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2363 lines
15 MiB
2363 lines
15 MiB
2 years ago
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"stim_data.pkl\n",
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"dict_keys(['stim_val', 'trial_stim_id', 'key_list', 'num_trial', 'trial_pair_id', 'pair_val', 'pair_trial_id', 'stim_id_trial', 'num_stim'])\n",
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"spike_data.pkl\n",
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"dict_keys(['spike_count_rate', 'avg_firing_rate', 'sem_firing_rate', 'firing_rate', 'stim_num_trial', 'C_r_fphi_theta', 'theta_hist', 'phase_hist', 'pair_hist'])\n",
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"corr_data.pkl\n",
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"dict_keys(['corr_stim_unit', 'optimal_avg_firing_rate', 'stim_hist', 'stim_hist_caution'])\n"
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]
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}
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],
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"source": [
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"# load data\n",
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"\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import scipy.stats as sts\n",
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"import pickle\n",
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"import ipywidgets as widgets\n",
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"from mpl_toolkits import mplot3d\n",
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"from mpl_toolkits.mplot3d import Axes3D\n",
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"from scipy import sparse\n",
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"from matplotlib import cm\n",
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"cmap_hot = cm.get_cmap('hot')\n",
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"cmap_viridis = cm.get_cmap('viridis')\n",
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"\n",
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"execfile('Stimulus.py')\n",
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"data_folder = \"Data/\"\n",
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"\n",
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"stim_file = \"Stiminfo_PVCre_2021_0012_s06_e14.csv\"\n",
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"stim = pd.read_csv(data_folder+stim_file)\n",
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"\n",
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"spike_times_file = \"Spiketimes_PVCre_2021_0012_s06_e14.npy\"\n",
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"spike_times = np.load(data_folder+spike_times_file, allow_pickle=True)\n",
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"active = [len(spike_times[i]) > 0 for i in range(len(spike_times))]\n",
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"spike_times = spike_times[np.where(active)]\n",
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"\n",
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"num_unit = len(spike_times)\n",
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"num_trial = len(stim)\n",
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"\n",
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"# sort by firing rate\n",
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"\n",
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"num_spike = list(map(len, spike_times))\n",
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"# num_spike = np.array([len(spike_times[i]) for i in range(len(spike_times))])\n",
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"spike_times = spike_times[np.argsort(num_spike)[::-1]]\n",
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"execfile('load.py')\n",
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"\n",
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"max_delay = 300 # dt\n",
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"tau_id_range = np.arange(max_delay)\n",
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"\n",
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"latest_spike_time = max([np.max(s) for s in spike_times if len(s)])\n",
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"latest_stim_offtime = list(stim['stim_offtime'])[-1]\n",
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"experiment_dur = max([latest_spike_time, latest_stim_offtime])\n",
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"\n",
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"dt = 0.001 # 1 ms\n",
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"exp_time = np.arange(0, experiment_dur, dt)\n",
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"M = len(exp_time)\n",
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"\n",
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"# binary spike and stimulus trains\n",
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"B_stim = {}\n",
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"for key in key_list:\n",
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" B_stim[key] = []\n",
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" for stim_id, trials in enumerate(stim_id_trial[key]):\n",
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" B_stim[key].append([])\n",
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" s = []\n",
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" for trial_id in trials:\n",
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" t_on, t_off = stim['stim_ontime'][trial_id], stim['stim_offtime'][trial_id]\n",
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" s += list(np.arange(int(t_on//dt), int(t_off//dt)))\n",
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"\n",
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" B_stim[key][stim_id] = sparse.coo_matrix((np.ones(len(s)), (np.zeros(len(s), dtype=int), s)), shape=(1, M))\n",
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"s = spike_times//dt\n",
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"B_spike = []\n",
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"for unit_id in range(num_unit):\n",
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" B_spike.append(sparse.coo_matrix((np.ones(len(s[unit_id])), (np.zeros(len(s[unit_id]), dtype=int), np.int0(s[unit_id]))), shape=(1, M)))\n",
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"\n",
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"# histogram error bars: num spikes\n",
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"s = np.zeros((num_unit, 2))\n",
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"for unit_id in range(num_unit):\n",
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" # print(\"unit: %d\"%unit_id)\n",
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" a = np.zeros(len(tau_id_range))\n",
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" for tau_id in tau_id_range:\n",
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" a[tau_id] = np.sum(B_spike[unit_id].col >= tau_id)\n",
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" \n",
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" s[unit_id] = [np.mean(a), np.std(a)]\n",
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"\n",
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"key_symbol = {'pair':'$(\\\\theta,\\phi)$', 'orientation':'$\\\\theta$', 'phase':'$\\phi$'}\n",
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"\n",
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"# 2D tuning\n",
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"avg_firing_rate_pair = np.array([sts.zscore(stim_hist['pair'][unit_id]).reshape((len(tau_id_range), num_stim['orientation'], num_stim['phase'])) for unit_id in range(num_unit)])"
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]
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},
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{
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"cell_type": "code",
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|
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"execution_count": 2,
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|
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"metadata": {},
|
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"outputs": [],
|
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|
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"source": [
|
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|
|
"import numpy as np\n",
|
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|
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"import matplotlib.pyplot as plt\n",
|
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|
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"import pandas as pd\n",
|
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|
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"import scipy.stats as sts\n",
|
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|
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"data_folder = \"Data/\"\n",
|
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|
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"stim_file = \"Stiminfo_PVCre_2021_0012_s06_e14.csv\"\n",
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"spike_times_file = \"Spiketimes_PVCre_2021_0012_s06_e14.npy\"\n",
|
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|
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"spike_times = np.load(data_folder+spike_times_file, allow_pickle=True)\n",
|
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|
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"stim = pd.read_csv(data_folder+stim_file)\n",
|
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|
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"# execfile('load.py')"
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]
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},
|
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{
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|
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"cell_type": "code",
|
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|
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"execution_count": 3,
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"metadata": {},
|
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"outputs": [],
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"source": [
|
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|
