Adapt to binder
imperator
1 year ago
parent
73a79a1627
commit
4649822609
Binary file not shown.
Binary file not shown.
@ -0,0 +1,178 @@
|
||||
<!DOCTYPE html>
|
||||
<html class="ng-csp" data-placeholder-focus="false" lang="en" translate="no">
|
||||
<head data-requesttoken="eTtjfxF4BCUfJQs2DB8cY3cDHD1iRHc4FAYxZBYLXVg=:LQ/LI1bbxjaxxRJ5F7Qm122vcKXPEHjj5l3o3JuQozc=">
|
||||
<meta charset="utf-8">
|
||||
<meta name="google" content="notranslate" />
|
||||
<title>
|
||||
polybox </title>
|
||||
<meta http-equiv="X-UA-Compatible" content="IE=edge">
|
||||
<meta name="referrer" content="never">
|
||||
<meta name="viewport" content="width=device-width, minimum-scale=1.0, maximum-scale=1.0">
|
||||
<meta name="theme-color" content="#745bca">
|
||||
<link rel="icon" href="/apps/theme-polybox/core/img/favicon.ico">
|
||||
<link rel="apple-touch-icon-precomposed" href="/apps/theme-polybox/core/img/favicon-touch.png">
|
||||
<link rel="mask-icon" sizes="any" href="/core/img/favicon-mask.svg" color="#041e42">
|
||||
<link rel="stylesheet" href="/core/vendor/select2/select2.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/css/styles.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/css/inputs.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/css/header.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/css/icons.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/css/fonts.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/css/apps.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/css/global.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/css/fixes.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/css/multiselect.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/css/mobile.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/vendor/jquery-ui/themes/base/jquery-ui.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/css/jquery-ui-fixes.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/css/tooltip.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/css/share.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/apps/files_versions/css/versions.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/apps/files_pdfviewer/css/style.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/apps/files_texteditor/css/DroidSansMono/stylesheet.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/apps/files_texteditor/css/style.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/apps/files_texteditor/css/mobile.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/apps/richdocuments/css/viewer/odfviewer.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/core/css/jquery.ocdialog.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/apps/files_sharing/css/public.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/apps/files_sharing/css/mobile.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/apps/files/css/files.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/apps/files/css/upload.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/apps/theme-polybox/core/css/styles.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<link rel="stylesheet" href="/apps/theme-polybox/core/css/header.css?v=68d311791adf3eebb9417bc9d6057e29">
|
||||
<script src="/index.php/core/js/oc.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/jquery/dist/jquery.min.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/jquery-migrate/jquery-migrate.min.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/jquery-ui/ui/jquery-ui.custom.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/underscore/underscore.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/moment/min/moment-with-locales.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/handlebars/handlebars.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/blueimp-md5/js/md5.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/bootstrap/js/tooltip.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/backbone/backbone.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/es6-promise/es6-promise.auto.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/davclient.js/lib/client.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/clipboard/dist/clipboard.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/bowser/src/bowser.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/jquery.ocdialog.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/oc-dialogs.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/js.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/l10n.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/octemplate.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/eventsource.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/config.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/search/js/search.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/oc-requesttoken.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/apps.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/mimetype.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/mimetypelist.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/snapjs/dist/latest/snap.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/vendor/select2/select2.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/oc-backbone.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/oc-backbone-webdav.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/guests/js/app.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/shareconfigmodel.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/sharemodel.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/sharescollection.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/shareitemmodel.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/sharedialogresharerinfoview.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/sharedialoglinklistview.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/sharedialoglinkshareview.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/sharedialogmailview.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/sharedialoglinksocialview.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/sharedialoglinkexpirationview.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/sharedialogshareelistview.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/sharedialogview.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/share.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/user.