Fig4-notebook.ipynb

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      "authorship_tag": "ABX9TyOtvWhhcjHaoY4quydBORcY",
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    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mpgl/dendrify-paper/blob/main/Fig4-notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Overview\n",
        "> *In this interactive Python notebook, we present and briefly discuss the code needed to replicate the model and the results shown in Figure 4 of the manuscript. In this example our goal is to illustrate:*\n",
        ">\n",
        "> - *More Dendrify features that allow modelling dendritic voltage-gated ion channels (VGICs) phenomenologically without using the Hodgkin-Huxley equations.*\n",
        ">\n",
        "> - *That with Dendrify it is possible to create simplified compartmental integrate and fire (I&F) models reproducing numerous biological dendritic functions.*\n",
        ">\n",
        ">\n",
        "> <hr>\n",
        ">\n",
        "> *__Note__: In case you are unfamiliar with how Python notebooks work and you would like to run or rerun the entire code, simply select one of the options bellow:*\n",
        ">\n",
        "> - **Runtime ➝ Run all**\n",
        "> - **Runtime ➝ Restart and run all**\n",
        ">\n",
        "> *located on the top menu and wait a few seconds until all cells finish running.*\n",
        "> <hr>\n",
        ">\n",
        "> *All source code of the current Dendrify version is available on* [GitHub](https://github.com/mpgl/dendrify-paper)"
      ],
      "metadata": {
        "id": "w2vBU-0DlDuj"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "s65GOCPcn4Yt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "cnUU2b6fkuRW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e90f3b74-9949-47ce-b523-91844aafce47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "#@title Install dependencies (might take a few seconds)\n",
        "import os\n",
        "if 'dendrify' in os.getcwd():\n",
        "    pass\n",
        "else:\n",
        "    !git clone https://github.com/mpgl/dendrify-paper.git --quiet\n",
        "    os.chdir('dendrify-paper')\n",
        "\n",
        "!pip install brian2 --quiet"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Imports and settings\n",
        "import brian2 as b\n",
        "from brian2.units import (ms, um, pA, nS, uS, ohm, cm, mV, uF, mvolt) # Generally a better practise than * imports\n",
        "from dendrify import Soma, Dendrite, NeuronModel\n",
        "\n",
        "b.prefs.codegen.target = 'numpy' # Improves performance significantly here\n",
        "b.start_scope()    # allows running separate simulations in the same notebook\n",
        "\n",
        "# @title Figure settings\n",
        "blue = '#005c94ff'\n",
        "green = '#338000ff'\n",
        "orange = '#ff6600ff'\n",
        "notred = '#aa0044ff'\n",
        "params = {\n",
        "          \"legend.fontsize\": 10,\n",
        "          \"legend.handlelength\": 1.5,\n",
        "          \"legend.edgecolor\": 'inherit',\n",
        "          \"legend.columnspacing\": 0.8,\n",
        "          \"legend.handletextpad\": 0.5,\n",
        "          \"axes.labelsize\": 10,\n",
        "          \"axes.titlesize\": 11, \n",
        "          \"axes.spines.right\": False,\n",
        "          \"axes.spines.top\": False,\n",
        "          \"xtick.labelsize\": 10,\n",
        "          \"ytick.labelsize\": 10,\n",
        "          'mathtext.default': 'regular',\n",
        "          'lines.markersize': 3,\n",
        "          'lines.linewidth': 1.25,\n",
        "          'grid.color': \"#d3d3d3\",\n",
        "          'text.antialiased': True,\n",
        "          'lines.antialiased': True,\n",
        "          'figure.dpi': 150,\n",
        "          'axes.prop_cycle': b.cycler(color=[blue, green, orange, notred])\n",
        "          }\n",
        "\n",
        "b.rcParams.update(params)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "V9i35CvonNS9"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Build model\n",
        "> *Let's first creat a 4-compartment toy model with active dendritic mechanism:*"
      ],
      "metadata": {
        "id": "rnDYcpS2n_Ut"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create soma\n",
        "soma = Soma('soma', model='leakyIF', length=25*um, diameter=25*um )\n",
        "\n",
        "# create trunk\n",
        "trunk = Dendrite('trunk', length=100*um, diameter=2.5*um)\n",
        "trunk.dspikes('Na', threshold=-35*mV, g_rise=34*nS, g_fall=27.2*nS)\n",
        "\n",