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"sorted_spike_num = np.sort(num_spike)[::-1]\n",
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|
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"cutoff_num_spike = 1000\n",
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"num_unit = np.sum(sorted_spike_num > cutoff_num_spike)\n",
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"for key in key_list:\n",
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|
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" stim_hist[key] = stim_hist[key][:num_unit]"
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]
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},
|
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{
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|
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"cell_type": "markdown",
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|
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"metadata": {},
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"source": [
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"orientation selectivity index (OSI)\n",
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"\n",
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"# $\\frac{R_{pref} - R_{orth}}{R_{pref} + R_{orth}}$"
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]
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},
|
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|
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{
|
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|
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"cell_type": "markdown",
|
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|
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"metadata": {},
|
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|
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"source": [
|
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|
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"Stimulus-triggered average response (tuning curve)"
|
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]
|
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|
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},
|
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|
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{
|
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|
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"cell_type": "code",
|
||
|
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"execution_count": 7,
|
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|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"data": {
|
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 648x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
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}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"unit_id = 1\n",
|
||
|
|
"fig, ax = plt.subplots(1,2, figsize=(9,4))\n",
|
||
|
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"for i, key in enumerate(key_list[1:]):\n",
|
||
|
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" ax[i].plot(stim_val[key], avg_firing_rate[key][unit_id], '.-')\n",
|
||
|
|
" ax[i].set_xlabel(key)\n",
|
||
|
|
" # ax[i].set_xticks(np.arange(20)[::5])\n",
|
||
|
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" # ax[i].set_xticklabels(stim_val[key][::5])\n",
|
||
|
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"\n",
|
||
|
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"fig.suptitle('unit '+ \"%d\"%unit_id)\n",
|
||
|
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"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Orientation Tuning"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2880 with 40 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"key = 'orientation'\n",
|
||
|
|
"fig, ax = plt.subplots(10, 4, figsize=(5*4, 10*4))\n",
|
||
|
|
"for unit_id in range(40):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].errorbar(stim_val[key], avg_firing_rate[key][unit_id], yerr=sem_firing_rate[key][unit_id])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel(key)\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('avg firing rate / Hz')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylim([0,np.max(avg_firing_rate[key][unit_id])+1])\n",
|
||
|
|
"\n",
|
||
|
|
"ax[0,0].set_title('firing rate (Hz) vs. orientation')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Phase Tuning"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABJIAAAi7CAYAAAAN7vqVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdd3iUZdbH8e9JT0ioCb136SVURbFjL2vDXrGXVde2+1p3V3d1dVUsoCKooCj2LnZBeu+9hZZQQhJC+v3+MQMbMIFJMiXl97muXJl5ytxnJsnJM2fuYs45REREREREREREjiQs1AGIiIiIiIiIiEjVoEKSiIiIiIiIiIj4RIUkERERERERERHxiQpJIiIiIiIiIiLiExWSRERERERERETEJyokiYiIiIiIiIiIT1RICjIz62Rm880s08zuMLNXzez/DnP8EDNbEcwYA8nMos1sqZk18cNjnWVmE/0Rl7+YWWszc2YWEepYRERERERERPzNnHOhjqFGMbM3gAzn3J9DHUtZmdlQ4B3nXPMKPMbtQFfn3E3e+2OBFOfc34od0xpYB0Q65wqO8HiLgUudcwvLG5M/lSV2ERERERERkapGPZKCrxWwxJcDg9mrxTyC8ftwE/C2Hx/vXWCEHx9PREREREREREqhQlIQmdmPwPHASDPLMrOOZjbWzP7u3T/UzFLM7H4z2wa8uX9bscdYb2b3mtlCM9tjZhPNLKbY/vvMbKuZbTGz673DrNqXEs/PZvYPM5sKZANtzewaM1vmHXq31sxu9B5bC/gaaOqNPcvMmppZmJk9YGZrzGynmb1vZvVLaa8l0BaYUYbXrHh7WWaWbWbFu9H9DJxRyrn3m9mkQ7Y9b2YveG9f7X2OmWa2zswu8zGmn83sSTObaWYZZvZpCc/5MjPbaGY7zOyvxc7tb2bTzCzd+3MaaWZR3n1mZs+ZWar3cReZWTfvvmgze8b7mNu9QyJjfYlXRERERERExF9USAoi59wJwG/Abc65eOfcyhIOawzUx9NzqbSeNhcBw4A2QA/gagAzGwbcDZwEtAeG+hDWFd52EoANQCpwJlAbuAZ4zsz6OOf2AqcBW7yxxzvntgC3A+cCxwFNgd3AS6W01R1YW5YhX8654u3FAx8D7xU7ZBnQ2sxql3D6e8DpZpYAYGbheF67Cd7C2AvAac65BGAwMN/XuIArgWuBJkCB97GKOwboBJwIPGxmR3m3FwJ/BhKBQd79t3j3nQIcC3QE6nhj3end95R3ey88P9tmwMNliFdERERERESkwlRIqnyKgEecc7nOuX2lHPOCt8CyC/gcT3EBPIWHN51zS5xz2cCjPrQ31nt8gXMu3zn3pXNujfP4BfgOGHKY828C/uqcS3HO5XrbvKCUYXl1gcwStt/r7aGTbmbpQInzHZnZ/UBnPAWc/fY/Xt1Dj3fObQDmAud5N50AZDvnpnvvFwHdzCzWObfVOefTkEOvt51zi70Ftv8DLvIWqvZ7zDm3zzm3AFgA9PTGNMc5N937eq8HRuEpwgHk4ynodcYzf9ky59xWMzM8xb4/O+d2OecygX8Cl5QhXhEREREREZEKUyGp8klzzuUc4ZhtxW5nA/He202BTcX2Fb9dmoOOMbPTzGy6me3yFnVOx9N7pjStgI+LFYGW4el106iEY3fjKZQc6hnnXN39X3h6WR3EzE4D7gTOPaTAtv/x0kuJbwIw3Hv7Uu99vAWgi/EUwraa2Zdm1rm0J1mC4q/bBiCSg1+nEn9G3uGMX5jZNjPLwFMQSvTG9CMwEk+PrlQzG+3taZUExAFzir3O33i3i4iIiIiIiASNCkmVT0WW0dsKFF9RrUVZ2jOzaOBD4Bmgkbeo8xVgh4ltE57hYXWLfcU45zaXcOxCoE0pvZVKZWadgHHARc65Q4tjRwHrnXMZpZz+ATDUzJrj6Zk0Yf8O59y3zrmT8QxPWw68Voawir+2LfH0Jtrhw3mveNvq4JyrDTzE/15fnHMvOOf6Al3wDGX7i/dx9+FZ7W7/a1zHO9RPREREREREJGhUSKpe3geuMbOjzCwOz5CrsogCooE0oMDbC+iUYvu3Aw3MrE6xba8C/zCzVgBmlmRm55T04M65FGA10N/XgLw9cj7FM3xuSgmHHIdnEvASOefS8EzI/Sawzjm3zPu4jczsHO9cSblAFp6hbr663My6eF/nx4FJzrlCH85LADKALG8PqJv37zCzfmY2wMwigb1ADlDknCvCU+R6zswaeo9tZmanliFeERERERERkQpTIakacc59jWfS55/wFGz2zwWU6+P5mcAdeApSu/EMBfus2P7lwLvAWu8Qq6bA895jvjOzTG+bAw7TzCg8E3z7qg+eSaufK756W7H9w72PeTgT8ExAPqHYtjA8E5NvAXbhKUjdDGBmQw5poyRvA2PxDGGLwfO6+eJePK9rJp7i0MRi+2p7t+3GM1xuJ/C0d9/9eH+m3iFx3+N5XURERERERESCxpyryEgqqcy8K4UtBqLLslJaIHmHz80DTnTOba3gY50FXOGcu8gvwfne7s/AO86514PZroiIiIiIiEioqUdSNWNm55lZtJnVA/4FfF5ZikgA3tXoulS0iOR9rM+DXUQSERERERERqclUSKp+bgRSgTV4Vk+7+fCHi4iIiIiIiIj4RkPbRERERPzEzFoAbwGN8Kx2Oto597yZ1cczL15rYD2elUh3l3D+VcDfvHf/7pwbF4y4RURERHylQpKIiIiIn5hZE6CJc26umSUAc4BzgauBXc65p8zsAaCec+7+Q86tD8wGkvEUoeYAfUsqOImIiIiESkSoAyirxMRE17p161CHIX6wNm0vAG2TaoU4EgmFOXPm7HDOJYU6jvJSLpKqRjm3ZP7ORd45ALd6b2ea2TKgGXAOMNR72DjgZzwrchZ3KjDZObcLwMwmA8PwrJhaIuUikeqhql8XgfKRSHXgay4KSiHJzGKAX4Fob5uTnHOPmNlYPMuu7/EeerVzbv7hHqt169bMnj07gNFKsFw8ahoAE28cFOJIJBTMbEOoY6gI5SKpapRzSxbIXGRmrYHewAygUbGFJrbhGfp2qGbApmL3U7zbDn3cEcAIgJYtWyoXiVQDobouMrMxwJlAqnOuW7HttwO34plz9Uvn3H1HeixdG4lUfb7momD1SMoFTnDOZZlZJDDFzL727vuLc25SkOIQERERCTgziwc+BO5yzmWY2YF9zjlnZuWeW8A5NxoYDZCcnKw5CkSkIsYCI/HM7QaAmR2PpxdlT+dcrpk1DFFsUsPpQ7DKKyirtjmPLO/dSO+XLnxERESk2vF+aPYhMN4595F383bv/En751FKLeHUzUCLYvebe7dJFXTxqGkH3gSJVFbOuV+BXYdsvhl4yjmX6z2mpHwlIjVYUApJAGYWbmbz8Vw4TXbOzfDu+oeZLTSz58wsOljxSOgVFBahyd5FRKQ6MU/XozeAZc65Z4vt+gy4ynv7KuDTEk7/FjjFzOqZWT3gFO82qQFUeJJKpCMwxMxmmNkvZtavtAPNbISZzTaz2WlpaUEMUURCKWiFJOdcoXOuF55P1/qbWTfgQaAz0A+ozx8nnQSUoKqj1amZzN2Uzub0nFCHIiJS7TnnSNmdza69eaEOpSY4GrgCOMHM5nu/TgeeAk42s1XASd77mFmymb0O4J1k+wlglvfr8f0Tb4uIBFEEnvdmA4G/AO9b8fG5xTjnRjvnkp1zyUlJVXqucKlhVLyvmKCv2uacSzezn4BhzrlnvJtzzexN4N5SztFcANWIc47HPl+Kc7B1zz52782jXq2oUIclIlJtfbN4G5vTcwgzSNmdTfN6caEOqdpyzk0BSnzDBZxYwvGzgeuL3R8DjAlMdCIiPkkBPnKeoQMzzawISAT0ib6IAEHqkWRmSWZW13s7FjgZWF5srgADzgUWByMeCa0fl6fy26odNEyIpsjB61PWhjokEZFqKzMnn0c/X0JMpOdf/iOfLtGwYhEROZxPgOMBzKwjEAXsCGVAIlK5BGtoWxPgJzNbiKer9mTn3BfAeDNbBCzCU+X+e5DikRDJLSjkiS+W0i6pFq0
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2880 with 40 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"key = 'phase'\n",
|
||
|
|
"fig, ax = plt.subplots(10, 4, figsize=(5*4, 10*4))\n",
|
||
|
|
"for unit_id in range(40):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].errorbar(stim_val[key], avg_firing_rate[key][unit_id], yerr=sem_firing_rate[key][unit_id])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel(key) \n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('avg firing rate / Hz')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylim([0,np.max(avg_firing_rate[key][unit_id])+1])\n",
|
||
|
|
"\n",
|
||
|
|
"ax[0,0].set_title('firing rate (Hz) vs. phase')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"2D Tuning"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"avg_firing_rate_pair = avg_firing_rate['pair'].reshape((num_unit, num_stim['orientation'], num_stim['phase']))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# normalize\n",
|
||
|
|
"import scipy.stats as sts\n",
|
||
|
|
"avg_firing_rate_pair_normalized = sts.zscore(avg_firing_rate_pair.reshape(num_unit, 20*20), axis = 1).reshape(num_unit, 20, 20)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2880 with 40 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(10, 4, figsize=(5*4, 10*4))\n",
|
||
|
|
"for unit_id in range(40):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].imshow(avg_firing_rate_pair_normalized[unit_id], interpolation='gaussian')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xticks(np.arange(20)[::5])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xticklabels(stim_val['phase'][::5])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_yticks(np.arange(20)[::5])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_yticklabels(stim_val['orientation'][::5])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('phase')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('orientation')\n",
|
||
|
|
" \n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Orientation tuning with fixed phase"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(stim_val['orientation'], avg_firing_rate_pair[5, :, :])\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Klein bottle"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2880 with 40 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# klein bottle symmetry\n",