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files_pdfviewer/js/previewplugin.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/federatedfilesharing/js/public.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files_texteditor/js/editor.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files_texteditor/js/vendor/ace/src-noconflict/ace.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/richdocuments/js/viewer/viewer.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/files/fileinfo.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/core/js/files/client.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files/js/file-upload.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files_sharing/js/public.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files/js/fileactions.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files/js/fileactionsmenu.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files/js/jquery.fileupload.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files/js/filesummary.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files/js/breadcrumb.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files/js/fileinfomodel.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files/js/newfilemenu.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files/js/files.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files/js/filelist.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files/js/keyboardshortcuts.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<script src="/apps/files/js/fileactionsapplicationselectmenu.js?v=68d311791adf3eebb9417bc9d6057e29"></script>
|
||||
<meta property="og:title" content="polybox - <a href="https://polyboxdoc.ethz.ch/" target="_blank">polybox Help</a><br>Use it as a logical memory stick<br>Store your data on the ETH campus"/><meta property="og:description" content="neural_data_short.mat is publicly shared"/><meta property="og:site_name" content="polybox"/><meta property="og:url" content="https://polybox.ethz.ch/index.php/s/UM1oJMRZYAlucIR"/><meta property="og:type" content="object"/><meta property="og:image" content="https://polybox.ethz.ch/core/img/favicon-fb.png"/> </head>
|
||||
<body id="body-public" class="theme-polybox has-theme" >
|
||||
<noscript>
|
||||
<div id="nojavascript">
|
||||
<div>
|
||||
This application requires JavaScript for correct operation. Please <a href="http://enable-javascript.com/" target="_blank" rel="noreferrer">enable JavaScript</a> and reload the page. </div>
|
||||
</div>
|
||||
</noscript>
|
||||
|
||||
|
||||
<div id="notification-container">
|
||||
<div id="notification" style="display: none;"></div>
|
||||
</div>
|
||||
|
||||
<input type="hidden" id="filesApp" name="filesApp" value="1">
|
||||
<input type="hidden" id="isPublic" name="isPublic" value="1">
|
||||
<input type="hidden" name="dir" value="" id="dir">
|
||||
<input type="hidden" name="downloadURL" value="https://polybox.ethz.ch/index.php/s/UM1oJMRZYAlucIR/download" id="downloadURL">
|
||||
<input type="hidden" name="sharingToken" value="UM1oJMRZYAlucIR" id="sharingToken">
|
||||
<input type="hidden" name="filename" value="neural_data_short.mat" id="filename">
|
||||
<input type="hidden" name="mimetype" value="application/octet-stream" id="mimetype">
|
||||
<input type="hidden" name="previewSupported" value="false" id="previewSupported">
|
||||
<input type="hidden" name="previewEnabled" value="true" id="previewEnabled">
|
||||
<input type="hidden" name="mimetypeIcon" value="/core/img/filetypes/file.svg" id="mimetypeIcon">
|
||||
<input type="hidden" name="filesize" value="3938006" id="filesize">
|
||||
<input type="hidden" name="maxSizeAnimateGif" value="10" id="maxSizeAnimateGif">
|
||||
<input type="hidden" name="sharePermission" value="1" id="sharePermission">
|
||||
|
||||
<header>
|
||||
<div id="header" class="share-file" data-protected="false"
|
||||
data-owner-display-name="Sandro Ackermann (sandroa@student.ethz.ch)" data-owner="sandroa" data-name="neural_data_short.mat">
|
||||
<a href="/index.php" title="" id="owncloud-public">
|
||||
<h1 class="logo-icon">
|
||||
polybox.ethz.ch </h1>
|
||||
</a>
|
||||
|
||||
<div id="logo-claim" style="display:none;"></div>
|
||||
<div class="header-right">
|
||||
<span id="details">
|
||||
<a href="https://polybox.ethz.ch/index.php/s/UM1oJMRZYAlucIR/download" id="download" class="button">
|
||||
<img class="svg" alt="" src="/core/img/actions/download.svg"/>
|
||||
<span id="download-text">Download</span>
|
||||
</a>
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
</header>
|
||||
<div id="content-wrapper">
|
||||
<div id="content">
|
||||
<div id="preview">
|
||||
<!-- Preview frame is filled via JS to support SVG images for modern browsers -->
|
||||
<div id="imgframe"></div>
|
||||
<div class="directDownload">
|
||||
<a href="https://polybox.ethz.ch/index.php/s/UM1oJMRZYAlucIR/download" id="downloadFile" class="button">
|
||||
<img class="svg" alt="" src="/core/img/actions/download.svg"/>
|
||||
Download neural_data_short.mat (3.8 MB)
|
||||
</a>
|
||||
</div>
|
||||
<div class="directLink">
|
||||
<label for="directLink">Direct link</label>
|
||||
<input id="directLink" type="text" readonly value="https://polybox.ethz.ch/index.php/s/UM1oJMRZYAlucIR/download">
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<footer>
|
||||
<p class="info">
|
||||
<a href="https://www.ethz.ch" target="_blank" rel="noreferrer">ETH Zürich</a> – <a href="https://polyboxdoc.ethz.ch/" target="_blank">polybox Help</a><br>Use it as a logical memory stick<br>Store your data on the ETH campus </p>
|
||||
</footer>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
@ -1,5 +1,5 @@
|
||||
scipy
|
||||
numpy
|
||||
matplotlib
|
||||
hdf5storage
|
||||
ipympl
|
||||
mat73
|
||||
|
||||
@ -0,0 +1,781 @@
|
||||
from itertools import product
|
||||
import numpy as np
|
||||
from scipy.integrate import trapezoid
|
||||
import hdf5storage
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.lines import Line2D
|
||||
from IPython.display import display
|
||||
from ipywidgets import interact, interact_manual, IntSlider, FloatSlider, IntRangeSlider, ToggleButton, ToggleButtons, Layout
|
||||
from scipy.io import loadmat as sp_loadmat
|
||||
from mat73 import loadmat as mat73_loadmat
|
||||
|
||||
|
||||
def in_colab():
|
||||
"""Check if the code is running in Google Colab."""