        "# create proximal dendrite\n",
        "prox = Dendrite('prox', length=100*um, diameter=1*um)\n",
        "prox.synapse('AMPA', pre='pathY', g=0.8*nS,  t_decay=2*ms)\n",
        "prox.synapse('NMDA', pre='pathY', g=0.8*nS,  t_decay=60*ms)\n",
        "prox.dspikes('Na', threshold=-35*mV, g_rise=15.3*nS, g_fall=12.24*nS)\n",
        "\n",
        "# create distal dendrite\n",
        "dist = Dendrite('dist', length=100*um, diameter=0.5*um)\n",
        "dist.synapse('AMPA', pre='pathX', g=0.8*nS,  t_decay=2*ms)\n",
        "dist.synapse('NMDA', pre='pathX', g=0.8*nS,  t_decay=60*ms)\n",
        "dist.dspikes('Na', threshold=-35*mV, g_rise=7*nS, g_fall=5.6*nS)\n",
        "\n",
        "# merge compartments into a  neuron model and set its basic properties\n",
        "edges = [(soma, trunk, 15*nS), (trunk, prox, 10*nS), (prox, dist, 4*nS)]\n",
        "pyr_model = NeuronModel(edges, cm=1*uF/(cm**2), gl=40*uS/(cm**2),\n",
        "                        v_rest=-70*mV, r_axial=150*ohm*cm,\n",
        "                        scale_factor=2.8, spine_factor=1.5)\n",
        "\n",
        "# set Na spikes properties\n",
        "pyr_model.dspike_properties('Na', tau_rise=0.6*ms, tau_fall=1.2*ms,\n",
        "                            refractory=5*ms, offset_fall=0.2*ms)\n",
        "\n",
        "# create a Brian NeuronGroup and link it to the neuron model\n",
        "pyr_group = b.NeuronGroup(3, model=pyr_model.equations, method='euler',\n",
        "                          threshold='V_soma > -40*mV', reset='V_soma = 40*mV',\n",
        "                          refractory=4*ms, events=pyr_model.events,\n",
        "                          namespace=pyr_model.parameters)\n",
        "pyr_model.link(pyr_group)\n",
        "\n",
        "#-------------------------dendrify workflow ends here--------------------------#"
      ],
      "metadata": {
        "id": "lBbpx0xqn-_D"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# a little trick for having somatic spikes with more realistic shape which also\n",
        "# allows the generation of backpropagating dendritic spikes\n",
        "second_reset = b.Synapses(pyr_group, pyr_group, on_pre='V_soma=-55*mV', delay=0.5*ms)\n",
        "second_reset.connect(j='i')"
      ],
      "metadata": {
        "id": "id-Hq49oFl-G"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "> *We can inspect what Dendrify did for us by simply printing the NeuronModel object we created. As we see, compared to the model shown in Fig. 2, this model is significantly more complex.*"
      ],
      "metadata": {
        "id": "bKgksCv5pKpc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(pyr_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGoaYgM0nqpw",
        "outputId": "6b8b3aed-187c-4a35-d9d4-53c33629c743"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "OBJECT TYPE:\n",
            "\n",
            "  <class 'dendrify.neuronmodel.NeuronModel'>\n",
            "\n",
            "---------------------------------------------\n",
            "\n",
            "PROPERTIES (type): \n",
            "\n",
            "→ equations (str):\n",
            "    dV_soma/dt = (gL_soma * (EL_soma-V_soma) + I_soma) / C_soma  :volt\n",
            "    I_soma = I_ext_soma + I_trunk_soma   :amp\n",
            "    I_ext_soma  :amp\n",
            "    I_trunk_soma = (V_trunk-V_soma) * g_trunk_soma  :amp\n",
            "    \n",
            "    dV_trunk/dt = (gL_trunk * (EL_trunk-V_trunk) + I_trunk) / C_trunk  :volt\n",
            "    I_trunk = I_ext_trunk + I_prox_trunk  + I_soma_trunk  + I_Na_trunk + I_Kn_trunk  :amp\n",
            "    I_ext_trunk  :amp\n",
            "    I_Na_trunk = g_Na_trunk * (E_Na-V_trunk)  :amp\n",
            "    I_Kn_trunk = g_Kn_trunk * (E_K-V_trunk)  :amp\n",
            "    dg_Na_trunk/dt = -g_Na_trunk/tau_Na  :siemens\n",
            "    dg_Kn_trunk/dt = -g_Kn_trunk/tau_Kn  :siemens\n",
            "    allow_I_Na_trunk  :boolean\n",
            "    allow_I_Kn_trunk  :boolean\n",
            "    timer_Na_trunk  :second\n",
            "    I_soma_trunk = (V_soma-V_trunk) * g_soma_trunk  :amp\n",
            "    I_prox_trunk = (V_prox-V_trunk) * g_prox_trunk  :amp\n",
            "    \n",
            "    dV_prox/dt = (gL_prox * (EL_prox-V_prox) + I_prox) / C_prox  :volt\n",
            "    I_prox = I_ext_prox + I_dist_prox  + I_trunk_prox  + I_Na_prox + I_Kn_prox + I_NMDA_pathY_prox + I_AMPA_pathY_prox  :amp\n",
            "    I_ext_prox  :amp\n",
            "    I_AMPA_pathY_prox = g_AMPA_pathY_prox * (E_AMPA-V_prox) * s_AMPA_pathY_prox * w_AMPA_pathY_prox  :amp\n",