|
||
|
|
"\n",
|
||
|
|
"fig, ax = plt.subplots(10, 4, figsize=(5*4, 10*4))\n",
|
||
|
|
"klein_bottle_rate = np.zeros((num_unit, 40, 40))\n",
|
||
|
|
"for unit_id in range(40):\n",
|
||
|
|
" x = np.hstack((avg_firing_rate_pair_normalized[unit_id], avg_firing_rate_pair_normalized[unit_id]))\n",
|
||
|
|
" klein_bottle_rate[unit_id] = np.vstack((x, x[:,::-1]))\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].imshow(klein_bottle_rate[unit_id], interpolation='gaussian')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot()\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_xticks(np.arange(40)[::5])\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_xticklabels(np.concatenate((stim_val['phase'][::5],stim_val['phase'][::5])))\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_yticks(np.arange(40)[::5])\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_yticklabels(stim_val['orientation'][::5])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xticks([])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_yticks([])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(np.arange(40), 19.5*np.ones(40), 'r', linewidth=0.5)\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(19.5*np.ones(40), np.arange(40), 'r', linewidth=0.5)\n",
|
||
|
|
"\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('phase')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('orientation')\n",
|
||
|
|
" \n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 41,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x2498c2e3160>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 41,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(np.mean(avg_firing_rate_pair[17], axis=0))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Orientation loop"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"x = np.copy(stim_val['pair'])\n",
|
||
|
|
"s_id = []\n",
|
||
|
|
"y = np.copy(x)\n",
|
||
|
|
"s = []\n",
|
||
|
|
"for i in range(len(x)):\n",
|
||
|
|
" if x[i,1] > 180:\n",
|
||
|
|
" y[i] = np.array([x[i,0] + 180, 360 - x[i,1]])\n",
|
||
|
|
" \n",
|
||
|
|
" elif x[i,1] == 180:\n",
|
||
|
|
" s.append(np.array([x[i,0] + 180, 360 - x[i,1]]))\n",
|
||
|
|
" s_id.append(i)\n",
|
||
|
|
"\n",
|
||
|
|
"stim_val_or_loop = np.concatenate((y, np.array(s)))\n",
|
||
|
|
"# avg_firing_or_loop = avg_firing_rate['pair'][:, list(range(len(y))) + s_id]\n",
|
||
|
|
"\n",
|
||
|
|
"arg = np.lexsort((stim_val_or_loop[:,1],stim_val_or_loop[:,0]))\n",
|
||
|
|
"stim_val_orientation_loop = {}\n",
|
||
|
|
"stim_val_orientation_loop['pair'] = stim_val_or_loop[arg]\n",
|
||
|
|
"# # for unit_id in range(num_unit):\n",
|
||
|
|
"# # avg_firing_or_loop[unit_id] = avg_firing_or_loop[unit_id, arg].reshape(40, 1)\n",
|
||
|
|
"stim_val_orientation_loop['orientation'] = np.unique(stim_val_orientation_loop['pair'][:,0])\n",
|
||
|
|
"stim_val_orientation_loop['phase'] = np.unique(stim_val_orientation_loop['pair'][:,1])\n",
|
||
|
|
"# np.all(stim_val['pair'][np.lexsort((stim_val['pair'][:,1], stim_val['pair'][:,0]))] == stim_val['pair'])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2880 with 40 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(10, 4, figsize=(5*4, 10*4))\n",
|
||
|
|
"avg_firing_rate_orientaion_loop = np.zeros((num_unit, 40, 10))\n",
|
||
|
|
"for unit_id in range(40):\n",
|
||
|
|
" avg_firing_rate_orientaion_loop[unit_id] = np.vstack((avg_firing_rate_pair[unit_id, :, :10], avg_firing_rate_pair[unit_id, :, 10:]))\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].imshow(avg_firing_rate_orientaion_loop[unit_id], interpolation='gaussian', aspect=0.25)\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xticks(np.arange(10)[::3])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xticklabels(stim_val_orientation_loop['phase'][::3])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_yticks(np.arange(40)[::5])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_yticklabels(stim_val_orientation_loop['orientation'][::5])\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_xticks([])\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_yticks([])\n",
|
||
|
|
"\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('phase')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('orientation')\n",
|
||
|
|
" \n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x3168 with 40 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(10,4, figsize=(4*5, 11*4))\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(stim_val_orientation_loop['orientation'], np.mean(avg_firing_rate_orientaion_loop[unit_id], axis=1))\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('orientation')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('avg firing rate / Hz')\n",
|
||
|
|
"\n",
|
||
|
|
"ax[0,0].set_title('firing rate (Hz) vs. orientation')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x3168 with 40 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(10,4, figsize=(4*5, 11*4))\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(stim_val_orientation_loop['orientation'], np.mean(avg_firing_rate_orientaion_loop[unit_id][:, :5], axis=1))\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('orientation')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('avg firing rate / Hz')\n",
|
||
|
|
"\n",
|
||
|
|
"ax[0,0].set_title('firing rate (Hz) vs. orientation')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x3168 with 40 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(10,4, figsize=(4*5, 11*4))\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(stim_val_orientation_loop['orientation'], np.mean(avg_firing_rate_orientaion_loop[unit_id][:, 5:], axis=1))\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('orientation')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('avg firing rate / Hz')\n",
|
||
|
|
"\n",
|
||
|
|
"ax[0,0].set_title('firing rate (Hz) vs. orientation')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 30,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x3168 with 40 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(10,4, figsize=(4*5, 11*4))\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(stim_val_orientation_loop['phase'][:-1], np.mean(avg_firing_rate_orientaion_loop[unit_id], axis=0))\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('phase')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('avg firing rate / Hz')\n",
|
||
|
|
"\n",
|
||
|
|
"ax[0,0].set_title('firing rate (Hz) vs. phase')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Preferred stimulus"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "NameError",
|
||
|
|
"evalue": "name 'avg_firing_rate_orientaion_loop' is not defined",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
|
||
|
|
"\u001b[1;32mc:\\Users\\arash\\Documents\\Uni\\Research Project (Topology)\\staticgrating-phasefreq-topology\\02_Tuning_Curves_Receptive_Fields.ipynb Cell 35'\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000034?line=1'>2</a>\u001b[0m R_pref \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros(num_unit)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000034?line=2'>3</a>\u001b[0m \u001b[39mfor\u001b[39;00m unit_id \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(num_unit):\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000034?line=3'>4</a>\u001b[0m R_pref[unit_id] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39margmax(avg_firing_rate_orientaion_loop[unit_id], axis\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m)\n",
|
||
|
|
"\u001b[1;31mNameError\u001b[0m: name 'avg_firing_rate_orientaion_loop' is not defined"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# orientation\n",
|
||
|
|
"R_pref = np.zeros(num_unit)\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" R_pref[unit_id] = np.argmax(avg_firing_rate_orientaion_loop[unit_id], axis=0)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Fisher Information"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# tuning index f'(s)**2/f(s)\n",
|
||
|
|
"def FisherInfo(stim, tuning_curve, variance, dim_stim = 1): # lower bound for fisher information\n",
|
||
|
|
" if dim_stim == 1:\n",
|
||
|
|
"\n",
|
||
|
|
" diff = np.diff(tuning_curve)/np.diff(stim)\n",
|
||
|
|
" return diff**2/(variance[:-1] + np.diff(variance)/2)\n",
|
||
|
|
"\n",
|
||
|
|
" elif dim_stim == 2:\n",
|
||
|
|
" diff = []\n",
|
||
|
|
" for i in range(2):\n",
|
||
|
|
" tuning_diff = np.diff(tuning_curve, axis=i)\n",
|
||
|
|
" dim = np.size(tuning_curve, axis=i)\n",
|
||
|
|
" stim_diff = np.diff(stim[i])\n",
|
||
|
|
" diff.append([tuning_diff[j] for j in range(dim)])\n",
|
||
|
|
"\n",
|
||
|
|
" Jacobian = np.array([diff[0][:,:-1], diff[1][:-1,:]])\n",
|
||
|
|
" Jacobian = np.transpose(Jacobian, (1,2,0))\n",
|
||
|
|
" return np.einsum('ijk,ijl->ijkl', Jacobian, Jacobian)/variance[:-1,:-1]\n",
|
||
|
|
"\n",
|
||
|
|
"a = np.arange(20).reshape(4,5)\n",
|
||
|
|
"a\n",
|
||
|
|
"diff = [np.diff(a, axis=i) for i in range(2)]\n",
|
||
|
|
"\n",
|
||
|
|
"[np.diff(a, axis=i) for i in range(2)]\n",
|
||
|
|
"Jacobian = np.array([diff[0][:,:-1], diff[1][:-1,:]])\n",
|
||
|
|
"Jacobian = np.transpose(Jacobian, (1,2,0))\n",
|
||
|
|
"np.einsum('ijk,ijl->ijkl', Jacobian, Jacobian)\n",
|
||
|
|
"\n",
|
||
|
|
"diff\n",
|
||
|
|
"FisherInfo(stim = np.arange(20).reshape(4,5), tuning_curve=a, variance=np.ones((4,5)), dim_stim = 2)\n",
|
||
|
|
"Jacobian[0,0]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Help on function take_along_axis in module numpy:\n",
|
||
|
|
"\n",
|
||
|
|
"take_along_axis(arr, indices, axis)\n",
|
||
|
|
" Take values from the input array by matching 1d index and data slices.\n",
|
||
|
|
" \n",
|
||
|
|
" This iterates over matching 1d slices oriented along the specified axis in\n",
|
||
|
|
" the index and data arrays, and uses the former to look up values in the\n",
|
||
|
|
" latter. These slices can be different lengths.\n",
|
||
|
|
" \n",
|
||
|
|
" Functions returning an index along an axis, like `argsort` and\n",
|
||
|
|
" `argpartition`, produce suitable indices for this function.\n",
|
||
|
|
" \n",
|
||
|
|
" .. versionadded:: 1.15.0\n",
|
||
|
|
" \n",
|
||
|
|
" Parameters\n",
|
||
|
|
" ----------\n",
|
||
|
|
" arr : ndarray (Ni..., M, Nk...)\n",
|
||
|
|
" Source array\n",
|
||
|
|
" indices : ndarray (Ni..., J, Nk...)\n",
|
||
|
|
" Indices to take along each 1d slice of `arr`. This must match the\n",
|
||
|
|
" dimension of arr, but dimensions Ni and Nj only need to broadcast\n",
|
||
|
|
" against `arr`.\n",
|
||
|
|
" axis : int\n",
|