|
||||
try:
|
||||
import google.colab
|
||||
return True
|
||||
except ImportError:
|
||||
return False
|
||||
|
||||
|
||||
is_colab = in_colab()
|
||||
continuous_update = not is_colab
|
||||
if is_colab:
|
||||
from google.colab import output
|
||||
output.enable_custom_widget_manager()
|
||||
|
||||
|
||||
def setup_matplotlib_magic():
|
||||
get_ipython().run_line_magic('matplotlib', 'inline' if is_colab else 'widget')
|
||||
|
||||
|
||||
def draw_figure(fig):
|
||||
if not is_colab:
|
||||
fig.canvas.draw_idle()
|
||||
else:
|
||||
plt.show()
|
||||
|
||||
|
||||
def maybe_setup(setup_fun, state):
|
||||
if not is_colab:
|
||||
return
|
||||
elif 'needs_setup' not in state:
|
||||
state['needs_setup'] = True
|
||||
else:
|
||||
state.update(setup_fun())
|
||||
|
||||
|
||||
def loadmat(mat_file):
|
||||
try:
|
||||
return sp_loadmat(mat_file)
|
||||
except Exception:
|
||||
return mat73_loadmat(mat_file)
|
||||
|
||||
|
||||
def generate_sims(C, k, alpha, sigma_a, sigma_s, lambda_, n_sim=100, tau=100, dt_total=11 / 85):
|
||||
dt = dt_total / tau
|
||||
|
||||
# discretize C
|
||||
if isinstance(k, np.ndarray):
|
||||
C_scaled = np.repeat(C * k[:, np.newaxis], tau, axis=1)
|
||||
n_sim = len(k)
|
||||
else:
|
||||
C_scaled = np.repeat(C * k, tau)[np.newaxis, :]
|
||||
|
||||
T = C_scaled.shape[-1]
|
||||
|
||||
# noise terms
|
||||
xiR = np.random.randn(n_sim) * alpha / k
|
||||
xiL = np.random.randn(n_sim) * alpha / k
|
||||
directional_noise = (
|
||||
xiR[:, np.newaxis] * (C_scaled > 0) +
|
||||
xiL[:, np.newaxis] * (C_scaled < 0)
|
||||
)
|
||||
dW = np.sqrt(dt) * np.random.randn(n_sim, T)
|
||||
eta = 1 + np.random.randn(n_sim, T) * (sigma_s * np.sqrt(tau))
|
||||
|
||||
# accumulated evidence
|
||||
a = np.zeros((n_sim, T + 1))
|
||||
mE = np.zeros((n_sim, T + 1))
|
||||
for t in range(T):
|
||||
a[:, t + 1] = a[:, t] + (
|
||||
directional_noise[:, t] * C_scaled[:, t] * (dt_total / tau) +
|
||||
lambda_ * a[:, t] * (dt_total / tau) +
|
||||
sigma_a * dW[:, t] +
|
||||
eta[:, t] * C_scaled[:, t] * (dt_total / tau)
|
||||
)
|
||||
|
||||
# momentary evidence
|
||||
mE[:, t+1] = eta[:, t] * C_scaled[:, t] * (dt_total / tau) + lambda_ * a[:, t] * (dt_total / tau)
|
||||
|
||||
return a[:, 1:], mE, tau, dt
|
||||
|
||||
|
||||
def generate_sims_conditions(ks, directions, sim_parameters, num_sims_per_condition):
|
||||
simulation_combinations = list(product(ks, directions))
|
||||
|
||||
a_all = []
|
||||
mE_all = []
|
||||
k_idx_all = []
|
||||
direction_all = []
|
||||
for idx, (k, direction) in enumerate(simulation_combinations):
|
||||
C = sim_parameters['C'] * direction
|
||||
dir_label = 1 if direction == 1 else 0
|
||||
|
||||
a_temp, mE_temp, tau, dt = generate_sims(**{
|
||||
**sim_parameters,
|
||||
'C': C,
|
||||
'k': k,
|
||||
'n_sim': num_sims_per_condition
|
||||
})
|
||||
|
||||
# subsample at every tau steps
|
||||
a_sampled = a_temp[:, tau-1::tau]
|
||||
mE_sampled = mE_temp[:, tau-1::tau] / dt
|
||||
|
||||
a_all.append(a_sampled)
|
||||
mE_all.append(mE_sampled)
|
||||
k_idx_all.extend([k] * num_sims_per_condition)
|
||||
direction_all.extend([dir_label] * num_sims_per_condition)
|
||||
|
||||
a_all = np.vstack(a_all)
|
||||
mE_all = np.vstack(mE_all)
|
||||
k_idx_all = np.array(k_idx_all)
|
||||
direction_all = np.array(direction_all)
|
||||
|
||||
choices = (a_all > 0).astype(int) # 1 is right, 0 is left
|
||||
is_correct = (choices == direction_all[:, np.newaxis]).astype(int)
|
||||
|
||||
time = np.arange(len(C))
|
||||
|
||||
return time, a_all, mE_all, k_idx_all, choices, is_correct
|
||||
|
||||
|
||||
def plot_sims(C_size=11, num_sims=30 if not is_colab else 5):
|
||||
setup_matplotlib_magic()
|
||||
|
||||
def setup():
