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"import numpy as np\n",
"\n",
"import plotly.graph_objects as go"
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"dt = 0.001 # set 1 ms timestep\n",
"\n",
"a = 0.02 # tau_recovery: time scale of the recovery variable\n",
"b = 0.25 # coupling: How sensitive recovery is to subthreshold fluctucations of voltage\n",
"c = -65 # reset voltage (mV): The voltage to reset to after a spike\n",
"d = 2 # reset_recovery: The recovery value to reset to after a spike\n",
"\n",
"input_current = range(28) # constant inputs for each simulation\n",
"t = np.arange(0, 1, 0.001) # time samples for 1 second"
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"data = [{\n",
" \"voltage\": 0,\n",
" \"recovery\": 0,\n",
"}]\n",
"\n",
"current_data = [{\n",
" \"current\": i,\n",
" \"fire\": 0\n",
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"# voltage represents membrane potential of a neuron\n",
"# u represents the membrane recovery\n",
"\n",
"# supply a constant input current\n",
"for idx, cdata in enumerate(current_data):\n",
" I = cdata[\"current\"]\n",
" \n",
" # loop through timesteps\n",
" for _ in t:\n",
" # get the neuron state of the previous timestep\n",
" voltage = data[-1][\"voltage\"]\n",
" recovery = data[-1][\"recovery\"]\n",
" \n",
" # compute change in voltage and recovery\n",
" dv = (0.04 * voltage**2 + 5 * voltage + 140 - recovery + I) * 1000\n",
" du = a*(b * voltage - recovery) * 1000\n",
" \n",
" # apply change\n",
" voltage += dv * dt\n",
" recovery += du * dt\n",
" \n",
" # if voltage threshold reached (spiked), reset\n",
" if voltage > 30: # mV\n",
" cdata.update({\n",
" \"fire\": cdata[\"fire\"]+1\n",
" })\n",
" voltage = c\n",
" recovery = recovery + d\n",
"\n",
" # save voltage and recovery for plotting\n",
" data.append({\n",
" \"voltage\": voltage,\n",
" \"recovery\": recovery,\n",
" })"
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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = go.Figure(data=go.Scatter(x=current_data_df[\"current\"], y=current_data_df[\"fire\"]))\n",
"fig.update_layout(\n",
" title=\"Tuning Curve\",\n",
" xaxis_title=\"Input Current (mA)\",\n",
" yaxis_title=\"Firing Rate (Hz)\",\n",
" # legend_title=\"Legend Title\",\n",
" # font=dict(\n",
" # family=\"Courier New, monospace\",\n",
" # size=18,\n",
" # color=\"RebeccaPurple\"\n",
" # )\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}