{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ND653384\\AppData\\Local\\Temp\\1\\ipykernel_16400\\3018936475.py:30: RuntimeWarning: overflow encountered in exp\n",
      "  sec.g_pas = 1e-5 * np.exp(distance / 100)  # setting passive conductance (S/cm2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Created by Namit Dwivedi (Scimemi Lab) on 09/19/2024\n",
    "# Please contact Namit (namitdwivedi08@gmail.com) or Dr Annalisa Scimemi (scimemia@gmail.com) for questions\n",
    "# The goal of this code is reproduce voltage escape errors during somatic voltage clamp recordings\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from neuron import h\n",
    "\n",
    "output_file_path = 'C:/Users/ND653384/Documents/Namit/Voltage_escape'\n",
    "\n",
    "# Path to the NRN-EZ file containing the location of each synaptic input\n",
    "syn_loc_file = 'C:/Users/ND653384/Documents/Namit/Voltage_escape/nrnez_2024_07_31_15_22_03/run_1/Inhibitory/syn_loc.dat'\n",
    "\n",
    "\n",
    "def calculate_distance(section):\n",
    "    h.distance(sec=h.soma[0])                           # Set soma as reference for making distance measures\n",
    "    return h.distance(0.5, sec=section)\n",
    "\n",
    "def run_simulation(section_type, section_number, syn_tau1, syn_tau2):\n",
    "    # Load morphology and hoc files\n",
    "    h.load_file('C:/Users/ND653384/Documents/Namit/Voltage_escape/CA1PC.nrn')\n",
    "\n",
    "    soma = h.soma[0]\n",
    "    axon = h.axon\n",
    "\n",
    "    # Create empty lists to store: (i) the identifier of the synaptic input locations from NRN-EZ; (ii) the voltage recordings from the selected synapses; (iii) distance-to-soma for each synaptic input\n",
    "    target_sections = []\n",
    "    recorders = []\n",
    "    distances = []          \n",
    "\n",
    "    # Set axial resistance for all sections (optional)\n",
    "    for sec in h.allsec():\n",
    "        sec.Ra = 150                                    # ohm.cm\n",
    "\n",
    "    # Introduce passive channels in each section \n",
    "    for sec in h.allsec():\n",
    "        sec.insert('pas')                               # Insert passive channel\n",
    "        distance = calculate_distance(sec)\n",
    "        sec.g_pas = 1e-5 * np.exp(distance / 100)       # Set leak conductance (S/cm2)\n",
    "        sec.e_pas = 0                                   # Set leak reversal potential\n",
    "\n",
    "    # Access the target section by its number\n",
    "    if section_type == 3:\n",
    "        target_section = h.apical[section_number]\n",
    "    elif section_type == 0:\n",
    "        target_section = h.soma[section_number]\n",
    "\n",
    "    # Calculate and store distance\n",
    "    distance = calculate_distance(target_section)\n",
    "    distances.append(distance)\n",
    "\n",
    "    # Record membrane potential\n",
    "    rec = h.Vector()\n",
    "    rec.record(target_section(0.5)._ref_v)\n",
    "    recorders.append(rec)\n",
    "    target_sections.append(target_section)\n",
    "\n",
    "    # Access the soma\n",
    "    soma = h.soma[0]\n",
    "    soma_rec = h.Vector()\n",
    "    soma_rec.record(h.soma[0](0.5)._ref_v)\n",
    "\n",
    "    # Create SEClamp \n",
    "    seclamp1 = h.SEClamp(0.5, sec=soma)\n",
    "    seclamp1.dur1 = 1e9\n",
    "    seclamp1.amp1 = 0\n",
    "    seclamp1.rs = 12\n",
    "\n",
    "    t = h.Vector().record(h._ref_t)\n",
    "\n",
    "    # Parameters for the inhibitory synapses\n",
    "    syn_e = -70                                         # Set GABA reversal potential\n",
    "\n",
    "    # List to hold synapses and netstims\n",
    "    synapses = []                                       # Store the inhibitory synapses created for each selected section\n",
    "    netstims = []                                       # Store the NetStims used to trigger the synaptic events\n",
    "    netcons = []                                        # Store the NetCons (connections between NetStim and synapse)\n",
    "    syn_voltage = []                                    # Store the voltage recordings of the synapse for each section\n",
    "    syn_currents = []                                   # Store the synaptic current values recorded for each synapse\n",
    "\n",
    "    for target_section in target_sections:\n",
    "        syn = h.Exp2Syn(0.5, sec=target_section)\n",
    "        syn.tau1 = syn_tau1\n",
    "        syn.tau2 = syn_tau2\n",
    "        syn.e = syn_e\n",