|
||||
fig, axes = plt.subplots(figsize=(6.5, 5))
|
||||
|
||||
evidence_line = axes.plot([], [], color='C2', alpha=1)[0]
|
||||
sim_lines = []
|
||||
for i in range(num_sims):
|
||||
sim_line = axes.plot([], [], color='C0', alpha=0.3)[0]
|
||||
sim_lines += [sim_line]
|
||||
|
||||
axes.set(
|
||||
title=f"{num_sims} Simulations",
|
||||
ylabel="value",
|
||||
xlabel="time $t$",
|
||||
xlim=(0, 11),
|
||||
ylim=(-1.5, 1.5)
|
||||
)
|
||||
|
||||
plt.axhline(0., color='black', alpha=0.3)
|
||||
plt.tight_layout()
|
||||
|
||||
legend_elements = [
|
||||
Line2D([], [], color='C2', label='evidence pulse'),
|
||||
Line2D([], [], color='C0', label='accumulator $a$ (decision: right)'),
|
||||
Line2D([], [], color='C1', label='accumulator $a$ (decision: left)')
|
||||
]
|
||||
axes.legend(handles=legend_elements, loc='upper right')
|
||||
|
||||
return {'fig': fig, 'axes': axes, 'evidence_line': evidence_line, 'sim_lines': sim_lines}
|
||||
|
||||
state = setup()
|
||||
state['random_seed'] = 42
|
||||
|
||||
def update_plot(C_dir, C, k, alpha, sigma_a, sigma_s, lambda_, fixed_noise):
|
||||
maybe_setup(setup, state)
|
||||
|
||||
if fixed_noise == 'redraw noise':
|
||||
state['random_seed'] = np.random.randint(0, 2**32)
|
||||
np.random.seed(state['random_seed'])
|
||||
|
||||
C = np.concatenate([np.zeros(C[0]), np.ones(C[1] - C[0]), np.zeros(C_size - C[1])])
|
||||
C *= 1 if C_dir == 'pulse right' else -1
|
||||
|
||||
sims, *_ = generate_sims(C, k, alpha, sigma_a, sigma_s, lambda_, n_sim=num_sims)
|
||||
|
||||
for sim, sim_line in zip(sims, state['sim_lines']):
|
||||
sim_line.set_data(np.linspace(0, len(C), len(sim)), sim)
|
||||
sim_line.set_color('C0' if sim[-1] > 0 else 'C1')
|
||||
|
||||
state['evidence_line'].set_data(np.linspace(0., len(C), len(C) * 1_000), np.repeat(C, 1_000) * k)
|
||||
|
||||
draw_figure(state['fig'])
|
||||
|
||||
style = {'description_width': '150px'}
|
||||
layout = Layout(width='600px')
|
||||
sliders = {
|
||||
'C_dir': ToggleButtons(options=['pulse left', 'pulse right'], value='pulse right', description=' '),
|
||||
'C': IntRangeSlider(min=0, max=C_size, value=[3, 7], description='evidence pulse timing', style=style, layout=layout, continuous_update=continuous_update),
|
||||
'k': FloatSlider(min=1e-6, max=1., step=0.01, value=0.5, description='coherence', style=style, layout=layout, continuous_update=continuous_update),
|
||||
'sigma_s': FloatSlider(min=0, max=3, step=0.01, value=0., description='fast noise (input)', style=style, layout=layout, continuous_update=continuous_update),
|
||||
'alpha': FloatSlider(min=0, max=1, step=0.01, value=0., description='slow noise (brain)', style=style, layout=layout, continuous_update=continuous_update),
|
||||
'sigma_a': FloatSlider(min=0, max=1, step=0.01, value=0., description='fast inner noise (brain)', style=style, layout=layout, continuous_update=continuous_update),
|
||||
'lambda_': FloatSlider(min=-5, max=5, step=0.01, value=0., description='leakiness', style=style, layout=layout, continuous_update=continuous_update),
|
||||
'fixed_noise': ToggleButtons(options=['fix noise', 'redraw noise'], value='fix noise', description=' '),
|
||||
}
|
||||
|
||||
interact(update_plot, **sliders)
|
||||
|
||||
|
||||
def update_errorbar(err_container, x, y, yerr):
|
||||
err_container.lines[0].set_data(x, y)
|
||||
linecol = err_container.lines[2][0]
|
||||
|
||||
segments = []
|
||||
for xi, yi, yerri in zip(x, y, yerr):
|
||||
segments.append([[xi, yi - yerri], [xi, yi + yerri]])
|
||||
|
||||
linecol.set_segments(segments)
|
||||
|
||||
|
||||
def plot_model_free_analysis_conditions(C, ks, num_sims_per_condition=2_000):
|
||||
setup_matplotlib_magic()
|
||||
|
||||
def setup():
|
||||