||
|
|
" The axis to take 1d slices along. If axis is None, the input array is\n",
|
||
|
|
" treated as if it had first been flattened to 1d, for consistency with\n",
|
||
|
|
" `sort` and `argsort`.\n",
|
||
|
|
" \n",
|
||
|
|
" Returns\n",
|
||
|
|
" -------\n",
|
||
|
|
" out: ndarray (Ni..., J, Nk...)\n",
|
||
|
|
" The indexed result.\n",
|
||
|
|
" \n",
|
||
|
|
" Notes\n",
|
||
|
|
" -----\n",
|
||
|
|
" This is equivalent to (but faster than) the following use of `ndindex` and\n",
|
||
|
|
" `s_`, which sets each of ``ii`` and ``kk`` to a tuple of indices::\n",
|
||
|
|
" \n",
|
||
|
|
" Ni, M, Nk = a.shape[:axis], a.shape[axis], a.shape[axis+1:]\n",
|
||
|
|
" J = indices.shape[axis] # Need not equal M\n",
|
||
|
|
" out = np.empty(Ni + (J,) + Nk)\n",
|
||
|
|
" \n",
|
||
|
|
" for ii in ndindex(Ni):\n",
|
||
|
|
" for kk in ndindex(Nk):\n",
|
||
|
|
" a_1d = a [ii + s_[:,] + kk]\n",
|
||
|
|
" indices_1d = indices[ii + s_[:,] + kk]\n",
|
||
|
|
" out_1d = out [ii + s_[:,] + kk]\n",
|
||
|
|
" for j in range(J):\n",
|
||
|
|
" out_1d[j] = a_1d[indices_1d[j]]\n",
|
||
|
|
" \n",
|
||
|
|
" Equivalently, eliminating the inner loop, the last two lines would be::\n",
|
||
|
|
" \n",
|
||
|
|
" out_1d[:] = a_1d[indices_1d]\n",
|
||
|
|
" \n",
|
||
|
|
" See Also\n",
|
||
|
|
" --------\n",
|
||
|
|
" take : Take along an axis, using the same indices for every 1d slice\n",
|
||
|
|
" put_along_axis :\n",
|
||
|
|
" Put values into the destination array by matching 1d index and data slices\n",
|
||
|
|
" \n",
|
||
|
|
" Examples\n",
|
||
|
|
" --------\n",
|
||
|
|
" \n",
|
||
|
|
" For this sample array\n",
|
||
|
|
" \n",
|
||
|
|
" >>> a = np.array([[10, 30, 20], [60, 40, 50]])\n",
|
||
|
|
" \n",
|
||
|
|
" We can sort either by using sort directly, or argsort and this function\n",
|
||
|
|
" \n",
|
||
|
|
" >>> np.sort(a, axis=1)\n",
|
||
|
|
" array([[10, 20, 30],\n",
|
||
|
|
" [40, 50, 60]])\n",
|
||
|
|
" >>> ai = np.argsort(a, axis=1); ai\n",
|
||
|
|
" array([[0, 2, 1],\n",
|
||
|
|
" [1, 2, 0]])\n",
|
||
|
|
" >>> np.take_along_axis(a, ai, axis=1)\n",
|
||
|
|
" array([[10, 20, 30],\n",
|
||
|
|
" [40, 50, 60]])\n",
|
||
|
|
" \n",
|
||
|
|
" The same works for max and min, if you expand the dimensions:\n",
|
||
|
|
" \n",
|
||
|
|
" >>> np.expand_dims(np.max(a, axis=1), axis=1)\n",
|
||
|
|
" array([[30],\n",
|
||
|
|
" [60]])\n",
|
||
|
|
" >>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1)\n",
|
||
|
|
" >>> ai\n",
|
||
|
|
" array([[1],\n",
|
||
|
|
" [0]])\n",
|
||
|
|
" >>> np.take_along_axis(a, ai, axis=1)\n",
|
||
|
|
" array([[30],\n",
|
||
|
|
" [60]])\n",
|
||
|
|
" \n",
|
||
|
|
" If we want to get the max and min at the same time, we can stack the\n",
|
||
|
|
" indices first\n",
|
||
|
|
" \n",
|
||
|
|
" >>> ai_min = np.expand_dims(np.argmin(a, axis=1), axis=1)\n",
|
||
|
|
" >>> ai_max = np.expand_dims(np.argmax(a, axis=1), axis=1)\n",
|
||
|
|
" >>> ai = np.concatenate([ai_min, ai_max], axis=1)\n",
|
||
|
|
" >>> ai\n",
|
||
|
|
" array([[0, 1],\n",
|
||
|
|
" [1, 0]])\n",
|
||
|
|
" >>> np.take_along_axis(a, ai, axis=1)\n",
|
||
|
|
" array([[10, 30],\n",
|
||
|
|
" [40, 60]])\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"help(np.take_along_axis)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"C:\\Users\\arash\\AppData\\Local\\Temp\\ipykernel_14200\\163951748.py:2: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
|
||
|
|
" np.array([np.diff(avg_firing_rate_pair_normalized, axis=i) for i in range(1,3)])\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"ename": "ValueError",
|
||
|
|
"evalue": "could not broadcast input array from shape (40,19,20) into shape (40,)",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
|
||
|
|
"\u001b[1;32mc:\\Users\\arash\\Documents\\Uni\\Research Project (Topology)\\staticgrating-phasefreq-topology\\Tuning_Curves.ipynb Cell 37'\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/Tuning_Curves.ipynb#ch0000036?line=0'>1</a>\u001b[0m \u001b[39m#\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/Tuning_Curves.ipynb#ch0000036?line=1'>2</a>\u001b[0m np\u001b[39m.\u001b[39;49marray([np\u001b[39m.\u001b[39;49mdiff(avg_firing_rate_pair_normalized, axis\u001b[39m=\u001b[39;49mi) \u001b[39mfor\u001b[39;49;00m i \u001b[39min\u001b[39;49;00m \u001b[39mrange\u001b[39;49m(\u001b[39m1\u001b[39;49m,\u001b[39m3\u001b[39;49m)])\n",
|
||
|
|
"\u001b[1;31mValueError\u001b[0m: could not broadcast input array from shape (40,19,20) into shape (40,)"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#\n",
|
||
|
|
"np.array([np.diff(avg_firing_rate_pair_normalized, axis=1)[:,:,:-1], np.diff(avg_firing_rate_pair_normalized, axis=1)[:,:,:-1]]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"$ r_{est}(t) = r_0 + \\int d\\tau \\left[D_{\\phi}(\\tau)e^{i\\phi(t-\\tau)} + D_{\\theta}(\\tau)\\theta(t-\\tau)\\right] $"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### assume instantaneous phase response\n",
|
||
|
|
"$ r_{est}(t) = f_{\\phi}(\\phi(t)) + \\int d\\tau D_{\\theta}(\\tau)\\theta(t-\\tau) $\n",
|
||
|
|
"\n",
|
||
|
|
"find $D_{\\theta}(\\tau)$ that minimizes the difference between the estimate of firing rate and the actual firing rate.\n",
|
||
|
|
"\n",
|
||
|
|
"$ E = \\int dt |{r_{est}(t) - r(t)}|^2 = \\int dt \\left|f_{\\phi}(\\phi(t)) + \\int d\\tau D_{\\theta}(\\tau)\\theta(t-\\tau) - r(t)\\right|^2 \\\\ = \\int dt \\left| D(t)*\\theta(t) - \\left[r(t)-f_{\\phi}(\\phi(t))\\right]\\right|^2 $\n",
|
||
|
|
"\n",
|
||
|
|
"$ \\delta E = 2\\int dt \\left(D(t)*\\theta(t) - \\left[r(t)-f_{\\phi}(\\phi(t))\\right]\\right) (\\int d\\tau \\delta D(\\tau) \\theta(t-\\tau) ) = 2\\int d\\tau \\int dt \\left( D(t)*\\theta(t) - \\left[r(t)-f_{\\phi}(\\phi(t))\\right]\\right) \\theta(t-\\tau) \\delta D(\\tau) = 0 \\\\ \\Longleftrightarrow \\int dt \\left( D(t)*\\theta(t) - \\left[r(t)-f_{\\phi}(\\phi(t))\\right]\\right) \\theta(t-\\tau) = 0 \\Rightarrow \\int dt \\left( \\int ds D(s)\\theta(t-s) - \\left[r(t)-f_{\\phi}(\\phi(t))\\right]\\right) \\theta(t-\\tau) = \\int ds D(s) \\int dt \\theta(t-s)\\theta(t-\\tau) - \\int dt \\left[r(t)-f_{\\phi}(\\phi(t))\\right] \\theta(t-\\tau) = \\int ds D(s) C_{\\theta, \\theta}(\\tau-s) - C_{r-f_{\\phi},\\theta}(-\\tau) = 0 \\Rightarrow D(\\tau) * C_{\\theta, \\theta}(\\tau) = C_{r-f_{\\phi},\\theta}(-\\tau)$\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Time difference histogram"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2016 with 28 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"key = 'phase'\n",
|
||
|
|
"fig, ax = plt.subplots(7,4,figsize=(5*4, 7*4))\n",
|
||
|
|
"X, Y = np.meshgrid(stim_val[key], tau_id_range)\n",
|
||
|
|
"plt.set_cmap('jet')\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].contour(X, Y, stim_hist[key][unit_id])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel(key_symbol[key]+'/degrees')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('$\\\\tau/(ms)$')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1728x2016 with 28 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"key = 'orientation'\n",
|
||
|
|
"fig, ax = plt.subplots(7,4,figsize=(6*4, 7*4))\n",
|
||
|
|
"X, Y = np.meshgrid(stim_val[key], tau_id_range)\n",
|
||
|
|
"plt.set_cmap('jet')\n",
|
||
|
|
"tau_id = 60\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].errorbar(stim_val[key], stim_hist[key][unit_id, tau_id], yerr=np.sqrt(stim_hist[key][unit_id, tau_id]/s[unit_id, 0]))\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel(key_symbol[key]+'/degrees')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('$\\\\tau/(ms)$')\n",
|
||
|
|
" ymin = np.min(stim_hist[key][unit_id])*0.95\n",
|
||
|
|
" ymax = np.max(stim_hist[key][unit_id])*1.05\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('$\\mathcal{P}($'+key_symbol[key]+'$|\\\\tau)$')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel(key_symbol[key])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylim([ymin, ymax])\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"subspace reverse correlation on orientation only is equivalent to integrating over phase for subspace reverse correlation on pair stimuli"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# np.mean(stim_hist['pair'][0,30].reshape(20,20), axis=1)/stim_hist['orientation'][0,30] # proportional to integration"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# SNR"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 213,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"snr = {}\n",
|
||
|
|
"for key in key_list:\n",
|
||
|
|
" snr[key] = np.mean(stim_hist[key], axis=2)/np.std(stim_hist['phase'], axis=2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 214,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x360 with 3 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# SNR imshow\n",
|
||
|
|
"\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"fig, ax = plt.subplots(1,3,figsize=(18, 5))\n",
|
||
|
|
"for i, key in enumerate(key_list):\n",
|
||
|
|
" ax[i].imshow(snr[key], aspect=6)\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].plot(stim_hist_KL_div[key][unit_id], label='Entropy of stimulus distribution')\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].plot(var_tuning[key][unit_id], label='variance: equivalent to square of euclidean distance from uniform distribution')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[i].set_ylabel('unit')\n",
|
||
|
|
" ax[i].set_xlabel('$\\\\tau/(ms)$')\n",
|
||
|
|
" ax[i].set_title(key)\n",
|
||
|
|
" # ax[].legend(loc=(1.5,1.2))\n",
|
||
|
|
"\n",
|
||
|
|
"plt.suptitle('SNR')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Optimal Time\n",
|
||
|
|
"## Tuning score"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 20,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from scipy.stats import zscore\n",
|
||
|
|
"from matplotlib import cm\n",
|
||
|
|
"from matplotlib import gridspec\n",
|
||
|
|
"import scipy.signal as signal"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### filter to find max score and argmax (optimal time)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# test filter\n",
|
||
|
|
"from scipy.signal import butter, lfilter, freqz, filtfilt\n",
|
||
|
|
"\n",
|
||
|
|
"def butter_lowpass(cutoff, fs, order=5):\n",
|
||
|
|
" nyq = 0.5 * fs\n",
|
||
|
|
" normal_cutoff = cutoff / nyq\n",
|
||
|
|
" b, a = butter(order, normal_cutoff, btype='low', analog=False)\n",
|
||
|
|
" return b, a\n",
|
||
|
|
"\n",
|
||
|
|
"def butter_lowpass_filter(data, cutoff, fs, order=5, no_lag=True):\n",
|
||
|
|
" b, a = butter_lowpass(cutoff, fs, order=order)\n",
|
||
|
|
" if no_lag:\n",
|
||
|
|
" y = filtfilt(b, a, data)\n",
|
||
|
|
" else:\n",
|
||
|
|
