{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "McBiAX2GtIhy"
},
"source": [
"\n",
"\n",
"# Running simulations \n",
"\n",
"In this tutorial, we are going to cover the following topics:\n",
"\n",
"* How to merge compartments into compartmental neuron models\n",
"* How to make Dendrify and Brian interact with each other\n",
"* How to run simulations of Dendrify models in Brian\n",
"\n",
"\n",
"\n",
"Disclaimer\n",
"\n",
"Below, we present a generic \"toy\" model developed solely for educational purposes.\n",
"However, please note that its parameters and behavior have not been validated using\n",
"real data. If you plan to use Dendrify in a project, we recommend calibrating the\n",
"model's parameters to suit your specific needs before applying it.\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_DY92XwdtIRn"
},
"source": [
"## Imports & settings"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "AsdN3SL3sw03"
},
"outputs": [],
"source": [
"import brian2 as b\n",
"import matplotlib.pyplot as plt\n",
"from brian2.units import *\n",
"from dendrify import Soma, Dendrite, NeuronModel\n",
"\n",
"b.prefs.codegen.target = 'numpy' # faster for basic models and short simulations"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"cellView": "form",
"id": "Lw68q2p-_b_f"
},
"outputs": [],
"source": [
"# Plot 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",
" 'lines.markersize': 3,\n",
" 'lines.linewidth': 1.25,\n",
" 'grid.color': \"#d3d3d3\",\n",
" 'figure.dpi': 150,\n",
" 'axes.prop_cycle': b.cycler(color=[blue, green, orange, notred])\n",
" }\n",
"\n",
"plt.rcParams.update(params)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "d4gD9Sdy-Gdp"
},
"source": [
"## Create a compartmental model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zza4XGmH-VM7"
},
"source": [
"\n",
"\n",
"![\"model\"](\"https://github.com/Poirazi-Lab/dendrify/blob/main/paper_figures/graphics/2.png?raw=true\")
\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "w3qaNaQREaAW"
},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "KnB8V4uSDxIz"
},
"outputs": [],
"source": [
"# create soma\n",
"soma = Soma('soma', model='leakyIF', length=25*um, diameter=25*um,\n",
" cm=1*uF/(cm**2), gl=40*uS/(cm**2),\n",
" v_rest=-65*mV)\n",
"\n",
"# create trunk\n",
"trunk = Dendrite('trunk', length=100*um, diameter=2.5*um,\n",
" cm=1*uF/(cm**2), gl=40*uS/(cm**2),\n",
" v_rest=-65*mV)\n",
"\n",
"# create proximal dendrite\n",
"prox = Dendrite('prox', length=100*um, diameter=1*um,\n",
" cm=1*uF/(cm**2), gl=40*uS/(cm**2),\n",
" v_rest=-65*mV)\n",
"prox.synapse('AMPA', tag='pathY', g=1*nS, t_decay=2*ms)\n",
"prox.synapse('NMDA', tag='pathY', g=1*nS, t_decay=60*ms)\n",
"\n",
"# create distal dendrite\n",
"dist = Dendrite('dist', length=100*um, diameter=0.5*um,\n",
" cm=1*uF/(cm**2), gl=40*uS/(cm**2),\n",
" v_rest=-65*mV)\n",
"dist.synapse('AMPA', tag='pathX', g=1*nS, t_decay=2*ms)\n",
"dist.synapse('NMDA', tag='pathX', g=1*nS, t_decay=60*ms)\n",
"\n",
"soma.connect(trunk, g=15*nS)\n",
"trunk.connect(prox, g=6*nS)\n",
"prox.connect(dist, g=2*nS)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xZv2KH5iE-L8"
},
"source": [
"