fig, axes = plt.subplots(1, 2, figsize=(10, 5), sharex=True)
|
||||
|
||||
accuracy_lines = [axes[0].errorbar([], [], yerr=[], label=f'$k = {k}$') for k in ks]
|
||||
kernel_lines = [axes[1].plot([], [], label=f'$k = {k}$')[0] for k in ks]
|
||||
|
||||
axes[0].set(
|
||||
title="accuracy",
|
||||
xlabel="$t$",
|
||||
xlim=(0, len(C) - 1),
|
||||
ylim=(0, 1)
|
||||
)
|
||||
axes[0].legend(loc='lower right', fontsize='small')
|
||||
|
||||
axes[1].set(
|
||||
title="psychophysical kernel",
|
||||
xlabel="$t$",
|
||||
ylim=(-3, 3)
|
||||
)
|
||||
axes[1].legend(loc='lower left', fontsize='small')
|
||||
|
||||
fig.tight_layout()
|
||||
|
||||
return {'fig': fig, 'axes': axes, 'accuracy_lines': accuracy_lines, 'kernel_lines': kernel_lines}
|
||||
|
||||
state = setup() if not is_colab else {'needs_setup': True}
|
||||
|
||||
def update_plot(sigma_s, alpha, sigma_a, lambda_):
|
||||
maybe_setup(setup, state)
|
||||
|
||||
sim_parameters = {
|
||||
'C': C,
|
||||
'sigma_s': sigma_s,
|
||||
'alpha': alpha,
|
||||
'sigma_a': sigma_a,
|
||||
'lambda_': lambda_
|
||||
}
|
||||
|
||||
directions = [1, -1]
|
||||
time, a_all, mE_all, k_idx_all, choices, is_correct = generate_sims_conditions(
|
||||
ks, directions, sim_parameters, num_sims_per_condition
|
||||
)
|
||||
|
||||
for i, k in enumerate(ks):
|
||||
mask = (k_idx_all == k)
|
||||
is_corr_k = is_correct[mask, :]
|
||||
perf = is_corr_k.mean(axis=0)
|
||||
ci95 = 1.96 * is_corr_k.std(axis=0) / np.sqrt(mask.sum())
|
||||
|
||||
update_errorbar(state['accuracy_lines'][i], time, perf, yerr=ci95)
|
||||
|
||||
psy_kernel = (
|
||||
mE_all[ (choices[:, -1] == 1) & mask ].mean(axis=0) -
|
||||
mE_all[ (choices[:, -1] != 1) & mask ].mean(axis=0)
|
||||
)
|
||||
|
||||
state['kernel_lines'][i].set_data(time, psy_kernel)
|
||||
|
||||
state['fig'].tight_layout()
|
||||
draw_figure(state['fig'])
|
||||
|
||||
|
||||
style = {'description_width': '150px'}
|
||||
layout = Layout(width='600px')
|
||||
sliders = {
|
||||
'sigma_s': FloatSlider(min=0, max=5, step=0.01, value=0., description='fast noise (input)', style=style, layout=layout),
|
||||
'alpha': FloatSlider(min=0, max=1, step=0.01, value=0., description='slow noise (brain)', style=style, layout=layout),
|
||||
'sigma_a': FloatSlider(min=0, max=2, step=0.01, value=0., description='fast inner noise (brain)', style=style, layout=layout),
|
||||
'lambda_': FloatSlider(min=-5, max=5, step=0.01, value=0., description='leakiness', style=style, layout=layout)
|
||||
}
|
||||
|
||||
interact_manual.options(manual_name='run simulations')(
|
||||
update_plot,
|
||||
**sliders
|
||||
)
|
||||
|
||||
|
||||
def model_free_analysis(dataset):
|
||||
is_correct = dataset['choices'] == dataset['direction'].flatten()
|
||||
time = np.arange(dataset['a'].shape[1])
|
||||
|
||||
perfs = []
|
||||
ci95s = []
|
||||
psy_kernels = []
|
||||
for k_idx in [1, 2, 3]:
|
||||
mask = (dataset['kIdx'].flatten() == k_idx)
|
||||
is_corr_k = is_correct[:, mask]
|
||||
perf = is_corr_k.mean(axis=1)
|
||||
ci95 = 1.96 * is_corr_k.std(axis=1) / np.sqrt(mask.sum())
|
||||
|
||||
psy_kernel = (
|
||||
dataset['mE'][ (dataset['choices'][-1, :] == 1) & mask ].mean(axis=0) -
|
||||
dataset['mE'][ (dataset['choices'][-1, :] != 1) & mask ].mean(axis=0)
|
||||
)
|
||||
|
||||
perfs += [perf]
|
||||
ci95s += [ci95]
|
||||
psy_kernels += [psy_kernel]
|
||||
|
||||
return time, perfs, ci95s, psy_kernels
|
||||
|
||||
|
||||
def plot_model_free_analysis_conditions_vs_baseline(baseline_data, num_sims_per_condition=2_000):
|
||||
setup_matplotlib_magic()
|
||||
|
||||
C = np.concatenate(([0], np.ones(10)))
|
||||
ks = [0.2, 0.4, 0.8]
|
||||
|
||||
def setup():
|
||||
fig, axes = plt.subplots(1, 2, figsize=(10, 5), sharex=True)