" y = lfilter(b, a, data)\n",
|
||
|
|
"\n",
|
||
|
|
" return y\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"# Setting standard filter requirements.\n",
|
||
|
|
"order = 6\n",
|
||
|
|
"fs = 1000 # Hz - 1ms time resolution \n",
|
||
|
|
"cutoff = 30 # Hz - 50ms ~ trial time\n",
|
||
|
|
"# 80 ms noise of tuning score; since we expect tuning score to vary with time order of 80 ms, this local measure of noise might be useful\n",
|
||
|
|
"\n",
|
||
|
|
"b, a = butter_lowpass(cutoff, fs, order)\n",
|
||
|
|
"\n",
|
||
|
|
"# Plotting the frequency response.\n",
|
||
|
|
"w, h = freqz(b, a, worN=8000)\n",
|
||
|
|
"plt.subplot(2, 1, 1)\n",
|
||
|
|
"plt.plot(0.5*fs*w/np.pi, np.abs(h), 'b')\n",
|
||
|
|
"plt.plot(cutoff, 0.5*np.sqrt(2), 'ko')\n",
|
||
|
|
"plt.axvline(cutoff, color='k')\n",
|
||
|
|
"plt.xlim(0, 0.5*fs)\n",
|
||
|
|
"plt.title(\"Lowpass Filter Frequency Response\")\n",
|
||
|
|
"plt.xlabel('Frequency [Hz]')\n",
|
||
|
|
"plt.grid()\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"# Creating the data for filteration\n",
|
||
|
|
"n = len(tau_id_range)\n",
|
||
|
|
"dt = 0.001\n",
|
||
|
|
"T = n*dt # value taken in seconds\n",
|
||
|
|
"# n = int(T * fs) # indicates total samples\n",
|
||
|
|
"t = np.linspace(0, T, n, endpoint=False)\n",
|
||
|
|
"\n",
|
||
|
|
"data = np.sin(1.2*2*np.pi*t) + 1.5*np.cos(9*2*np.pi*t) + 0.5*np.sin(50.0*2*np.pi*t)\n",
|
||
|
|
"# data = tuning_score['pair']['var']['z'][0]\n",
|
||
|
|
"\n",
|
||
|
|
"# Filtering and plotting\n",
|
||
|
|
"y = butter_lowpass_filter(data, cutoff, fs, order, no_lag=True)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.subplot(2, 1, 2)\n",
|
||
|
|
"plt.plot(t, data, 'b-', label='data')\n",
|
||
|
|
"plt.plot(t, y, 'g-', linewidth=2, label='filtered data')\n",
|
||
|
|
"plt.xlabel('Time [sec]')\n",
|
||
|
|
"plt.grid()\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"\n",
|
||
|
|
"plt.subplots_adjust(hspace=0.35)\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def KL_div_unif(p):\n",
|
||
|
|
" n = len(p) + 1\n",
|
||
|
|
" p_non = 0\n",
|
||
|
|
" s_non = 0\n",
|
||
|
|
"\n",
|
||
|
|
" if np.sum(p)<1:\n",
|
||
|
|
" p_non = 1-np.sum(p)\n",
|
||
|
|
" if p_non > 0:\n",
|
||
|
|
" s_non = -p_non * np.log10(p_non)\n",
|
||
|
|
"\n",
|
||
|
|
" nonzero_id = np.where(p != 0)\n",
|
||
|
|
" p_nonzero = p[nonzero_id]\n",
|
||
|
|
" if np.any(p_nonzero<=0):\n",
|
||
|
|
" print('negative probability')\n",
|
||
|
|
"\n",
|
||
|
|
" if p_non >1 or np.any(p_nonzero>1):\n",
|
||
|
|
" print('probability greater than one')\n",
|
||
|
|
" \n",
|
||
|
|
" s_stim = -p_nonzero * np.log10(p_nonzero)\n",
|
||
|
|
" s = np.sum(s_stim) + s_non\n",
|
||
|
|
" if s<0:\n",
|
||
|
|
" print('negative entropy')\n",
|
||
|
|
"\n",
|
||
|
|
" \n",
|
||
|
|
" return np.log10(n) - s\n",
|
||
|
|
"\n",
|
||
|
|
"def mod_depth(p):\n",
|
||
|
|
" return np.max(p) - np.min(p)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"def sparsity(p):\n",
|
||
|
|
" return 1 - len(p) * np.sum(p)**2/np.sum(p**2)\n",
|
||
|
|
"\n",
|
||
|
|
"# def half_BW(p): # The bandwidth is a local measure of selectivity around the peak of the tuning curve.\n",
|
||
|
|
"# half_height = np.min(np.log10(p))\n",
|
||
|
|
"# np.min(p, key=lambda x:(x-))\n",
|
||
|
|
"# return 1/2\n",
|
||
|
|
"\n",
|
||
|
|
"def filt(p_series):\n",
|
||
|
|
" # filter tuning score\n",
|
||
|
|
" q = np.ndarray(p_series.shape)\n",
|
||
|
|
" for unit_id in range(len(p_series)):\n",
|
||
|
|
" q[unit_id] = butter_lowpass_filter(p_series[unit_id], cutoff, fs, order, no_lag=True)\n",
|
||
|
|
"\n",
|
||
|
|
" return q"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"score_key_list = ['kl', 'var', 'sparsity']\n",
|
||
|
|
"score_key_method = {'kl':KL_div_unif, 'var':np.var, 'mod_depth':mod_depth, 'sparsity':sparsity}\n",
|
||
|
|
"\n",
|
||
|
|
"score_processing_list = ['raw','z','z_filt']\n",
|
||
|
|
"score_processing_method = {'raw':lambda x:x, 'z': lambda x:zscore(x, axis=1), 'z_filt': filt}\n",
|
||
|
|
"tuning_score = {}\n",
|
||
|
|
"\n",
|
||
|
|
"for key in key_list:\n",
|
||
|
|
" tuning_score[key] = {}\n",
|
||
|
|
" for score_key in score_key_list:\n",
|
||
|
|
" tuning_score[key][score_key] = {}\n",
|
||
|
|
" for score_processing in score_processing_list:\n",
|
||
|
|
" tuning_score[key][score_key][score_processing] = np.zeros((num_unit, len(tau_id_range)))\n",
|
||
|
|
"\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" for tau_id in tau_id_range:\n",
|
||
|
|
" tuning_score[key][score_key]['raw'][unit_id, tau_id] = score_key_method[score_key](stim_hist[key][unit_id, tau_id])\n",
|
||
|
|
" # tuning_score['var'][key][unit_id, tau_id] = np.var(stim_hist[key][unit_id, tau_id])\n",
|
||
|
|
" # tuning_score['kl'][key][unit_id, tau_id] = KL_div_unif(stim_hist[key][unit_id, tau_id])\n",
|
||
|
|
"\n",
|
||
|
|
"# normalize KL div for each unit\n",
|
||
|
|
"\n",
|
||
|
|
"for key in key_list:\n",
|
||
|
|
" for score_key in score_key_list:\n",
|
||
|
|
" for i, score_processing in enumerate(score_processing_list[1:]):\n",
|
||
|
|
" # tuning_score[key][score_key][score_processing] = (score_processing_method[score_processing])(tuning_score[key][score_key][score_processing_list[i-1]])\n",
|
||
|
|
" tuning_score[key]['kl']['z'] = zscore(tuning_score[key]['kl']['raw'], axis=1)\n",
|
||
|
|
" tuning_score[key]['var']['z'] = zscore(tuning_score[key]['var']['raw'], axis=1)\n",
|
||
|
|
" # tuning_score[key]['mod_depth']['z'] = zscore(tuning_score[key]['mod_depth']['raw'], axis=1)\n",
|
||
|
|
" tuning_score[key]['sparsity']['z'] = zscore(tuning_score[key]['sparsity']['raw'], axis=1)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"# filter tuning score\n",
|
||
|
|
"for key in key_list:\n",
|
||
|
|
" for score_key in score_key_list:\n",
|
||
|
|
" for i, score_processing in enumerate(score_processing_list[1:]):\n",
|
||
|
|
" tuning_score[key][score_key]['z_filt'] = np.ndarray((num_unit, len(tau_id_range)))\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" tuning_score[key][score_key]['z_filt'][unit_id] = butter_lowpass_filter(tuning_score[key][score_key]['z'][unit_id], cutoff, fs, order, no_lag=True)\n",
|
||
|
|
"\n",
|
||
|
|
"# tuning_score = {}\n",
|
||
|
|
"# for key in key_list:\n",
|
||
|
|
"# tuning_score[key] = np.max(tuning_score['kl'][key], axis=1)\n",
|
||
|
|
"\n",
|
||
|
|
"# unit_tuning_order = np.argsort(tuning_score['pair'])[::-1]\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x360 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# KL div comparison across units\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"score_key = 'kl'\n",
|
||
|
|
"score_processing = 'z_filt'\n",
|
||
|
|
"fig, ax = plt.subplots(1,3,figsize=(18, 5))\n",
|
||
|
|
"for i, key in enumerate(key_list):\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" im = ax[i].plot(tuning_score[key][score_key][score_processing][unit_id], c=cmap_hot(sorted_spike_num[unit_id]/np.max(sorted_spike_num)))\n",
|
||
|
|
" # ax[i].plot(tuning_score[key][score_key][unit_id],)\n",
|
||
|
|
"\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].plot(tuning_score[key][score_key][unit_id], label='Entropy of stimulus distribution')\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].plot(var_tuning[key][unit_id], label='variance: equivalent to square of euclidean distance from uniform distribution')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[i].set_ylabel('tuning score')\n",
|
||
|
|
" ax[i].set_xlabel('$\\\\tau/(ms)$')\n",
|
||
|
|
" # ax[i].set_ylim([0,1])\n",
|
||
|
|
" ax[i].set_title(key)\n",
|
||
|
|
" # ax[].legend(loc=(1.5,1.2))\n",
|
||
|
|
"\n",
|
||
|
|
"cbar_ax = fig.add_axes([0.3, -0.1, 0.5, 0.1])\n",
|
||
|
|
"fig.colorbar(cm.ScalarMappable(cmap=cmap_hot), cax=cbar_ax, orientation='horizontal', label='spike count')\n",
|
||
|
|
"plt.suptitle('tuning score: ' + score_key)\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x720 with 9 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# normalized tuning score imshow\n",
|
||
|
|
"\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"fig, ax = plt.subplots(3,3,figsize=(18, 10))\n",
|
||
|
|
"plt.set_cmap('jet')\n",
|
||
|
|
"score_mode = 'z_filt'\n",
|
||
|
|
"\n",
|
||
|
|
"for j, score_key in enumerate(score_key_list):\n",
|
||
|
|
" for i, key in enumerate(key_list):\n",
|
||
|
|
" ax[j, i].imshow(tuning_score[key][score_key][score_mode], aspect=8)\n",
|
||
|
|
" # ax[j, i].imshow(stim_hist_KL_div_norm[key], aspect=6)\n",
|
||
|
|
"\n",
|
||
|
|
" ax[j, i].set_ylabel('unit')\n",
|
||
|
|
" ax[j, i].set_xlabel('$\\\\tau/(ms)$')\n",
|
||
|
|
" ax[j, i].set_title(key)\n",
|
||
|
|
" ax[j, i].set_title(key + ' score: ' + score_key + '_'+ score_mode)\n",
|
||
|
|
" # ax[].legend(loc=(1.5,1.2))\n",
|
||
|
|
"\n",
|
||
|
|
"plt.suptitle('normalized tuning score')\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# all local maxima tuning score across time delay\n",
|
||
|
|
"\n",
|
||
|
|
"def local_max(y):\n",
|
||
|
|
" # returns local maxima \n",
|
||
|
|
" arg_locmax = np.where((y[1:-1] >= y[:-2]) & (y[1:-1] >= y[2:]))\n",
|
||
|
|
" sort_argmax = arg_locmax[0][np.argsort(y[arg_locmax])[::-1]]\n",
|
||
|
|
" return np.array([sort_argmax, y[sort_argmax]])\n",
|
||
|
|
"\n",
|
||
|
|
"max_tuning_score = {}\n",
|
||
|
|
"for key in key_list:\n",
|
||
|
|
" max_tuning_score[key] = {}\n",
|
||
|
|
" for score_key in score_key_list:\n",
|
||
|
|
" max_tuning_score[key][score_key] = {}\n",
|
||
|
|
" for score_processing in score_processing_list:\n",
|
||
|
|
" max_tuning_score[key][score_key][score_processing] = np.ndarray(num_unit, dtype=object)\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" max_tuning_score[key][score_key][score_processing][unit_id] = local_max(tuning_score[key][score_key][score_processing][unit_id])\n",
|
||
|
|
"\n",
|
||
|
|
"optimal_time = {}\n",
|
||
|
|
"for key in key_list:\n",
|
||
|
|
" optimal_time[key] = {}\n",
|
||
|
|
" for score_key in score_key_list:\n",
|
||
|
|
" optimal_time[key][score_key]= {}\n",
|
||
|
|
" for score_processing in score_processing_list:\n",
|
||
|
|
" optimal_time[key][score_key][score_processing] = pd.DataFrame([max_tuning_score[key][score_key][score_processing][unit_id][:,0] for unit_id in range(num_unit)], columns=['optimal_time', 'max_tuning_score']).astype({'optimal_time':int})\n",
|
||
|
|
" first_argmax = [np.argmin(max_tuning_score[key][score_key][score_processing][unit_id][0,:]) for unit_id in range(num_unit)]\n",
|
||
|
|
" optimal_time[key][score_key][score_processing]['first_argmax'] = [int(max_tuning_score[key][score_key][score_processing][unit_id][0, first_argmax[unit_id]]) for unit_id in range(num_unit)]\n",
|
||
|
|
