{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Jupyter notebook for testing purposes. Will create formal julia analysis script later. \n",
"\n",
"include(\"utilities.jl\");\n",
"default(show=false)\n",
"default(fontfamily=\"Computer Modern\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Dict{String, Vector{Int64}} with 1 entry:\n",
" \"DG\" => [0, 1]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# HYPERPARAMS\n",
"n_runs = 10\n",
"patterns = 0:1\n",
"duration = 750\n",
"labels = [\"theta\" , \"ftheta\", \"alpha\", \"beta\", \"gamma\"]\n",
"freqlabels = [L\"\\theta\" L\"\\theta_{fast}\" L\"\\alpha\" L\"\\beta\" L\"\\gamma\"]\n",
"\n",
"fig_ext = \".png\"\n",
"\n",
"# CREATE NECESSARY DIRECTORIES \n",
"create_directories_if_not_exist()\n",
"\n",
"# IDENTIFY NEURON POPULATION RANGES\n",
"populations = Dict(\n",
" \"DG\" => [0,1]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"load_spike_files (generic function with 1 method)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"function load_spike_files(\n",
" patterns::Union{UnitRange{Int64}, Vector{Int64}}, \n",
" model_label::String,\n",
" neuron_ids::Dict;\n",
" neurons_per_pattern::Int64=1, #\n",
" data_dir::String=\"data/gcpp/\")\n",
"\n",
" df = DataFrame()\n",
" for p ∈ patterns\n",
" spike_fname = data_dir*\"DGsp-\"*string(p)*\"-\"*string(neurons_per_pattern)*\"-\"*model_label*\".txt\"\n",
" stims_fname = data_dir*\"StimIn-\"*string(p)*\"-\"*string(neurons_per_pattern)*\"-\"*model_label*\".txt\"\n",
" spikes = CSV.read(spike_fname, delim=\"\\t\", header=0, DataFrame)\n",
" stimin = CSV.read(stims_fname, delim=\"\\t\", header=0, DataFrame)\n",
"\n",
" if size(spikes, 1) > 0\n",
" rename!(spikes, [\"Time\", \"Neuron\"])\n",
" spikes[:,\"Population\"] .= \"\" \n",
" spikes[:,\"Pattern\"] .= p\n",
" spikes[:,\"NeuronsPerPattern\"] .= neurons_per_pattern \n",
" spikes[:,\"Model\"] .= model_label\n",
" for k ∈ keys(neuron_ids) \n",
" lb, ub = neuron_ids[k]\n",
" spikes[lb .<= spikes[:, \"Neuron\"] .< ub, \"Population\"] .= k\n",
" end\n",
"\n",
" df = [df; spikes]\n",
" end \n",
"\n",
" if size(stimin, 1) > 0\n",
" rename!(stimin, [\"Time\", \"Neuron\"])\n",
" stimin[:,\"Population\"] .= \"PP\"\n",
" stimin[:,\"Pattern\"] .= p\n",
" stimin[:,\"NeuronsPerPattern\"] .= neurons_per_pattern \n",
" stimin[:,\"Model\"] .= model_label\n",
" df = [df; stimin]\n",
" end\n",
" end\n",
"\n",
" return df\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# ISI figure\n",
"\n",
"default(fontfamily=\"Computer Modern\")\n",
"n_runs = 10\n",
"save_cov = OrderedDict(\"theta\"=>[], \"ftheta\"=>[], \"alpha\"=>[], \"beta\"=>[], \"gamma\"=>[])\n",
"\n",
"for run_ ∈ 1:n_runs\n",
" for l ∈ 1:length(labels)\n",
" spikes = load_spike_files(patterns, labels[l]*\"-$run_\", populations)\n",
" gc = spikes[(spikes.Population .== \"DG\") .& (spikes.Pattern .== 1), :]\n",
" spiketimes = gc[!,\"Time\"]\n",
" ISIs = []\n",
"\n",
" for i ∈ 1:(length(spiketimes)-1)\n",