|
||||
|
||||
accuracy_lines = [axes[0].errorbar([], [], yerr=[], label=f'$k = {k}$') for k in ks]
|
||||
kernel_lines = [axes[1].plot([], [], label=f'$k = {k}$')[0] for k in ks]
|
||||
|
||||
axes[0].set(
|
||||
title="accuracy",
|
||||
xlabel="$t$",
|
||||
xlim=(0, len(C) - 1),
|
||||
ylim=(0, 1)
|
||||
)
|
||||
|
||||
axes[1].set(
|
||||
title="psychophysical kernel",
|
||||
xlabel="$t$",
|
||||
ylim=(-3, 3)
|
||||
)
|
||||
|
||||
time, perfs, ci95s, psy_kernels = model_free_analysis(baseline_data)
|
||||
for i, (perf, ci95, psy_kernel) in enumerate(zip(perfs, ci95s, psy_kernels, strict=True)):
|
||||
axes[0].errorbar(time, perf, yerr=ci95, color=f'C{i}', label=f'$k = {ks[i]}$ (baseline)', linestyle='--', alpha=0.3)
|
||||
axes[1].plot(time, psy_kernel, color=f'C{i}', label=f'$k = {ks[i]}$ (baseline)', linestyle='--', alpha=0.3)
|
||||
|
||||
axes[0].legend(loc='lower right', fontsize='small')
|
||||
axes[1].legend(loc='lower left', fontsize='small')
|
||||
|
||||
fig.tight_layout()
|
||||
|
||||
return {'fig': fig, 'axes': axes, 'accuracy_lines': accuracy_lines, 'kernel_lines': kernel_lines}
|
||||
|
||||
state = setup() if not is_colab else {'needs_setup': True}
|
||||
|
||||
def update_plot(sigma_s, alpha, sigma_a, lambda_):
|
||||
maybe_setup(setup, state)
|
||||
|
||||
sim_parameters = {
|
||||
'C': C,
|
||||
'sigma_s': sigma_s,
|
||||
'alpha': alpha,
|
||||
'sigma_a': sigma_a,
|
||||
'lambda_': lambda_
|
||||
}
|
||||
|
||||
directions = [1, -1]
|
||||
time, a_all, mE_all, k_idx_all, choices, is_correct = generate_sims_conditions(
|
||||
ks, directions, sim_parameters, num_sims_per_condition
|
||||
)
|
||||
|
||||
for i, k in enumerate(ks):
|
||||
mask = (k_idx_all == k)
|
||||
is_corr_k = is_correct[mask, :]
|
||||
perf = is_corr_k.mean(axis=0)
|
||||
ci95 = 1.96 * is_corr_k.std(axis=0) / np.sqrt(mask.sum())
|
||||
|
||||
update_errorbar(state['accuracy_lines'][i], time, perf, yerr=ci95)
|
||||
|
||||
psy_kernel = (
|
||||
mE_all[ (choices[:, -1] == 1) & mask ].mean(axis=0) -
|
||||
mE_all[ (choices[:, -1] != 1) & mask ].mean(axis=0)
|
||||
)
|
||||
|
||||
state['kernel_lines'][i].set_data(time, psy_kernel)
|
||||
|
||||
state['fig'].tight_layout()
|
||||
draw_figure(state['fig'])
|
||||
|
||||
|
||||
style = {'description_width': '150px'}
|
||||
layout = Layout(width='600px')
|
||||
sliders = {
|
||||
'sigma_s': FloatSlider(min=0, max=5, step=0.01, value=0., description='fast noise (input)', style=style, layout=layout),
|
||||
'alpha': FloatSlider(min=0, max=1, step=0.01, value=0., description='slow noise (brain)', style=style, layout=layout),
|
||||
'sigma_a': FloatSlider(min=0, max=2, step=0.01, value=0., description='fast inner noise (brain)', style=style, layout=layout),
|
||||
'lambda_': FloatSlider(min=-5, max=5, step=0.01, value=0., description='leakiness', style=style, layout=layout)
|
||||
}
|
||||
|
||||
interact_manual.options(manual_name='run simulations')(
|
||||
update_plot,
|
||||
**sliders
|
||||
)
|
||||
|
||||
|
||||
def bin_spikes(raw_spike_matrix, bin_size=50):
|
||||
num_bins = raw_spike_matrix.shape[1] // bin_size
|
||||
|
||||
truncated_raw_spike_matrix = raw_spike_matrix[:, :num_bins * bin_size, :]
|
||||
binned_spike_matrix = truncated_raw_spike_matrix.reshape([
|
||||
truncated_raw_spike_matrix.shape[0],
|
||||
num_bins,
|
||||
-1,
|
||||
truncated_raw_spike_matrix.shape[2]
|
||||
]).sum(axis=2)
|
||||
|
||||
return binned_spike_matrix
|
||||
|
||||
|
||||
def get_binned_spike_matrix(mat_data):
|
||||
raw_spike_matrix = mat_data['RawSpikeMatrix1'][:, 149:1000, :]
|
||||
binned_spike_matrix = bin_spikes(raw_spike_matrix)
|
||||
binned_spike_matrix = np.sqrt(binned_spike_matrix)
|
||||
time = np.arange(binned_spike_matrix.shape[1]) * 50
