{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Perlin spontaneous\n",
"\n",
"Author: Sebastian Spreizer\n",
"\n",
"The MIT License"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import nest\n",
"import noise\n",
"import numpy as np\n",
"\n",
"import lib.connectivity_map as cm\n",
"import lib.lcrn_network as lcrn\n",
"import lib.animation as animation\n",
"import lib.plot3d as pl3d\n",
"import lib.colormap as cmap"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Perlin landscape for connectivity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Network size\n",
"nrowE = ncolE = 120\n",
"nrowI = ncolI = 60\n",
"npopE = nrowE * ncolE\n",
"npopI = nrowI * ncolI\n",
"\n",
"nrow = nrowE\n",
"landscape = np.round(cm.Perlin_uniform(nrow, size=3, base=1) * 7).astype(int)\n",
"move = cm.move(nrow)\n",
"\n",
"fig,ax = plt.subplots(1,1)\n",
"im = ax.matshow(landscape.reshape(nrow,-1), cmap=cmap.virno())\n",
"plt.colorbar(im, ax=ax)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Set Kernel Status"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(0)\n",
"nest.ResetKernel()\n",
"nest.SetKernelStatus({\n",
" 'local_num_threads': 4,\n",
" 'resolution': 0.1,\n",
"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create nodes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"params = {\n",
" \"C_m\": 250.0,\n",
" \"E_L\": -70.0,\n",
" \"V_reset\": -70.0,\n",
" \"V_th\": -55.0,\n",
" \"t_ref\": 2.0,\n",
" \"tau_m\": 10.0,\n",
" \"tau_minus\": 20.0,\n",
" \"tau_syn_ex\": 5.0,\n",
" \"tau_syn_in\": 5.0,\n",
"}\n",
"\n",
"popE = nest.Create('iaf_psc_alpha', npopE, params=params)\n",
"popI = nest.Create('iaf_psc_alpha', npopI, params=params)\n",
"pop = popE + popI\n",
"\n",
"# Create devices\n",
"ngE = nest.Create('noise_generator')\n",
"ngI = nest.Create('noise_generator')\n",
"ng = ngE + ngI\n",
"sd = nest.Create('spike_detector', params={'start':500.})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Connect nodes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"offsetE = popE[0]\n",
"offsetI = popI[0]\n",
"\n",
"p = 0.05\n",
"stdE = 9\n",
"stdI = 12\n",
"g = 8\n",
"shift = 1\n",
"\n",
"\n",
"for idx in range(npopE):\n",
" # E-> E\n",
" source = idx, nrowE, ncolE, nrowE, ncolE, int(p * npopE), stdE, False\n",
" targets, delay = lcrn.lcrn_gauss_targets(*source)\n",
" targets = (targets + shift * move[landscape[idx] % len(move)]) % npopE\n",
" targets = targets[targets != idx] \n",
" nest.Connect([popE[idx]], (targets + offsetE).tolist(), syn_spec={'weight': 10.0})\n",
"\n",
" # E-> I\n",
" source = idx, nrowE, ncolE, nrowI, ncolI, int(p * npopI), stdE / 2, False\n",
" targets, delay = lcrn.lcrn_gauss_targets(*source)\n",
" nest.Connect([popE[idx]], (targets + offsetI).tolist(), syn_spec={'weight': 10.0})\n",
"\n",
"for idx in range(npopI):\n",
" # I-> E\n",
" source = idx, nrowI, ncolI, nrowE, ncolE, int(p * npopE), stdI, False\n",
" targets, delay = lcrn.lcrn_gauss_targets(*source)\n",
" nest.Connect([popI[idx]], (targets + offsetE).tolist(), syn_spec={'weight': g * -10.0})\n",
"\n",
" # I-> I\n",
" source = idx, nrowI, ncolI, nrowI, ncolI, int(p * npopI), stdI / 2, False\n",
" targets, delay = lcrn.lcrn_gauss_targets(*source)\n",
" targets = targets[targets != idx]\n",
" nest.Connect([popI[idx]], (targets + offsetI).tolist(), syn_spec={'weight': g * -10.0})\n",
"\n",
"# Connect noise input device to all neurons\n",
"nest.Connect(ngE, popE, syn_spec={'weight': 10.0})\n",
"nest.Connect(ngI, popI, syn_spec={'weight': 10.0})\n",
"\n",
"nest.Connect(pop, sd)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Warming up"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nest.SetStatus(ng, params={'std': 50.})\n",
"nest.Simulate(250.)\n",
"nest.SetStatus(ng, params={'mean': 35., 'std': 10.})\n",
"nest.Simulate(250.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Start simulation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nest.SetStatus(ng, params={'mean': 35., 'std': 10.})\n",
"nest.Simulate(1000.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot spiking activity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sdE = nest.GetStatus(sd, 'events')[0]\n",
"ts, gids = sdE['times'], sdE['senders']\n",
"fig, ax = plt.subplots(1)\n",
"ax.plot(ts, gids, 'k|')\n",
"ax.set_xlabel('Time [ms]')\n",
"ax.set_ylabel('Neuron')\n",
"ax.set_ylim(200,400)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"idx = gids - offsetE < npopE\n",
"gids, ts = gids[idx] - offsetE, ts[idx]\n",
"time = nest.GetKernelStatus('time')\n",
"\n",
"ts_bins = np.arange(time-1000., time, 10.)\n",
"h = np.histogram2d(ts, gids, bins=[ts_bins, range(npopE + 1)])[0]\n",
"hh = h.reshape(-1, nrowE, ncolE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig,ax = plt.subplots(1)\n",
"im = ax.imshow(hh.sum(0) / 1., cmap='binary')\n",
"ax.set_xlabel('x')\n",
"ax.set_ylabel('y')\n",
"plt.colorbar(im,ax=ax)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Animation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, ax = plt.subplots(1)\n",
"im2 = ax.imshow(landscape.reshape(nrow,-1), cmap=cmap.virno())\n",
"im1 = ax.imshow(hh[0], vmin=0, vmax=np.max(hh), cmap='binary_r', alpha=.5)\n",
"anim = animation.imshow(fig, ax, im1, hh, ts_bins)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#anim.save('EI_networks-spontaneous.mp4', fps=10., extra_args=['-vcodec', 'libx264'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Interactive animation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"animation.HTML(anim.to_jshtml())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 3D plotting of spike activity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig,ax = pl3d.scatter(ts,gids%nrow,gids//nrow)\n",
"ax.set_xlabel('Time [ms]')\n",
"ax.set_ylabel('y position')\n",
"ax.set_zlabel('x position')\n",
"ax.set_xlim(500,1500)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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