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@rueberger
Created November 23, 2016 04:50
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x105e02400>]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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qoBcRaYTmrqOfCvQ2s52Bz4HjgVHVjpkEjAEez3wwrKipfr4pgYqISOPUmeid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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x105d9c358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lmbda = 0.1\n",
"t_alpha = np.linspace(0, 1, 100)\n",
"alpha = (t_alpha / lmbda) * np.exp((lmbda - t_alpha) / lmbda)\n",
"plt.plot(alpha)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# i feel like integrated is poor language here, as that as a specific meaning in neuroscience\n",
"# whereas what we're simulating here is neurotransmitter concentration...\n",
"accum_alpha = np.zeros(500)\n",
"spike_times = [5, 105, 150, 175, 300]\n",
"for t_s in spike_times:\n",
" accum_alpha[t_s:t_s + 100] += alpha"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x105e9f630>]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AnKBDcpY9yZI7tU7LRdwqW6K1ZTRzjQl7XuQZkkOqJE+ZIm3ezCluNIdjBYA8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\nUdMmWloqbroAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x105dd3d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12, 8))\n",
"plt.plot(accum_alpha)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# circuit simulation"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"timesteps = 2000\n",
"# [E1, I1, I2]\n",
"mem_v = np.zeros((3, timesteps))\n",
"spikes = np.zeros((3, timesteps))\n",
"transmitter = np.zeros((3, timesteps))\n",
"thresholds = np.array([500, 400, 400])\n",
"leak_rates = np.array([0.9, 0.8, 0.8])\n",
"# constant external input current\n",
"external_i = np.array([100, 0, 0])\n",
"weights = np.array([[0, 250, 230], \n",
" [0, 0, -30],\n",
" [0, -30, 0]])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for t_idx in range(timesteps):\n",
" # matmul W v\n",
" mem_v[:, t_idx] += weights.dot(transmitter[:, t_idx]) + external_i\n",
" # in numpy boolean arrays are encoded as int arrays with 1, 0 respectively true and false\n",
" spikes[:, t_idx] = mem_v[:, t_idx] > thresholds\n",
" # reset membrane voltage\n",
" mem_v[:, t_idx] *= np.logical_not(spikes[:, t_idx])\n",
" # broadcast magic\n",
" extent_idx = min(timesteps - 1 - t_idx, 100)\n",
" transmitter[:, t_idx:t_idx + extent_idx] += alpha[:extent_idx].reshape(1, extent_idx) * spikes[:, t_idx].reshape(3, 1)\n",
" # apply leak\n",
" mem_v[:, t_idx] *= leak_rates\n",
" if t_idx + 1 < timesteps:\n",
" # prepare for next iteration\n",
" mem_v[:, t_idx + 1] = mem_v[:, t_idx]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x107a97080>]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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E6qb/apKyp58OV1jq0iWz15uF1Sl33x0uvTSnoYlIHTTrRlJSUREuxzd4cN0XBBGR3NOs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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10794c1d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(transmitter[0, :100])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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