|
||||
return time, binned_spike_matrix
|
||||
|
||||
|
||||
def plot_single_neuron(mat_data):
|
||||
setup_matplotlib_magic()
|
||||
|
||||
time, binned_spike_matrix = get_binned_spike_matrix(mat_data)
|
||||
|
||||
def setup():
|
||||
fig, axes = plt.subplots(figsize=(6.5, 4.5))
|
||||
|
||||
neuron_line = axes.plot([], [])[0]
|
||||
|
||||
axes.set(
|
||||
ylabel=r'$\sqrt{N_\mathrm{spikes}}$',
|
||||
xlabel='time [ms]',
|
||||
xlim=(0, 800)
|
||||
)
|
||||
|
||||
return {'fig': fig, 'axes': axes, 'neuron_line': neuron_line}
|
||||
|
||||
state = setup()
|
||||
|
||||
def update_plot(neuron_idx):
|
||||
maybe_setup(setup, state)
|
||||
|
||||
state['neuron_line'].set_data(time, binned_spike_matrix.mean(axis=0)[:, neuron_idx])
|
||||
|
||||
state['axes'].relim()
|
||||
state['axes'].autoscale(axis='y')
|
||||
state['axes'].set_title(f'Neuron #{neuron_idx}', fontsize='small')
|
||||
state['fig'].tight_layout()
|
||||
draw_figure(state['fig'])
|
||||
|
||||
sliders = {
|
||||
'neuron_idx': IntSlider(min=0, max=binned_spike_matrix.shape[2] - 1, description='neuron #', layout=Layout(width='800px'), continuous_update=continuous_update)
|
||||
}
|
||||
|
||||
interact(update_plot, **sliders)
|
||||
|
||||
|
||||
def plot_neuron_by_choice(mat_data):
|
||||
setup_matplotlib_magic()
|
||||
|
||||
time, binned_spike_matrix = get_binned_spike_matrix(mat_data)
|
||||
|
||||
correct_trials_mask = (mat_data['targ_cho'].flatten() == mat_data['targ_cor'].flatten())
|
||||
right_choice = (mat_data['targ_cho'].flatten() == 1)
|
||||
|
||||
def setup():
|
||||
fig, axes = plt.subplots(1, 2, figsize=(8, 4), sharex=True)
|
||||
|
||||
choices = ['right choice', 'left choice']
|
||||
correct_lines = []
|
||||
for choice in choices:
|
||||
correct_line = axes[0].plot([], [], label=choice)[0]
|
||||
correct_lines += [correct_line]
|
||||
|
||||
incorrect_lines = []
|
||||
for choice in choices:
|
||||
incorrect_line = axes[1].plot([], [], label=choice)[0]
|
||||
incorrect_lines += [incorrect_line]
|
||||
|
||||
axes[0].set(
|
||||
title='correct trials',
|
||||
ylabel=r'$\sqrt{N_\mathrm{spikes}}$',
|
||||
xlabel='time [ms]',
|
||||
xlim=(0, 800)
|
||||
)
|
||||
axes[1].set(
|
||||
title='incorrect trials',
|
||||
xlabel='time [ms]'
|
||||
)
|
||||
axes[0].legend(loc='upper right')
|
||||
axes[1].legend(loc='upper right')
|
||||
|
||||
return {'fig': fig, 'axes': axes, 'correct_lines': correct_lines, 'incorrect_lines': incorrect_lines}
|
||||
|
||||
state = setup()
|
||||
|
||||
def update_plot(neuron_idx):
|
||||
maybe_setup(setup, state)
|
||||
|
||||
state['correct_lines'][0].set_data(time, binned_spike_matrix[correct_trials_mask & right_choice].mean(axis=0)[:, neuron_idx])
|
||||
state['correct_lines'][1].set_data(time, binned_spike_matrix[correct_trials_mask & ~right_choice].mean(axis=0)[:, neuron_idx])
|
||||
state['incorrect_lines'][0].set_data(time, binned_spike_matrix[~correct_trials_mask & right_choice].mean(axis=0)[:, neuron_idx])
|
||||
state['incorrect_lines'][1].set_data(time, binned_spike_matrix[~correct_trials_mask & ~right_choice].mean(axis=0)[:, neuron_idx])
|
||||
|
||||
state['axes'][0].relim()
|
||||
state['axes'][1].relim()
|
||||
state['axes'][0].autoscale(axis='y')
|
||||
state['axes'][1].autoscale(axis='y')
|
||||
state['fig'].suptitle(f'Neuron #{neuron_idx}', fontsize='small')
|
||||
state['fig'].tight_layout()
|
||||
draw_figure(state['fig'])
|
||||
|
||||
sliders = {
|
||||
'neuron_idx': IntSlider(min=0, max=binned_spike_matrix.shape[2] - 1, description='neuron #', layout=Layout(width='800px'), continuous_update=continuous_update)
|
||||
}
|
||||
|
||||
interact(update_plot, **sliders)
|
||||
|
||||
|
||||
def plot_neuron_by_coherence(mat_data):