    "\n",
    "        stim = h.NetStim()\n",
    "        stim.number = 1\n",
    "        stim.start = 100\n",
    "        stim.interval = 0\n",
    "\n",
    "        nc = h.NetCon(stim, syn)\n",
    "        nc.weight[0] = 1.7e-3\n",
    "\n",
    "        syn_current = h.Vector()\n",
    "        syn_current.record(syn._ref_i)\n",
    "\n",
    "        synapses.append(syn)\n",
    "        netstims.append(stim)\n",
    "        netcons.append(nc)\n",
    "        syn_currents.append(syn_current)\n",
    "        syn_voltage.append(rec)\n",
    "\n",
    "    h.load_file(\"stdrun.hoc\")\n",
    "    h.finitialize(0)\n",
    "    h.continuerun(300)\n",
    "\n",
    "    soma_current = h.Vector()\n",
    "    soma_current.record(seclamp1._ref_i)\n",
    "\n",
    "    h.finitialize(0)\n",
    "    h.continuerun(300)\n",
    "\n",
    "    soma_current_pA = np.array(soma_current) * 1000\n",
    "    soma_peak = max(soma_current_pA)\n",
    "    soma_peak_time = t[soma_current_pA.argmax()]\n",
    "\n",
    "    syn_peaks = []\n",
    "    syn_peak_times = []\n",
    "    for syn_current in syn_currents:\n",
    "        syn_current_pA = np.array(syn_current) * 1000\n",
    "        positive_syn_current = syn_current_pA[syn_current_pA > 0]\n",
    "        syn_peak = max(positive_syn_current) if len(positive_syn_current) > 0 else 0\n",
    "        syn_peak_time = t[syn_current_pA.argmax()] if len(positive_syn_current) > 0 else 0\n",
    "        syn_peaks.append(syn_peak)\n",
    "        syn_peak_times.append(syn_peak_time)\n",
    "\n",
    "    return soma_peak, soma_peak_time, syn_peaks, syn_peak_times, distances\n",
    "\n",
    "def save_results(file_name, all_syn_peaks, soma_peaks, ratios, distances):\n",
    "    with open(file_name, 'w') as f:\n",
    "        f.write(\"Synaptic Peaks, Soma Peaks, Ratios, Distances\\n\")\n",
    "        for syn_peak, soma_peak, ratio, distance in zip(all_syn_peaks, soma_peaks, ratios, distances):\n",
    "            f.write(f\"{syn_peak}, {soma_peak}, {ratio}, {distance}\\n\")\n",
    "\n",
    "# Define rise and tau_decay for each simulation scenario (3 in our case)\n",
    "kinetics = [\n",
    "    (6, 18),\n",
    "    (3, 30),\n",
    "    (0.4, 4)\n",
    "]\n",
    "\n",
    "\n",
    "\n",
    "# Read synapse location data\n",
    "with open(syn_loc_file, 'r') as file:\n",
    "    synapse_data = [line.strip().split() for line in file.readlines()]\n",
    "\n",
    "\n",
    "os.makedirs(output_file_path, exist_ok=True)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Loop over each pair of kinetics\n",
    "for syn_tau1, syn_tau2 in kinetics:\n",
    "    soma_peaks = []\n",
    "    soma_peak_times = []\n",
    "    all_syn_peaks = []\n",
    "    all_syn_peak_times = []\n",
    "    ratios = []\n",
    "    all_distances = []\n",
    "\n",
    "    for section_type, section_number in synapse_data:\n",
    "        section_type = int(section_type)\n",
    "        section_number = int(section_number)\n",
    "\n",
    "        if section_type == 3 or section_type == 0:      # \"Process\" both apical and soma sections\n",
    "            soma_peak, soma_peak_time, syn_peaks, syn_peak_times, distances = run_simulation(section_type, section_number, syn_tau1, syn_tau2)\n",
    "            soma_peaks.append(soma_peak)\n",
    "            soma_peak_times.append(soma_peak_time)\n",
    "            all_syn_peaks.extend(syn_peaks)\n",
    "            all_syn_peak_times.extend(syn_peak_times)\n",
    "            all_distances.extend(distances)\n",
    "\n",
    "            file_ratios = [soma_peak / syn_peak if syn_peak > 0 else 0 for syn_peak in syn_peaks]\n",
    "            ratios.extend(file_ratios)\n",
    "\n",
    "    avg_ratios = [np.mean(r) for r in ratios]\n",
    "\n",
    "    label = f'tau1={syn_tau1}, tau2={syn_tau2}'\n",
    "    plt.scatter(all_distances, avg_ratios, label=label)\n",
    "\n",
    "    # Generate the output file name\n",
    "    output_file = os.path.join(output_file_path, f'results_tau1_{syn_tau1}_tau2_{syn_tau2}.txt')\n",
    "    save_results(output_file, all_syn_peaks, soma_peaks, avg_ratios, all_distances)\n",
    "\n",
    "plt.xlabel('Distance (µm)')\n",
    "plt.ylabel('Ratio of Soma Peak to Synaptic Peak')\n",
    "plt.ylim(0.0, 1)\n",
    "plt.title('Ratio of Soma Peak to Synaptic Peak vs Distance with Different Kinetics')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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