" optimal_time[key][score_key][score_processing]['first_max'] = [max_tuning_score[key][score_key][score_processing][unit_id][1, first_argmax[unit_id]] for unit_id in range(num_unit)]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 35,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"C:\\Users\\arash\\AppData\\Local\\Temp\\ipykernel_25088\\1394730034.py:14: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
|
||
|
|
" w_mode = ['optimal_time', 'first_argmax'][winner_mode[unit_id]]\n",
|
||
|
|
"C:\\Users\\arash\\AppData\\Local\\Temp\\ipykernel_25088\\1394730034.py:15: SettingWithCopyWarning: \n",
|
||
|
|
"A value is trying to be set on a copy of a slice from a DataFrame\n",
|
||
|
|
"\n",
|
||
|
|
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
|
|
" optimal_time['pair'][score_key][score_processing]['opt_time'][unit_id] = int(optimal_time['pair'][score_key][score_processing][w_mode][unit_id])\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# optimal time: trade off between first maximum and global maximum\n",
|
||
|
|
"\n",
|
||
|
|
"alpha_first = 8\n",
|
||
|
|
"\n",
|
||
|
|
"for score_key in score_key_list:\n",
|
||
|
|
" for score_processing in score_processing_list:\n",
|
||
|
|
" optimal_time['pair'][score_key][score_processing]['max_score'] = optimal_time['pair'][score_key][score_processing]['max_tuning_score'] - alpha_first * (optimal_time['pair'][score_key][score_processing]['optimal_time'])/300\n",
|
||
|
|
" optimal_time['pair'][score_key][score_processing]['first_score'] = optimal_time['pair'][score_key][score_processing]['first_max'] - alpha_first * (optimal_time['pair'][score_key][score_processing]['first_argmax'])/300\n",
|
||
|
|
"\n",
|
||
|
|
" # optimal_time['pair'][score_key][score_processing]['opt_time'] = np.zeros(num_unit, dtype=int)\n",
|
||
|
|
" winner_mode = optimal_time['pair'][score_key][score_processing]['first_score'] > optimal_time['pair'][score_key][score_processing]['max_score']\n",
|
||
|
|
" optimal_time['pair'][score_key][score_processing]['opt_time'] = np.zeros(num_unit, dtype=int)\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" w_mode = ['optimal_time', 'first_argmax'][winner_mode[unit_id]]\n",
|
||
|
|
" optimal_time['pair'][score_key][score_processing]['opt_time'][unit_id] = int(optimal_time['pair'][score_key][score_processing][w_mode][unit_id])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2520 with 28 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"key = 'pair'\n",
|
||
|
|
"score_processing = 'z_filt'\n",
|
||
|
|
"opt_time_mode = 'optimal_time'\n",
|
||
|
|
"row_num = 7\n",
|
||
|
|
"fig = plt.figure(figsize=(4*5, row_num*5))\n",
|
||
|
|
"gs0 = gridspec.GridSpec(row_num, 4, figure=fig)\n",
|
||
|
|
"color_list = ['b','g','r']\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" r = unit_id//4\n",
|
||
|
|
" c = unit_id%4\n",
|
||
|
|
"\n",
|
||
|
|
" gs00 = gridspec.GridSpecFromSubplotSpec(2, 1, subplot_spec=gs0[r, c], hspace=0)\n",
|
||
|
|
" \n",
|
||
|
|
" ax0 = fig.add_subplot(gs00[0, :])\n",
|
||
|
|
" for i, score_key in enumerate(score_key_list):\n",
|
||
|
|
" ax0.plot(tuning_score[key][score_key][score_processing][unit_id], label=score_key, color=color_list[i]) # 'zscore KL divergence of stimulus distribution from uniform distribution'\n",
|
||
|
|
" ax0.vlines(optimal_time[key][score_key][score_processing][opt_time_mode][unit_id], -4, 4, color=color_list[i])\n",
|
||
|
|
"\n",
|
||
|
|
" # ax0.plot(tuning_score[key]['var_z_filt'][unit_id], label='var' , color='r') # label='zscore variance: equivalent to square of euclidean distance from uniform distribution'\n",
|
||
|
|
" # ax0.plot(tuning_score[key]['mod_depth_z_filt'][unit_id], label='mod depth', color='g')\n",
|
||
|
|
" # ax0.plot(tuning_score[key]['sparsity_z_filt'][unit_id], label='sparsity', color='k')\n",
|
||
|
|
" # ax0.plot(tuning_score[key]['var_z'][unit_id], color='r')\n",
|
||
|
|
"\n",
|
||
|
|
" # ax0.vlines(optimal_time[key]['var_z_filt']['first_argmax'][unit_id], -4, 4, color='r')\n",
|
||
|
|
" # ax0.vlines(optimal_time[key]['mod_depth_z_filt']['first_argmax'][unit_id], -4, 4, color='r')\n",
|
||
|
|
" # ax0.vlines(optimal_time[key]['sparsity_z_filt']['first_argmax'][unit_id], -4, 4, color='r')\n",
|
||
|
|
"\n",
|
||
|
|
" ax0.set_ylabel('tuning score')\n",
|
||
|
|
" ax0.set_xlabel('$\\\\tau/(ms)$')\n",
|
||
|
|
" ax0.set_title('unit %d'%unit_id + ', spike num: %d'%sorted_spike_num[unit_id])\n",
|
||
|
|
"\n",
|
||
|
|
" # ax0.set_ylim([0,1])\n",
|
||
|
|
" ax0.legend(loc=(.5,1.2))\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 41,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x720 with 9 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# compare units based on tuning score\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"score_processing = 'z_filt'\n",
|
||
|
|
"fig, ax = plt.subplots(3,3,figsize=(18, 10))\n",
|
||
|
|
"for i, key in enumerate(key_list):\n",
|
||
|
|
" for j, score_key in enumerate(score_key_list):\n",
|
||
|
|
" ax[j, i].bar(range(num_unit), [tuning_score[key][score_key][score_processing][unit_id,int(optimal_time[key][score_key][score_processing]['optimal_time'][unit_id])] for unit_id in range(num_unit)]) #/np.max(max_tuning_score[key][score_key][score_processing]))\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].plot(stim_hist_KL_div[key][unit_id], label='Entropy of stimulus distribution')\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].plot(var_tuning[key][unit_id], label='variance: equivalent to square of euclidean distance from uniform distribution')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[j, i].set_ylabel('relative tuning score')\n",
|
||
|
|
" ax[j, i].set_xlabel('$unit$')\n",
|
||
|
|
" # ax[j, i].set_ylim([0,1])\n",
|
||
|
|
" ax[j, i].set_title(key)\n",
|
||
|
|
" # ax[].legend(loc=(1.5,1.2))\n",
|
||
|
|
"\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# # reoder based on tuning score\n",
|
||
|
|
"# unit_tuning_order = np.argsort(tuning_score['orientation'])[::-1]\n",
|
||
|
|
"# unit_tuning2rate_order = np.argsort(unit_tuning_order)\n",
|
||
|
|
"\n",
|
||
|
|
"# def reorder_unitsO(unit_tuning_order):\n",
|
||
|
|
"# for key in key_list:\n",
|
||
|
|
"\n",
|
||
|
|
"# stim_hist[key] = stim_hist[key][unit_tuning_order]\n",
|
||
|
|
"# stim_hist_KL_div[key] = stim_hist_KL_div[key][unit_tuning_order]\n",
|
||
|
|
"# stim_hist_KL_div_norm[key] = stim_hist_KL_div_norm[key][unit_tuning_order]\n",
|
||
|
|
"# tuning_score[key] = tuning_score[key][unit_tuning_order]\n",
|
||
|
|
"# var_tuning[key] = var_tuning[key][unit_tuning_order]\n",
|
||
|
|
"# snr[key] = snr[key][unit_tuning_order]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 42,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2520 with 84 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# selectivity time course and optimal delay\n",
|
||
|
|
"key = 'pair'\n",
|
||
|
|
"score_key = 'var'\n",
|
||
|
|
"score_processing = 'z_filt'\n",
|
||
|
|
"opt_time = optimal_time['pair'][score_key][score_processing]['optimal_time']\n",
|
||
|
|
"\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"pair_optimal_time = True # bool\n",
|
||
|
|
"opt_time_mode = ['optimal_time', 'first_argmax', 'opt_time'][0]\n",
|
||
|
|
"\n",
|
||
|
|
"row_num = 7\n",
|
||
|
|
"\n",
|
||
|
|
"fig = plt.figure(figsize=(4*5, row_num*5))\n",
|
||
|
|
"gs0 = gridspec.GridSpec(row_num, 4, figure=fig)\n",
|
||
|
|
"\n",
|
||
|
|
"X, Y = {}, {}\n",
|
||
|
|
"for key in key_list[1:]:\n",
|
||
|
|
" X[key], Y[key] = np.meshgrid(stim_val[key], -tau_id_range)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.set_cmap('jet')\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" r = unit_id//4\n",
|
||
|
|
" c = unit_id%4\n",
|
||
|
|
"\n",
|
||
|
|
" gs00 = gridspec.GridSpecFromSubplotSpec(3, 1, subplot_spec=gs0[r, c], hspace=0)\n",
|
||
|
|
"\n",
|
||
|
|
" key = 'phase'\n",
|
||
|
|
" ax0 = fig.add_subplot(gs00[0, :])\n",
|
||
|
|
" ax0.contour(Y[key], X[key], stim_hist[key][unit_id])\n",
|
||
|
|
" # ax0.set_ylabel(key_symbol[key]+'/degrees')\n",
|
||
|
|
" ax0.set_ylabel(key)\n",
|
||
|
|
" ax0.set_title('unit %d'%unit_id + ', spike num: %d'%sorted_spike_num[unit_id])\n",
|
||
|
|
" plt.setp(ax0.get_xticklabels(), visible=False)\n",
|
||
|
|
"\n",
|
||
|
|
" # t0 = -optimal_time[[key, 'pair'][pair_optimal_time]][score_key][score_processing][opt_time_mode][unit_id]\n",
|
||
|
|
" t0 = -optimal_time[key][score_key][score_processing]['optimal_time'][unit_id]\n",
|
||
|
|
" ax0.vlines(t0, 0, max(stim_val[key]), color='b')\n",
|
||
|
|
" ax0.axvspan(t0-15, t0+15, alpha=0.1, color='b')\n",
|
||
|
|
"\n",
|
||
|
|
" key = 'orientation'\n",
|
||
|
|
" ax1 = fig.add_subplot(gs00[1, :], sharex=ax0)\n",
|
||
|
|
" ax1.contour(Y[key], X[key], stim_hist[key][unit_id])\n",
|
||
|
|
" # ax1.set_ylabel(key_symbol[key]+'/degrees')\n",
|
||
|
|
" ax1.set_ylabel(key)\n",
|
||
|
|
" ax1.set_xlabel('$\\\\tau/(ms)$')\n",
|
||
|
|
" \n",
|
||
|
|
" plt.subplots_adjust(hspace=0)\n",
|
||
|
|
"\n",
|
||
|
|
" # t0 = -optimal_time[[key, 'pair'][pair_optimal_time]][score_key][score_processing][opt_time_mode][unit_id]\n",
|
||
|
|
" t0 = -optimal_time[key][score_key][score_processing]['optimal_time'][unit_id]\n",
|
||
|
|
" ax1.vlines(t0, 0, max(stim_val[key]), color='r')\n",
|
||
|
|
" ax1.axvspan(t0-15, t0+15, alpha=0.1, color='r')\n",
|
||
|
|
"\n",
|
||
|
|
" key = 'pair'\n",
|
||
|
|
" ax2 = fig.add_subplot(gs00[2, :])\n",
|
||
|
|
" ax2.plot(-tau_id_range, tuning_score['orientation'][score_key][score_processing][unit_id], label='orientation', color='r', alpha=0.5) # 'zscore KL divergence of stimulus distribution from uniform distribution'\n",
|
||
|
|
" ax2.plot(-tau_id_range, tuning_score['phase'][score_key][score_processing][unit_id], label='phase', color='b', alpha=0.5) # 'zscore KL divergence of stimulus distribution from uniform distribution'\n",
|
||
|
|
" ax2.plot(-tau_id_range, tuning_score[key][score_key][score_processing][unit_id], label='joint', color='k', alpha=0.5) # 'zscore KL divergence of stimulus distribution from uniform distribution'\n",
|
||
|
|
"\n",
|
||
|
|
" ax2.vlines(-optimal_time[key][score_key][score_processing][opt_time_mode][unit_id], -4, 4, color='k')\n",
|
||
|
|
" ax2.legend(loc=(.5,-1.2))\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 48,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# optimal delay histogram\n",
|
||
|
|
"h, tau = np.histogram(optimal_time['pair']['var']['z_filt']['opt_time'])\n",
|
||
|
|
"bin_size = np.diff(tau[:2])\n",
|
||
|
|
"plt.bar(tau[:-1]+bin_size/2, h/40, width=bin_size)\n",
|
||
|
|
"plt.xlabel('optimal time delay / (ms)')\n",
|
||
|
|
"plt.ylabel('percentage of units')\n",
|
||
|
|