            "    ds_AMPA_pathY_prox/dt = -s_AMPA_pathY_prox / t_AMPA_decay_pathY_prox  :1\n",
            "    I_NMDA_pathY_prox = g_NMDA_pathY_prox * (E_NMDA-V_prox) * s_NMDA_pathY_prox / (1 + Mg * exp(-alpha*(V_prox/mV+gamma)) / beta) * w_NMDA_pathY_prox  :amp\n",
            "    ds_NMDA_pathY_prox/dt = -s_NMDA_pathY_prox/t_NMDA_decay_pathY_prox  :1\n",
            "    I_Na_prox = g_Na_prox * (E_Na-V_prox)  :amp\n",
            "    I_Kn_prox = g_Kn_prox * (E_K-V_prox)  :amp\n",
            "    dg_Na_prox/dt = -g_Na_prox/tau_Na  :siemens\n",
            "    dg_Kn_prox/dt = -g_Kn_prox/tau_Kn  :siemens\n",
            "    allow_I_Na_prox  :boolean\n",
            "    allow_I_Kn_prox  :boolean\n",
            "    timer_Na_prox  :second\n",
            "    I_trunk_prox = (V_trunk-V_prox) * g_trunk_prox  :amp\n",
            "    I_dist_prox = (V_dist-V_prox) * g_dist_prox  :amp\n",
            "    \n",
            "    dV_dist/dt = (gL_dist * (EL_dist-V_dist) + I_dist) / C_dist  :volt\n",
            "    I_dist = I_ext_dist + I_prox_dist  + I_Na_dist + I_Kn_dist + I_NMDA_pathX_dist + I_AMPA_pathX_dist  :amp\n",
            "    I_ext_dist  :amp\n",
            "    I_AMPA_pathX_dist = g_AMPA_pathX_dist * (E_AMPA-V_dist) * s_AMPA_pathX_dist * w_AMPA_pathX_dist  :amp\n",
            "    ds_AMPA_pathX_dist/dt = -s_AMPA_pathX_dist / t_AMPA_decay_pathX_dist  :1\n",
            "    I_NMDA_pathX_dist = g_NMDA_pathX_dist * (E_NMDA-V_dist) * s_NMDA_pathX_dist / (1 + Mg * exp(-alpha*(V_dist/mV+gamma)) / beta) * w_NMDA_pathX_dist  :amp\n",
            "    ds_NMDA_pathX_dist/dt = -s_NMDA_pathX_dist/t_NMDA_decay_pathX_dist  :1\n",
            "    I_Na_dist = g_Na_dist * (E_Na-V_dist)  :amp\n",
            "    I_Kn_dist = g_Kn_dist * (E_K-V_dist)  :amp\n",
            "    dg_Na_dist/dt = -g_Na_dist/tau_Na  :siemens\n",
            "    dg_Kn_dist/dt = -g_Kn_dist/tau_Kn  :siemens\n",
            "    allow_I_Na_dist  :boolean\n",
            "    allow_I_Kn_dist  :boolean\n",
            "    timer_Na_dist  :second\n",
            "    I_prox_dist = (V_prox-V_dist) * g_prox_dist  :amp\n",
            "\n",
            "→ parameters (dict):\n",
            "    'C_dist': 6.59734457 pF\n",
            "    'C_prox': 13.19468915 pF\n",
            "    'C_soma': 54.97787144 pF\n",
            "    'C_trunk': 32.98672286 pF\n",
            "    'EL_dist': -70. mV\n",
            "    'EL_prox': -70. mV\n",
            "    'EL_soma': -70. mV\n",
            "    'EL_trunk': -70. mV\n",
            "    'E_AMPA': 0. V\n",
            "    'E_Ca': 136. mV\n",
            "    'E_GABA': -80. mV\n",
            "    'E_K': -89. mV\n",
            "    'E_NMDA': 0. V\n",
            "    'E_Na': 70. mV\n",
            "    'Mg': 1.0\n",
            "    'Vth_Na_dist': -35. mV\n",
            "    'Vth_Na_prox': -35. mV\n",
            "    'Vth_Na_trunk': -35. mV\n",
            "    'alpha': 0.062\n",
            "    'beta': 3.57\n",
            "    'gL_dist': 263.8937829 pS\n",
            "    'gL_prox': 0.52778757 nS\n",
            "    'gL_soma': 2.19911486 nS\n",
            "    'gL_trunk': 1.31946891 nS\n",
            "    'g_AMPA_pathX_dist': 0.8 nS\n",
            "    'g_AMPA_pathY_prox': 0.8 nS\n",
            "    'g_Kn_dist_max': 5.6 nS\n",
            "    'g_Kn_prox_max': 12.24 nS\n",
            "    'g_Kn_trunk_max': 27.2 nS\n",
            "    'g_NMDA_pathX_dist': 0.8 nS\n",
            "    'g_NMDA_pathY_prox': 0.8 nS\n",
            "    'g_Na_dist_max': 7. nS\n",
            "    'g_Na_prox_max': 15.3 nS\n",
            "    'g_Na_trunk_max': 34. nS\n",
            "    'g_dist_prox': 4. nS\n",
            "    'g_prox_dist': 4. nS\n",
            "    'g_prox_trunk': 10. nS\n",
            "    'g_soma_trunk': 15. nS\n",
            "    'g_trunk_prox': 10. nS\n",
            "    'g_trunk_soma': 15. nS\n",
            "    'gamma': 0\n",
            "    'offset_Kn': 200. us\n",
            "    'refractory_Na': 5. ms\n",
            "    't_AMPA_decay_pathX_dist': 2. ms\n",
            "    't_AMPA_decay_pathY_prox': 2. ms\n",
            "    't_NMDA_decay_pathX_dist': 60. ms\n",
            "    't_NMDA_decay_pathY_prox': 60. ms\n",
            "    'tau_Kn': 1.2 ms\n",
            "    'tau_Na': 0.6 ms\n",
            "    'w_AMPA_pathX_dist': 1\n",
            "    'w_AMPA_pathY_prox': 1\n",
            "    'w_NMDA_pathX_dist': 1\n",
            "    'w_NMDA_pathY_prox': 1\n",
            "\n",
            "→ events (dict):\n",
            "    'activate_I_Na_trunk': 'V_trunk > Vth_Na_trunk and allow_I_Na_trunk and t > timer_Na_trunk + refractory_Na'\n",
