning, now it's .."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## end the simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.end()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# task1: on thalamus input spike times\n",
    "- test different interspike timing <br>\n",
    "  e.g., <br>\n",
    "    a=np.arange(0,100,10) for the thalamus-pre <br>\n",
    "    a+15 for the thalamus post <br>\n",
    "    \n",
    "- test only one spike source eliciting the pre synaptic cell<br>\n",
    "  remember to set the starting weight > 0, otherwise the post synaptic doesn't learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# task2: on STDP mechanism\n",
    "- try to change the parameter inside the SpikePairRule\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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