" time1 = spiketimes[i]\n",
" time2 = spiketimes[i+1] \n",
" ISI = time2 - time1 \n",
" append!(ISIs, ISI)\n",
" end\n",
"\n",
" cov = mean(ISIs)\n",
" append!(save_cov[labels[l]], cov)\n",
"\n",
" end\n",
"end\n",
"\n",
"meanisi = OrderedDict(\"theta\"=>[], \"ftheta\"=>[], \"alpha\"=>[], \"beta\"=>[], \"gamma\"=>[])\n",
"stdisi = OrderedDict(\"theta\"=>[], \"ftheta\"=>[], \"alpha\"=>[], \"beta\"=>[], \"gamma\"=>[])\n",
"\n",
"for l ∈ 1:length(labels)\n",
" meanv = []\n",
" stdv = []\n",
" meanv = mean(save_cov[labels[l]])\n",
" append!(meanisi[labels[l]], meanv)\n",
" stdv = std(save_cov[labels[l]])\n",
" append!(stdisi[labels[l]], stdv)\n",
"end\n",
"\n",
"\n",
"unpack(a) = eltype(a[1])[el[1] for el in a]\n",
"\n",
"avgisi = unpack(collect(values(meanisi)))\n",
"varisi = unpack(collect(values(stdisi)))\n",
"freqlabels = [L\"\\theta\", L\"\\theta_{f}\", L\"\\alpha\", L\"\\beta\", L\"\\gamma\"]\n",
"\n",
"isi_fig = plot(freqlabels, avgisi, yerror = varisi, xlabel = \"Input Frequency Band\", yticks = [50, 75, 100],\n",
" xtickfont=font(16), xguidefontsize=11, ytickfontsize = 16, yguidefontsize = 14, \n",
" markerstrokewidth =2, grid=:none, ylabel = \"Mean ISI (ms)\", \n",
" c = :black, linewidth = 4, dpi=300, size=(250,220), label=nothing)\n",
"\n",
"savefig(isi_fig, \"figures/pattern-separation/ISI_10runs-CCN\"*fig_ext)\n"
]
},
{
"cell_type": "code",
"execution_count": 222,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"┌ Warning: thread = 1 warning: parsed expected 2 columns, but didn't reach end of line around data row: 1. Parsing extra columns and widening final columnset\n",
"└ @ CSV /Users/selenasingh/.julia/packages/CSV/jFiCn/src/file.jl:579\n"
]
}
],
"source": [
"# VOLTAGE TRACE + INPUT SPIKE TRAIN FIGURE\n",
"volt_fname = \"data/gcpp/DGVt-1-1-gamma-1.txt\"\n",
"volttrace = CSV.read(volt_fname, delim=\"\\t\", header=1, DataFrame)\n",
"volttrace = volttrace[:, 1:2]\n",
"\n",
"spikes = load_spike_files(patterns, labels[5]*\"-1\", populations)\n",
"stimin = spikes[(spikes.Population .== \"PP\") .& (spikes.Pattern .== 1), :]\n",
"plots = []\n",
"\n",
"rename!(volttrace, [\"Time\", \"Voltage\"])\n",
"\n",
"p = scatter(stimin[:,1], stimin[:,2], \n",
" marker = (:vline, 30, 0.8, :red, stroke(4, 0.8, :black, :vline)),\n",
" label=nothing, c=:red, msc=:black, #msa=1, ma=1, \n",
" xlims = [0,285], ylims = [0,5], axis=([], false), grid = false )\n",
"\n",
"vtrace = plot(volttrace[1:2850, \"Time\"], volttrace[1:2850, \"Voltage\"],\n",
" color = :black, linewidth = 2, legend = :none, axis=([], false), grid = false, \n",
" size = (600,300))\n",
"\n",
"append!(plots, [vtrace])\n",
"append!(plots, [p])\n",
"\n",
"fig = plot(plots..., layout=grid(2, 1, heights=[0.95, 0.05]), size=(500, 400), dpi = 300)\n",
"\n",
"savefig(fig, \"figures/voltage-tracings/voltage-tracings-gamma.png\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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