            "    'activate_I_Kn_trunk': 't > (timer_Na_trunk + offset_Kn) and allow_I_Kn_trunk'\n",
            "    'activate_I_Na_prox': 'V_prox > Vth_Na_prox and allow_I_Na_prox and t > timer_Na_prox + refractory_Na'\n",
            "    'activate_I_Kn_prox': 't > (timer_Na_prox + offset_Kn) and allow_I_Kn_prox'\n",
            "    'activate_I_Na_dist': 'V_dist > Vth_Na_dist and allow_I_Na_dist and t > timer_Na_dist + refractory_Na'\n",
            "    'activate_I_Kn_dist': 't > (timer_Na_dist + offset_Kn) and allow_I_Kn_dist'\n",
            "\n",
            "---------------------------------------------\n",
            "\n",
            "USEFUL ATTRIBUTES:\n",
            "\n",
            "→ _linked_neurongroup:\n",
            "    ('pyr_group', NeuronGroup(clock=Clock(dt=100. * usecond, name='defaultclock'), when=start, order=0, name='neurongroup'))\n",
            "\n",
            "→ _extra_equations:\n",
            "    None\n",
            "\n",
            "→ _extra_params:\n",
            "    'offset_Kn': 200. us\n",
            "    'refractory_Na': 5. ms\n",
            "    'tau_Kn': 1.2 ms\n",
            "    'tau_Na': 0.6 ms\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> *Our model now looks somewhat like this:*\n",
        ">\n",
        "><p align=\"center\">\n",
        "    <img src=\"https://github.com/mpgl/dendrify-paper/blob/main/graphics/2.png?raw=true\" alt=\"model\" width=\"25%\">\n",
        "></p>\n",
        ">\n",
        "> *Let's use it to replicate the experiments shown in Figure 4.*"
      ],
      "metadata": {
        "id": "v-xa92xqr7u9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Panels b-e | dSpike initiation and propagation\n",
        "> *To test the impact of locally generated Na+ spikes on dendritic and somatic responses we simulated the application of short current injections (5 ms long pulses of rheobase intensity) to each dendritic segment and recorded the voltage responses of all compartments.*"
      ],
      "metadata": {
        "id": "DGMumiLp3dMP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# set monitors to record membrane voltages\n",
        "vars = [\"V_soma\", \"V_trunk\", \"V_prox\", \"V_dist\"]\n",
        "M = b.StateMonitor(pyr_group, vars, record=True)\n",
        "\n",
        "# first 5 ms -> no input\n",
        "b.run(5*ms)\n",
        "\n",
        "# next 5 ms -> square pulse for crossing dSpike threshold\n",
        "pyr_group.I_ext_dist[0] = 123 * pA\n",
        "pyr_group.I_ext_prox[1] = 309 * pA\n",
        "pyr_group.I_ext_trunk[2] = 560 * pA\n",
        "b.run(5*ms)\n",
        "\n",
        "# next 40 ms -> no input to let membranes return closer to Vrest\n",
        "pyr_group.I_ext_dist[0] = 0 * pA\n",
        "pyr_group.I_ext_prox[1] = 0 * pA\n",
        "pyr_group.I_ext_trunk[2] = 0 * pA\n",
        "b.run(40*ms)"
      ],
      "metadata": {
        "id": "tA2uzClBsPOJ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Plot voltage traces\n",
        "time = M.t/ms\n",
        "vs = M.V_soma/mV\n",
        "vt = M.V_trunk/mV\n",
        "vp = M.V_prox/mV\n",
        "vd = M.V_dist/mV\n",
        "\n",
        "fig, axes = b.subplots(1, 3, figsize=[6,2.5], sharex=True, sharey=True)\n",
        "ax0, ax1, ax2 = axes\n",
        "\n",
        "for id, ax in enumerate(axes):\n",
        "    for v, l in zip([vs, vt, vp, vd], ['soma', 'trunk', 'prox', 'dist']):\n",
        "        ax.plot(time, v[id], label=l)\n",
        "\n",
        "ax0.legend()\n",
        "ax0.set_title('Input \\u2192 dist')\n",
        "ax0.set_ylabel('Voltage (mV)')\n",
        "ax1.set_xlabel('Time (ms)')\n",
        "ax1.set_title('Input \\u2192 prox')\n",
        "ax2.set_title('Input \\u2192 trunk')\n",
        "fig.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "zzfQHY4Wzrji",
        "outputId": "bad27bdd-93e1-4e8d-91d1-0a189c8a0c68",
        "cellView": "form"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x375 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Plot first temporal derivatives of Voltage\n",
        "fig, axes = b.subplots(1, 2, figsize=[5, 3], sharex=True)\n",
        "ax0, ax1 = axes\n",
        "secret_ax = fig.add_subplot(111, frameon=False)\n",
        "secret_ax.tick_params(labelcolor='none', which='both', bottom=False, left=False)\n",
        "\n",
        "\n",
        "steps = b.arange(0.1, 50, 0.1)\n",
        "dvdt_dist =  b.diff(vd[0]) / (0.1)\n",
        "dvdt_prox =  b.diff(vp[1]) / (0.1)\n",
        "dvdt_trunk =  b.diff(vp[2]) / (0.1)\n",
        "dvdt_som0 =  b.diff(vs[0]) / (0.1)\n",
        "dvdt_som1 =  b.diff(vs[1]) / (0.1)\n",