"plt.title('Optimal delay histogram')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 48,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([ 6, 1, 23, 16, 10, 7, 21, 24, 0, 11, 18, 19, 22, 8, 25, 13, 15,\n",
|
||
|
|
" 12, 5, 4, 27, 26, 14, 17, 2, 9, 3, 20], dtype=int64)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 48,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"np.argsort(np.max(var_tuning['pair'], axis=1))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 49,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([ 6, 1, 23, 16, 10, 7, 21, 24, 0, 11, 18, 19, 22, 8, 25, 13, 15,\n",
|
||
|
|
" 12, 5, 4, 27, 26, 14, 17, 2, 9, 3, 20], dtype=int64)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 49,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"np.argsort(np.max(var_tuning['pair'], axis=1))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 118,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "NameError",
|
||
|
|
"evalue": "name 'var_tuning' is not defined",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
|
||
|
|
"\u001b[1;32mc:\\Users\\arash\\Documents\\Uni\\Research Project (Topology)\\staticgrating-phasefreq-topology\\02_Tuning_Curves_Receptive_Fields.ipynb Cell 66'\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000082?line=4'>5</a>\u001b[0m fig, ax \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39msubplots(\u001b[39m1\u001b[39m,\u001b[39m3\u001b[39m,figsize\u001b[39m=\u001b[39m(\u001b[39m18\u001b[39m, \u001b[39m5\u001b[39m))\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000082?line=5'>6</a>\u001b[0m \u001b[39mfor\u001b[39;00m i, key \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(key_list):\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000082?line=6'>7</a>\u001b[0m ax[i]\u001b[39m.\u001b[39mimshow(zscore(var_tuning[key]\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m))\u001b[39m.\u001b[39mreshape(num_unit,\u001b[39m300\u001b[39m)[np\u001b[39m.\u001b[39margsort(np\u001b[39m.\u001b[39mmax(var_tuning[key], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m))], aspect\u001b[39m=\u001b[39m\u001b[39m7\u001b[39m) \u001b[39m# ax[unit_id//4, unit_id%4].plot(stim_hist_entropy[key][unit_id], label='Entropy of stimulus distribution')\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000082?line=7'>8</a>\u001b[0m \u001b[39m# ax[unit_id//4, unit_id%4].plot(var_tuning[key][unit_id], label='variance: equivalent to square of euclidean distance from uniform distribution')\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000082?line=9'>10</a>\u001b[0m ax[i]\u001b[39m.\u001b[39mset_ylabel(\u001b[39m'\u001b[39m\u001b[39munit\u001b[39m\u001b[39m'\u001b[39m)\n",
|
||
|
|
"\u001b[1;31mNameError\u001b[0m: name 'var_tuning' is not defined"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x360 with 3 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# entropy imshow\n",
|
||
|
|
"\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"fig, ax = plt.subplots(1,3,figsize=(18, 5))\n",
|
||
|
|
"for i, key in enumerate(key_list):\n",
|
||
|
|
" ax[i].imshow(zscore(var_tuning[key].reshape(-1)).reshape(num_unit,300)[np.argsort(np.max(var_tuning[key], axis=1))], aspect=7) # ax[unit_id//4, unit_id%4].plot(stim_hist_entropy[key][unit_id], label='Entropy of stimulus distribution')\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].plot(var_tuning[key][unit_id], label='variance: equivalent to square of euclidean distance from uniform distribution')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[i].set_ylabel('unit')\n",
|
||
|
|
" ax[i].set_xlabel('$\\\\tau/(ms)$')\n",
|
||
|
|
" ax[i].set_title(key)\n",
|
||
|
|
" # ax[].legend(loc=(1.5,1.2))\n",
|
||
|
|
"\n",
|
||
|
|
"plt.suptitle('normalized KL div from uniform dist')\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 53,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1296x360 with 3 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# entropy imshow\n",
|
||
|
|
"\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"fig, ax = plt.subplots(1,3,figsize=(18, 5))\n",
|
||
|
|
"for i, key in enumerate(key_list):\n",
|
||
|
|
" ax[i].imshow(zscore(var_tuning[key], axis=1), aspect=7)\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].plot(stim_hist_entropy[key][unit_id], label='Entropy of stimulus distribution')\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].plot(var_tuning[key][unit_id], label='variance: equivalent to square of euclidean distance from uniform distribution')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[i].set_ylabel('unit')\n",
|
||
|
|
" ax[i].set_xlabel('$\\\\tau/(ms)$')\n",
|
||
|
|
" ax[i].set_title(key)\n",
|
||
|
|
" # ax[].legend(loc=(1.5,1.2))\n",
|
||
|
|
"\n",
|
||
|
|
"plt.suptitle('normalized variance')\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 117,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "NameError",
|
||
|
|
"evalue": "name 'stim_hist_KL_div_norm' is not defined",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
|
||
|
|
"\u001b[1;32mc:\\Users\\arash\\Documents\\Uni\\Research Project (Topology)\\staticgrating-phasefreq-topology\\02_Tuning_Curves_Receptive_Fields.ipynb Cell 68'\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000084?line=4'>5</a>\u001b[0m optimal_time \u001b[39m=\u001b[39m {}\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000084?line=6'>7</a>\u001b[0m \u001b[39mfor\u001b[39;00m i, key \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(key_list): \n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000084?line=7'>8</a>\u001b[0m optimal_time[key] \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mkl\u001b[39m\u001b[39m'\u001b[39m:np\u001b[39m.\u001b[39margmax(stim_hist_KL_div_norm[key], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m), \u001b[39m'\u001b[39m\u001b[39mvar\u001b[39m\u001b[39m'\u001b[39m:np\u001b[39m.\u001b[39margmax(var_tuning[key], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)}\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000084?line=8'>9</a>\u001b[0m optimal_time_order[key] \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mkl\u001b[39m\u001b[39m'\u001b[39m:np\u001b[39m.\u001b[39margsort(np\u001b[39m.\u001b[39margmax(stim_hist_KL_div_norm[key], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)), \u001b[39m'\u001b[39m\u001b[39mvar\u001b[39m\u001b[39m'\u001b[39m:np\u001b[39m.\u001b[39margsort(np\u001b[39m.\u001b[39margmax(var_tuning[key], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m))}\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000084?line=10'>11</a>\u001b[0m fig, ax \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39msubplots(\u001b[39m3\u001b[39m,\u001b[39m3\u001b[39m,figsize\u001b[39m=\u001b[39m(\u001b[39m18\u001b[39m, \u001b[39m13\u001b[39m))\n",
|
||
|
|
"\u001b[1;31mNameError\u001b[0m: name 'stim_hist_KL_div_norm' is not defined"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# entropy imshow\n",
|
||
|
|
"\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"optimal_time_order = {}\n",
|
||
|
|
"optimal_time = {}\n",
|
||
|
|
"\n",
|
||
|
|
"for i, key in enumerate(key_list): \n",
|
||
|
|
" optimal_time[key] = {'kl':np.argmax(stim_hist_KL_div_norm[key], axis=1), 'var':np.argmax(var_tuning[key], axis=1)}\n",
|
||
|
|
" optimal_time_order[key] = {'kl':np.argsort(np.argmax(stim_hist_KL_div_norm[key], axis=1)), 'var':np.argsort(np.argmax(var_tuning[key], axis=1))}\n",
|
||
|
|
"\n",
|
||
|
|
"fig, ax = plt.subplots(3,3,figsize=(18, 13))\n",
|
||
|
|
"\n",
|
||
|
|
"tuning = 'var'\n",
|
||
|
|
"for i, order_key in enumerate(key_list):\n",
|
||
|
|
" for j, key in enumerate(key_list):\n",
|
||
|
|
" # order_key = 'orientation' # key\n",
|
||
|
|
" ax[i, j].imshow(stim_hist_KL_div_norm[key][optimal_time_order[order_key][tuning]], aspect=7)\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].plot(stim_hist_entropy[key][unit_id], label='Entropy of stimulus distribution')\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].plot(var_tuning[key][unit_id], label='variance: equivalent to square of euclidean distance from uniform distribution')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[i, j].set_ylabel('unit')\n",
|
||
|
|
" ax[i, j].set_xlabel('$\\\\tau/(ms)$')\n",
|
||
|
|
" ax[i, j].set_title(key + ': sorted by '+order_key)\n",
|
||
|
|
" # ax[].legend(loc=(1.5,1.2))\n",
|
||
|
|
"\n",
|
||
|
|
" \n",
|
||
|
|
"plt.suptitle('normalized KL div from uniform dist')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 261,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x936 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(2,1, figsize=(20,13))\n",
|
||
|
|
"opt_time_arr = np.array([optimal_time_order[key] for key in key_list])\n",
|
||
|
|
"\n",
|
||
|
|
"for i, key in enumerate(key_list):\n",
|
||
|
|
" ax[0].bar(np.arange(num_unit)+i/5, optimal_time[key], width=1/4, label=key)\n",
|
||
|
|
" # ax[0].scatter(np.arange(num_unit), optimal_time_order[key], label=key)\n",
|
||
|
|
"\n",
|
||
|
|
"ax[1].bar(range(num_unit), np.mean(opt_time_arr, axis=0), yerr = np.std(opt_time_arr, axis=0), label=key)\n",
|
||
|
|
"\n",
|
||
|
|
"ax[0].legend()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 176,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import scipy.signal as signal\n",
|
||
|
|
"\n",
|
||
|
|
"def local_max(y):\n",
|
||
|
|
" # returns local maxima \n",
|
||
|
|
" arg_locmax = np.where((y[1:-1] >= y[:-2]) & (y[1:-1] >= y[2:]))\n",
|
||
|
|
" sort_argmax = arg_locmax[0][np.argsort(y[arg_locmax])[::-1]]\n",
|
||
|
|
" return y[sort_argmax], sort_argmax"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 184,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"tuning_max = {}\n",
|
||
|
|
"for key in key_list:\n",
|
||
|
|
" time_diff = np.zeros(num_unit)\n",
|
||
|
|
" tuning_max[key] = np.ndarray(num_unit, dtype=object)\n",
|
||
|
|
"\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" y = var_tuning_norm[key][unit_id]\n",
|
||
|
|
" y_filtered = signal.convolve(y, np.ones(50)/50, mode='same')\n",
|
||
|
|
" tuning_max[key][unit_id] = local_max(y_filtered)\n",
|
||
|
|
" if not len(tuning_max[key][unit_id][1]):\n",
|
||
|
|
" print('no local maxima for the variance of stimulus distribution for unit:')\n",
|
||
|
|
" print(unit_id)\n",
|
||
|
|
"\n",
|
||
|
|
" \n",
|
||
|
|
" # time_diff[unit_id] = np.diff(tuning_max[key][unit_id][1][:2])[0]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 202,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"t_peak = np.zeros(num_unit)\n",
|
||
|
|