        "dvdt_som2 =  b.diff(vs[2]) / (0.1)\n",
        "\n",
        "for dend, som in zip([dvdt_dist, dvdt_prox, dvdt_trunk],\n",
        "                     [dvdt_som0, dvdt_som1, dvdt_som2]):\n",
        "    ax0.plot(steps, dend, alpha=0.4, c='black')\n",
        "    ax1.plot(steps, som, alpha=0.4, c='black')\n",
        "\n",
        "ax0.set_xlim(right=23)\n",
        "ax0.set_ylabel('dV/dt (mV/ms)')\n",
        "ax0.set_title('Dendrites')\n",
        "ax1.set_title('Soma')\n",
        "secret_ax.set_xlabel('Time (ms)')\n",
        "fig.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "cellView": "form",
        "id": "1uKY0mtez2GD",
        "outputId": "9e035cea-24cf-4562-b70a-24b48d0da230"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 750x450 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> *Although model parameters were adjusted to elicit nearly identical dSpikes in all dendrites, somatic responses varied significantly, depending on the dSpike initiation site. As in real neurons, distal dSpikes became much weaker and broader as they travel towards the soma due to the dendritic filtering effect.*"
      ],
      "metadata": {
        "id": "NMPwPkIaHX3R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Panel f | Input-output curves\n",
        "> *To examine how dendritic spiking combined with local branch properties affect synaptic integration, we will activate quasi-simultaneously (train of spikes with ISI 0.1 ms) an increasing number of synapses placed on the distal and the proximal segments. We will then compared the peak amplitude of the dendritic voltage responses (Actual) to what would be obtained by a linear arithmetic sum of unitary responses (Expected).*\n",
        ">\n",
        "> *ISI = inter-spike interval"
      ],
      "metadata": {
        "id": "tSFuBfbIJBU_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> *Apply the stimulation protocol first on the distal dendrite*"
      ],
      "metadata": {
        "id": "F45anDh5sXHn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create a new Brian NeuronGroup and link it to the existing NeuronModel\n",
        "b.start_scope()    # allows running separate simulations in the same notebook\n",
        "b.defaultclock.dt = 0.05*ms    # increases simulation accuracy (default: 0.1 ms)\n",
        "Nsyn_d = 15    # 15 synapses are enough since distal dendrites are very excitable\n",
        "\n",
        "# create a Brian NeuronGroup and link it to the neuron model\n",
        "pyr_group2 = b.NeuronGroup(Nsyn_d, model=pyr_model.equations, method='euler',\n",
        "                          refractory=4*ms, events=pyr_model.events,\n",
        "                          namespace=pyr_model.parameters)\n",
        "pyr_model.link(pyr_group2)\n",
        "\n",
        "# synaptic protocol: Nsyn_d presynaptic spikes with isi of 0.1 ms commonly used\n",
        "# by experimental labs\n",
        "start = 20*ms\n",
        "isi = 0.1*ms\n",
        "spiketimes = [(start + (i*isi)) for i in range(Nsyn_d)]\n",
        "I = b.SpikeGeneratorGroup(Nsyn_d, range(Nsyn_d), spiketimes)\n",
        "\n",
        "synaptic_effect = \"s_AMPA_pathX_dist += 1.0; s_NMDA_pathX_dist += 1.0\"\n",
        "S = b.Synapses(I, pyr_group2, on_pre=synaptic_effect)\n",
        "S.connect('j >= i')"
      ],
      "metadata": {
        "id": "hlfctnReACiP"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "spiketimes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MB5me1EnIzt6",
        "outputId": "581097de-f1a4-42c9-91d5-caf0077f1f48"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[20. * msecond,\n",
              " 20.1 * msecond,\n",
              " 20.2 * msecond,\n",
              " 20.3 * msecond,\n",
              " 20.4 * msecond,\n",
              " 20.5 * msecond,\n",
              " 20.6 * msecond,\n",
              " 20.7 * msecond,\n",
              " 20.8 * msecond,\n",
              " 20.9 * msecond,\n",
              " 21. * msecond,\n",
              " 21.1 * msecond,\n",
              " 21.2 * msecond,\n",
              " 21.3 * msecond,\n",
              " 21.4 * msecond]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# set monitors to record membrane voltages and run simulation\n",
        "M = b.StateMonitor(pyr_group2, [\"V_dist\"], record=True)\n",
        "b.run(200 *ms)\n",
        "vd = M.V_dist/mV"
      ],
      "metadata": {
        "id": "mVDpGTQrEWpB"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a new Brian NeuronGroup and link it to the existing NeuronModel\n",
        "b.start_scope()    # allows running separate simulations in the same notebook\n",
        "b.defaultclock.dt = 0.05*ms    # increase the simulation accuracy  \n",
        "\n",
        "# create a Brian NeuronGroup and link it to the neuron model\n",
        "pyr_group3 = b.NeuronGroup(Nsyn_d, model=pyr_model.equations, method='euler',\n",
        "                          refractory=4*ms, events=pyr_model.events,\n",
        "                          namespace=pyr_model.parameters)\n",
        "pyr_model.link(pyr_group3, automate='v_rest')\n",
        "\n",
        "# synaptic protocol\n",
        "I = b.SpikeGeneratorGroup(Nsyn_d, range(Nsyn_d), spiketimes)\n",
        "\n",
        "synaptic_effect = \"s_AMPA_pathX_dist += 1.0; s_NMDA_pathX_dist += 1.0\"\n",
        "S = b.Synapses(I, pyr_group3, on_pre=synaptic_effect)\n",
        "S.connect('j >= i')\n",
        "\n",
        "# set monitors to record membrane voltages and run simulation\n",
        "M = b.StateMonitor(pyr_group3, [\"V_dist\"], record=True)\n",
        "b.run(200 *ms)\n",
        "vd_ttx = M.V_dist/mV"
      ],
      "metadata": {
        "id": "Q2G_QQZDyVte"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "> *Note: We are exploring ways on making Dendrify more flexible when it comes to performing multiple simulations with different parameters or mechanisms. Some of them include:*\n",
        "- *Being able to alter/remove NeuronModel mechanisms more easily.*\n",
        "- *Being able to use the same NeuronModel with multiple NeuronGroups in the same simulation (might work now but it's  not thoroughly tested).*\n",
        ">\n",
        "> *Since Dendrify is a project in active development many improvements & features will be included in future versions.*"
      ],
      "metadata": {
        "id": "9tJLqX2js9cz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Repeat process fot the proximal dendrite\n",
        "# create a new Brian NeuronGroup and link it to the existing NeuronModel\n",
        "b.start_scope()    # allows running separate simulations in the same notebook\n",
        "b.defaultclock.dt = 0.05*ms    # increases simulation accuracy (default: 0.1 ms)\n",
        "Nsyn_p = 30    # more synapses than before since prox is less excitable\n",
        "\n",
        "# create a Brian NeuronGroup and link it to the neuron model\n",
        "pyr_group4 = b.NeuronGroup(Nsyn_p, model=pyr_model.equations, method='euler',\n",
        "                          refractory=4*ms, events=pyr_model.events,\n",
        "                          namespace=pyr_model.parameters)\n",
        "pyr_model.link(pyr_group4)\n",
        "\n",
        "# synaptic protocol\n",
        "spiketimes = [(start + (i*isi)) for i in range(Nsyn_p)]\n",
        "I = b.SpikeGeneratorGroup(Nsyn_p, range(Nsyn_p), spiketimes)\n",
        "\n",
        "synaptic_effect = \"s_AMPA_pathY_prox += 1.0; s_NMDA_pathY_prox += 1.0\"\n",
        "S = b.Synapses(I, pyr_group4, on_pre=synaptic_effect)\n",
        "S.connect('j >= i')\n",
        "\n",
        "# Set monitors to record membrane voltages and run simulation\n",
        "M = b.StateMonitor(pyr_group4, [\"V_prox\"], record=True)\n",
        "b.run(200 *ms)\n",
        "vp = M.V_prox/mV\n",
        "\n",
        "################################################################################\n",
        "\n",
        "# create a new Brian NeuronGroup and link it to the existing NeuronModel\n",
        "b.start_scope()    # allows running separate simulations in the same notebook\n",
        "b.defaultclock.dt = 0.05*ms    # increases simulation accuracy (default: 0.1 ms)\n",
        "\n",
        "# create a Brian NeuronGroup and link it to the neuron model\n",
        "pyr_group5 = b.NeuronGroup(Nsyn_p, model=pyr_model.equations, method='euler',\n",
        "                          refractory=4*ms, events=pyr_model.events,\n",
        "                          namespace=pyr_model.parameters)\n",
        "pyr_model.link(pyr_group5, automate='v_rest')\n",
        "\n",
        "# synaptic protocol\n",
        "I = b.SpikeGeneratorGroup(Nsyn_p, range(Nsyn_p), spiketimes)\n",
        "\n",
        "synaptic_effect = \"s_AMPA_pathY_prox += 1.0; s_NMDA_pathY_prox += 1.0\"\n",
        "S = b.Synapses(I, pyr_group5, on_pre=synaptic_effect)\n",
        "S.connect('j >= i')\n",
        "\n",
        "# Set monitors to record membrane voltages and run simulation\n",
        "M = b.StateMonitor(pyr_group5, [\"V_prox\"], record=True)\n",
        "b.run(200 *ms)\n",
        "vp_ttx = M.V_prox/mV"
      ],
      "metadata": {
        "id": "dWkgVIVXOP0e",
        "cellView": "form"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Plot expected vs actual\n",