"t_peak = np.array([tuning_max['pair'][unit_id][1][0] for unit_id in range(num_unit)])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 205,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x22382828d30>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 205,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(t_peak[unit_tuning_order])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Time diff"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 153,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"t_peak = np.zeros((2, num_unit))\n",
|
||
|
|
"for i in range(2):\n",
|
||
|
|
" t_peak[i] = [var_max['pair'][unit_id][1][i] for unit_id in range(num_unit)]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 154,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x360 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(1,2,figsize=(12,5))\n",
|
||
|
|
"for i in range(2):\n",
|
||
|
|
" ax[i].hist(t_peak[i])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 155,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"bin_size = 10\n",
|
||
|
|
"t_bins = np.arange(0, 250+bin_size, bin_size)\n",
|
||
|
|
"time_diff_hist, edges = np.histogram(np.abs(time_diff), t_bins)\n",
|
||
|
|
"bins = edges[:-1] + np.diff(edges[:2])\n",
|
||
|
|
"plt.bar(bins, time_diff_hist, width=0.9*bin_size)\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 156,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "NameError",
|
||
|
|
"evalue": "name 'argmax' is not defined",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
|
||
|
|
"\u001b[1;32mc:\\Users\\arash\\Documents\\Uni\\Research Project (Topology)\\staticgrating-phasefreq-topology\\02_Tuning_Curves_Receptive_Fields.ipynb Cell 89'\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000090?line=0'>1</a>\u001b[0m plt\u001b[39m.\u001b[39mplot(y)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000090?line=1'>2</a>\u001b[0m plt\u001b[39m.\u001b[39mplot(y_filtered)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000090?line=2'>3</a>\u001b[0m plt\u001b[39m.\u001b[39mscatter(argmax[:\u001b[39m2\u001b[39m], y_max)\n",
|
||
|
|
"\u001b[1;31mNameError\u001b[0m: name 'argmax' is not defined"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(y)\n",
|
||
|
|
"plt.plot(y_filtered)\n",
|
||
|
|
"plt.scatter(argmax[:2], y_max)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 157,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"c:\\Users\\arash\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:1298: ComplexWarning: Casting complex values to real discards the imaginary part\n",
|
||
|
|
" return np.asarray(x, float)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x223fd80cf40>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 157,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"y = var_tuning['phase'][0]/np.mean(var_tuning['phase'][0])\n",
|
||
|
|
"plt.plot(np.fft.fft(y))\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 158,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"y_filtered = signal.convolve(y, np.ones(50)/50, mode='same')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 159,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.legend.Legend at 0x223fdc008e0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 159,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(y_filtered, label='filtered')\n",
|
||
|
|
"plt.plot(y)\n",
|
||
|
|
"plt.legend()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 160,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def local_max(y):\n",
|
||
|
|
" arg_locmax = np.where((y[1:-1] > y[:-2]) & (y[1:-1] > y[2:]))\n",
|
||
|
|
" return y[arg_locmax], arg_locmax"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 161,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(array([0.97644136, 1.34344875, 1.35522203, 0.98133749, 0.98127211]),\n",
|
||
|
|
" (array([ 47, 130, 140, 249, 251], dtype=int64),))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 161,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"local_max(y_filtered)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 162,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"data = y"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 49,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import numpy as np\n",
|
||
|
|
"from scipy.signal import butter, lfilter, freqz, filtfilt\n",
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"def butter_lowpass(cutoff, fs, order=5):\n",
|
||
|
|
" nyq = 0.5 * fs\n",
|
||
|
|
" normal_cutoff = cutoff / nyq\n",
|
||
|
|
" b, a = butter(order, normal_cutoff, btype='low', analog=False)\n",
|
||
|
|
" return b, a\n",
|
||
|
|
"\n",
|
||
|
|
"def butter_lowpass_filter(data, cutoff, fs, order=5, no_lag=True):\n",
|
||
|
|
" b, a = butter_lowpass(cutoff, fs, order=order)\n",
|
||
|
|
" if no_lag:\n",
|
||
|
|
" y = filtfilt(b, a, data)\n",
|
||
|
|
" else:\n",
|
||
|
|
" y = lfilter(b, a, data)\n",
|
||
|
|
"\n",
|
||
|
|
" return y\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"# Setting standard filter requirements.\n",
|
||
|
|
"order = 6\n",
|
||
|
|
"fs = 1000 # Hz - 1ms time resolution \n",
|
||
|
|
"cutoff = 20 # Hz - 50ms ~ trial time\n",
|
||
|
|
"\n",
|
||
|
|
"b, a = butter_lowpass(cutoff, fs, order)\n",
|
||
|
|
"\n",
|
||
|
|
"# Plotting the frequency response.\n",
|
||
|
|
"w, h = freqz(b, a, worN=8000)\n",
|
||
|
|
"plt.subplot(2, 1, 1)\n",
|
||
|
|
"plt.plot(0.5*fs*w/np.pi, np.abs(h), 'b')\n",
|
||
|
|
"plt.plot(cutoff, 0.5*np.sqrt(2), 'ko')\n",
|
||
|
|
"plt.axvline(cutoff, color='k')\n",
|
||
|
|
"plt.xlim(0, 0.5*fs)\n",
|
||
|
|
"plt.title(\"Lowpass Filter Frequency Response\")\n",
|
||
|
|
"plt.xlabel('Frequency [Hz]')\n",
|
||
|
|
"plt.grid()\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"# Creating the data for filteration\n",
|
||
|
|
"n = len(tau_id_range)\n",
|
||
|
|
"dt = 0.001\n",
|
||
|
|
"T = n*dt # value taken in seconds\n",
|
||
|
|
"# n = int(T * fs) # indicates total samples\n",
|
||
|
|
"t = np.linspace(0, T, n, endpoint=False)\n",
|
||
|
|
"\n",
|
||
|
|
"# data = np.sin(1.2*2*np.pi*t) + 1.5*np.cos(9*2*np.pi*t) + 0.5*np.sin(50.0*2*np.pi*t)\n",
|
||
|
|
"data = tuning_score['pair']['var_z'][0]\n",
|
||
|
|
"\n",
|
||
|
|
"# Filtering and plotting\n",
|
||
|
|
"y = butter_lowpass_filter(data, cutoff, fs, order, no_lag=True)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.subplot(2, 1, 2)\n",
|
||
|
|
"plt.plot(t, data, 'b-', label='data')\n",
|
||
|
|
"plt.plot(t, y, 'g-', linewidth=2, label='filtered data')\n",
|
||
|
|
"plt.xlabel('Time [sec]')\n",
|
||
|
|
"plt.grid()\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"\n",
|
||
|
|
"plt.subplots_adjust(hspace=0.35)\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 50,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"for key in key_list:\n",
|
||
|
|
" for score_key in score_key_list[2:]:\n",
|
||
|
|
" tuning_score[key][score_key+'_filt'] = np.ndarray((num_unit, len(tau_id_range)))\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" tuning_score[key][score_key+'_filt'][unit_id] = butter_lowpass_filter(tuning_score[key][score_key][unit_id], cutoff, fs, order, no_lag=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 54,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"dict_keys(['kl', 'var', 'kl_z', 'var_z', 'kl_z_filt', 'var_z_filt'])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 54,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 4,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from scipy.optimize import curve_fit\n",
|
||
|
|
"\n",
|
||
|
|
"def gauss(x,mu,sigma,A):\n",
|
||
|
|
" return A*np.exp(-(x-mu)**2/2/sigma**2)\n",
|
||
|
|
"\n",
|
||
|
|
"def bimodal_gaussian(x, mu1, mu2, A1, A2, sigma1,sigma2, c):\n",
|
||
|
|
" return c + gauss(x,mu1,sigma1,A1)+gauss(x,mu2,sigma2,A2)\n",
|
||
|
|
"\n",
|
||
|
|
"def von_mises(x, mu, A, sigma):\n",
|
||
|
|
" return A * np.exp(np.cos(x-mu)/sigma**2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "NameError",
|
||
|
|
"evalue": "name 'var_tuning' is not defined",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
|
||
|
|
"\u001b[1;32mc:\\Users\\arash\\Documents\\Uni\\Research Project (Topology)\\staticgrating-phasefreq-topology\\02_Tuning_Curves_Receptive_Fields.ipynb Cell 31'\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000030?line=1'>2</a>\u001b[0m expected \u001b[39m=\u001b[39m (\u001b[39m30\u001b[39m,\u001b[39m140\u001b[39m,\u001b[39m1\u001b[39m,\u001b[39m1\u001b[39m,\u001b[39m30\u001b[39m,\u001b[39m30\u001b[39m,\u001b[39m0.2\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000030?line=2'>3</a>\u001b[0m x \u001b[39m=\u001b[39m tau_id_range\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000030?line=3'>4</a>\u001b[0m y \u001b[39m=\u001b[39m var_tuning[\u001b[39m'\u001b[39m\u001b[39mphase\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m0\u001b[39m]\u001b[39m/\u001b[39mnp\u001b[39m.\u001b[39mmean(var_tuning[\u001b[39m'\u001b[39m\u001b[39mphase\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m0\u001b[39m])\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000030?line=4'>5</a>\u001b[0m params,cov\u001b[39m=\u001b[39mcurve_fit(bimodal_gaussian,x, y, expected)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/arash/Documents/Uni/Research%20Project%20%28Topology%29/staticgrating-phasefreq-topology/02_Tuning_Curves_Receptive_Fields.ipynb#ch0000030?line=5'>6</a>\u001b[0m sigma\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39msqrt(np\u001b[39m.\u001b[39mdiag(cov))\n",
|
||
|
|
"\u001b[1;31mNameError\u001b[0m: name 'var_tuning' is not defined"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"# expected=(0.2, 30,30,1.3,140,30,3) #c, mu1, sigma1, A1, mu2, sigma2, A2\n",
|
||
|
|
"expected = (30,140,1,1,30,30,0.2)\n",
|
||
|
|
"x = tau_id_range\n",
|
||
|
|
"y = var_tuning['phase'][0]/np.mean(var_tuning['phase'][0])\n",
|
||
|
|
"params,cov=curve_fit(bimodal_gaussian,x, y, expected)\n",
|
||
|
|
"sigma=np.sqrt(np.diag(cov))\n",
|
||
|
|
"plt.plot(x, bimodal_gaussian(x,*params), color='red', lw=3, label='fitted model')\n",
|
||
|
|
"plt.plot(x, bimodal_gaussian(x,*expected), color='g', lw=3, label='initial model')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.plot(x, y)\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"print(params, '\\n', sigma)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def tuning_index(tuning_curve):\n",
|
||
|
|
" return np.std(tuning_curve)/np.mean(tuning_curve)\n",
|
||
|
|
"\n",
|
||
|
|
"def selectivity_index(tuning_curve):\n",
|
||
|
|
" pref = np.argmax(tuning_curve)\n",
|
||
|
|
" return \n",
|
||
|
|
"\n",
|
||
|
|
"def unimodal_periodic_fit(tuning_curve):\n",
|
||
|
|
" \n",
|
||
|
|
"\n",
|
||
|
|
" return "
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3.10.5 64-bit",
|
||
|
|
"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": "3.10.5"
|
||
|
|
},
|
||
|
|
"orig_nbformat": 4,
|
||
|
|
"vscode": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|