        "time = M.t/ms\n",
        "\n",
        "peaks_d = [max(v)+70 for v in vd]\n",
        "unitary_d = peaks_d[0]\n",
        "expected_d = b.linspace(1, Nsyn_d, Nsyn_d) * unitary_d\n",
        "\n",
        "peaks_d_ttx = [max(v)+70 for v in vd_ttx]\n",
        "unitary_d_ttx = peaks_d_ttx[0]\n",
        "expected_d_ttx = b.linspace(1, Nsyn_d, Nsyn_d) * unitary_d_ttx\n",
        "\n",
        "peaks_p = [max(v)+70 for v in vp]\n",
        "unitary_p = peaks_p[0]\n",
        "expected_p = b.linspace(1, Nsyn_p, Nsyn_p) * unitary_p\n",
        "\n",
        "peaks_p_ttx = [max(v)+70 for v in vp_ttx]\n",
        "unitary_p_ttx = peaks_p_ttx[0]\n",
        "expected_p_ttx = b.linspace(1, Nsyn_p, Nsyn_p) * unitary_p_ttx\n",
        "\n",
        "fig, axes = b.subplots(1, 2, sharex=True, sharey=True, figsize=[6,3.5])\n",
        "ax0, ax1 = axes\n",
        "\n",
        "ax0.plot(expected_d, peaks_d, \"o-\", ms=4, c='black', lw=1, mfc='#aa0044ff',\n",
        "          mec='#aa0044ff', alpha=0.5, label='control')\n",
        "ax0.plot(expected_d_ttx, peaks_d_ttx, \"x-\", ms=4, c='gray', lw=1, alpha=0.5, label='OFF')\n",
        "ax0.plot(range(75), range(75), c='#888a85ff', ls=':' )\n",
        "ax0.set_ylabel('Actual EPSP (mV)')\n",
        "ax0.set_title('distal')\n",
        "ax0.legend()\n",
        "\n",
        "ax1.plot(expected_p, peaks_p, \"o-\", ms=4, c='black', lw=1, mfc='#ff6600ff',\n",
        "          mec='#ff6600ff', alpha=0.5, label='control')\n",
        "ax1.plot(expected_p_ttx, peaks_p_ttx, \"x-\", ms=4, c='gray', lw=1, alpha=0.5, label='OFF')\n",
        "ax1.plot(range(75), range(75), c='#888a85ff', ls=':' )\n",
        "ax1.set_title('proximal')\n",
        "ax1.legend()\n",
        "\n",
        "secret_ax = fig.add_subplot(111, frameon=False)\n",
        "secret_ax.tick_params(labelcolor='none', which='both', bottom=False, left=False)\n",
        "secret_ax.set_xlabel('Expected EPSP (mV)')\n",
        "\n",
        "fig.tight_layout()"
      ],
      "metadata": {
        "id": "FoS81wgKt3jg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "cellView": "form",
        "outputId": "fdb88360-1296-4e80-9869-d53e27c37060"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x525 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> *Although the two segments appear to have similar input-output curves, dendritic nonlinearities emerge with less synapses in the distal compartment. This is because of its higher input resistance (Rinput), requiring less excitation to cross the dSpike threshold.*"
      ],
      "metadata": {
        "id": "OMBPVSoYyeRQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Panel g | Backpropagating action potentials\n",
        "\n",
        "> *An import property of many biological neurons is the ability of spikes initiated in the perisomatic area to invade adjacent dendrites and propagate \"backward\", towards the dendritic tips. To test if our model can reproduce this phenomenon, we will inject a depolarizing step current at the soma (125 pA for 300 ms), capable of eliciting a small number of somatic APs, and record the dendritic voltage responses*"
      ],
      "metadata": {
        "id": "mfXDSi-0w3-J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create a Brian NeuronGroup and link it to the neuron model\n",
        "b.start_scope()    # allows running separate simulations in the same notebook\n",
        "b.defaultclock.dt = 0.1*ms    # set it back to normal\n",
        "pyr_group = b.NeuronGroup(1, model=pyr_model.equations, method='euler',\n",
        "                          threshold='V_soma > -40*mV', reset='V_soma = 40*mV',\n",
        "                          refractory=4*ms, events=pyr_model.events,\n",
        "                          namespace=pyr_model.parameters)\n",
        "pyr_model.link(pyr_group)\n",
        "\n",
        "# trick for having more realistic somatic spike\n",
        "second_reset = b.Synapses(pyr_group, pyr_group, on_pre='V_soma=-55*mV', delay=0.5*ms)\n",
        "second_reset.connect(j='i')\n",
        "\n",
        "# set monitors to record membrane voltages\n",
        "vars = [\"V_soma\", \"V_trunk\", \"V_prox\", \"V_dist\",\n",
        "        'I_Na_dist', 'I_Kn_dist', 'I_Na_prox', 'I_Kn_prox', \n",
        "        'I_Na_trunk', 'I_Kn_trunk']\n",
        "M = b.StateMonitor(pyr_group, vars, record=True)\n",
        "\n",
        "# first 20 ms -> no input\n",
        "b.run(20*ms)\n",
        "\n",
        "# next 300 ms -> somatic current injection\n",
        "pyr_group.I_ext_soma[0] = 125 *pA\n",
        "b.run(300*ms)\n",
        "\n",
        "# next 120 ms -> no current to let membranes return to Vrest\n",
        "pyr_group.I_ext_soma[0] = 0 *pA\n",
        "b.run(120*ms)"
      ],
      "metadata": {
        "id": "otPApdIohlLV"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Plot BPAPs\n",
        "time = M.t/ms\n",
        "vs = M.V_soma[0]/mV\n",
        "vt = M.V_trunk[0]/mV\n",
        "vp = M.V_prox[0]/mV\n",
        "vd = M.V_dist[0]/mV\n",
        "\n",
        "fig, axes = b.subplots(1,2, figsize=[6,3.5])\n",
        "ax0, ax1 = axes\n",
        "\n",
        "shift = 0\n",
        "names = ['soma', 'trunk', 'prox', 'dist']\n",
        "for v, name in zip([vs, vt, vp, vd], names):\n",
        "    ax0.plot(time, v+shift)\n",
        "    ax1.plot(time, v, label=name)\n",
        "    shift -= 55\n",
        "\n",
        "ax0.plot([350,390],[-10, -10], c='black', lw=1)\n",
        "ax0.plot([350,350],[-10, 20], c='black', lw=1)\n",
        "ax0.tick_params(labelleft=False, labelbottom=False, bottom=False, left=False)\n",
        "ax0.spines['bottom'].set_visible(False)\n",
        "ax0.spines['left'].set_visible(False)\n",
        "ax0.set_title('125 pA \\u2192 soma')\n",
        "\n",
        "ax1.plot([0, 500], [40, 40], ls=':', c='black', label='Vr1')\n",
        "ax1.plot([0, 500], [-55, -55], ls=':', c='red', label='Vr2')\n",
        "ax1.set_xlim(left=95, right=104)\n",
        "ax1.set_ylim(bottom=-60)\n",
        "ax1.set_title('1st spike zoomed')\n",
        "ax1.set_ylabel('Voltage (mV)')\n",
        "ax1.set_xlabel('Time (ms)')\n",
        "ax1.legend()\n",
        "\n",
        "fig.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "cellView": "form",
        "id": "5uJ-OrLF2R67",
        "outputId": "98b3e440-bc50-434e-9fbd-71eb195ddbd6"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x525 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> *Backpropagating dSpikes are generated spontaneously in response to somatic current injections. The short-amplitude spikelets detected in the distal branch are subthreshold voltage responses for dSpike initiation. The doted lines indicate the double voltage reset mechanism we utilise to have somatic APs with more realistic shape (than ordinary I&F models). Scalebar: 40mV / 30ms.*"
      ],
      "metadata": {
        "id": "en2NM2zjZlG4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Plot dendritic currents\n",
        "ina_trunk = M.I_Na_trunk[0]/pA\n",
        "ik_trunk = M.I_Kn_trunk[0]/pA\n",
        "ina_prox = M.I_Na_prox[0]/pA\n",
        "ik_prox = M.I_Kn_prox[0]/pA\n",
        "ina_dist = M.I_Na_dist[0]/pA\n",
        "ik_dist = M.I_Kn_dist[0]/pA\n",
        "\n",
        "fig, axes = b.subplots(3, 1, figsize=[3.8,4.2], sharex=True, sharey=True)\n",
        "ax0, ax1, ax2 = axes\n",
        "\n",
        "ax0.plot(time, ina_trunk, c='black', label='INa')\n",
        "ax0.plot(time, ik_trunk, c='crimson', label='IK')\n",
        "\n",
        "ax1.plot(time, ina_prox, c='black')\n",
        "ax1.plot(time, ik_prox, c='crimson')\n",
        "\n",
        "ax2.plot(time, ina_dist, c='black')\n",
        "ax2.plot(time, ik_dist, c='crimson')\n",
        "\n",
        "ax0.set_xlim(left=97, right=102)\n",
        "ax0.legend(loc=1, frameon=False)\n",
        "ax0.set_title('trunk')\n",
        "ax1.set_ylabel('Current (pA)')\n",
        "ax1.set_title('prox')\n",
        "ax2.set_xlabel('Time (ms)')\n",
        "ax2.set_title('dist')\n",
        "\n",
        "fig.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "cellView": "form",
        "id": "TFpqwAT78maA",
        "outputId": "bb67e7b9-ba49-4eda-a7e5-d2a5ebe2d237"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 570x630 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> *To increase simulation performance we introduce a new approach for modeling dSpikes, significantly more efficient and tractable than the Hodgkin-Huxley equations. See the equations bellow:*\n",
        ">\n",
        ">```\n",
        "    I_Na_trunk = g_Na_trunk * (E_Na-V_trunk)  :amp\n",
        "    I_Kn_trunk = g_Kn_trunk * (E_K-V_trunk)  :amp\n",
        "    dg_Na_trunk/dt = -g_Na_trunk/tau_Na  :siemens\n",
        "    dg_Kn_trunk/dt = -g_Kn_trunk/tau_Kn  :siemens\n",
        ">```\n",
        "> *Each current is simulated as an alpha function multiplied by the driving force of its ion. By using Brian's custom events we were able to describe the conditions that activate these currents. Dendrify takes care of the dSpike events in the background, without users having to worry about them.*\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "9ZE8oV2Fxaq1"
      }
    }
  ]
}