|
||||
setup_matplotlib_magic()
|
||||
|
||||
time, binned_spike_matrix = get_binned_spike_matrix(mat_data)
|
||||
|
||||
correct_trials_mask = (mat_data['targ_cho'].flatten() == mat_data['targ_cor'].flatten())
|
||||
coherences = np.sort(
|
||||
np.unique(mat_data['dot_coh'])
|
||||
)
|
||||
coherences = coherences[[0, 3, 5]]
|
||||
|
||||
def setup():
|
||||
fig, axes = plt.subplots(1, 2, figsize=(8, 4), sharex=True)
|
||||
|
||||
choices = ['right choice', 'left choice']
|
||||
correct_lines = []
|
||||
for coherence in coherences:
|
||||
correct_line = axes[0].plot([], [], label=f'{coherence = :.1%}')[0]
|
||||
correct_lines += [correct_line]
|
||||
|
||||
incorrect_lines = []
|
||||
for coherence in coherences:
|
||||
incorrect_line = axes[1].plot([], [], label=f'{coherence = :.1%}')[0]
|
||||
incorrect_lines += [incorrect_line]
|
||||
|
||||
axes[0].set(
|
||||
title='correct trials',
|
||||
ylabel=r'$\sqrt{N_\mathrm{spikes}}$',
|
||||
xlabel='time [ms]',
|
||||
xlim=(0, 800)
|
||||
)
|
||||
axes[1].set(
|
||||
title='incorrect trials',
|
||||
xlabel='time [ms]'
|
||||
)
|
||||
axes[0].legend(loc='upper right')
|
||||
axes[1].legend(loc='upper right')
|
||||
|
||||
return {'fig': fig, 'axes': axes, 'correct_lines': correct_lines, 'incorrect_lines': incorrect_lines}
|
||||
|
||||
state = setup()
|
||||
|
||||
|
||||
def update_plot(neuron_idx):
|
||||
maybe_setup(setup, state)
|
||||
|
||||
for i, coherence in enumerate(coherences):
|
||||
coherence_mask = (mat_data['dot_coh'].flatten() == coherence)
|
||||
state['correct_lines'][i].set_data(time, binned_spike_matrix[correct_trials_mask & coherence_mask].mean(axis=0)[:, neuron_idx])
|
||||
state['incorrect_lines'][i].set_data(time, binned_spike_matrix[~correct_trials_mask & coherence_mask].mean(axis=0)[:, neuron_idx])
|
||||
|
||||
state['axes'][0].relim()
|
||||
state['axes'][1].relim()
|
||||
state['axes'][0].autoscale(axis='y')
|
||||
state['axes'][1].autoscale(axis='y')
|
||||
state['fig'].suptitle(f'Neuron #{neuron_idx}', fontsize='small')
|
||||
state['fig'].tight_layout()
|
||||
draw_figure(state['fig'])
|
||||
|
||||
sliders = {
|
||||
'neuron_idx': IntSlider(min=0, max=binned_spike_matrix.shape[2] - 1, description='neuron #', layout=Layout(width='800px'), continuous_update=continuous_update)
|
||||
}
|
||||
|
||||
interact(update_plot, **sliders)
|
||||
|
||||
|
||||
def calculate_deltas(mat_data):
|
||||
time, binned_spike_matrix = get_binned_spike_matrix(mat_data)
|
||||
right_choice = (mat_data['targ_cho'].flatten() == 1)
|
||||
|
||||
mean_spikes_right = binned_spike_matrix[right_choice].mean(axis=0)
|
||||
mean_spikes_left = binned_spike_matrix[~right_choice].mean(axis=0)
|
||||
|
||||
deltas = (
|
||||
trapezoid(mean_spikes_right, axis=0) -
|
||||
trapezoid(mean_spikes_left, axis=0)
|
||||
)
|
||||
|
||||
return deltas
|
||||
|
||||
|
||||
def plot_deltas(deltas):
|
||||
setup_matplotlib_magic()
|
||||
|
||||
fig, axes = plt.subplots(1, 2, figsize=(8, 4), sharey=True)
|
||||
|
||||
axes[0].hist(deltas, bins=16, range=(-4, 4))
|
||||
axes[1].hist(np.abs(deltas), bins=15, range=(0, 4.2))
|
||||
|
||||
axes[0].set(
|
||||
ylabel='counts',
|
||||
xlabel=r'$\Delta$'
|
||||
)
|
||||
axes[1].set(
|
||||
xlabel=r'|$\Delta$|'
|
||||
)
|
||||
plt.tight_layout()
|
||||
|
||||
|
||||
def plot_aggregated_neurons(mat_data):
|
||||
setup_matplotlib_magic()
|
||||
|
||||
time, binned_spike_matrix = get_binned_spike_matrix(mat_data)
|
||||
right_choice = (mat_data['targ_cho'].flatten() == 1)
|
||||
mean_spikes_right = binned_spike_matrix[right_choice].mean(axis=0)
|
||||
mean_spikes_left = binned_spike_matrix[~right_choice].mean(axis=0)
|
||||
|
||||
deltas = calculate_deltas(mat_data)
|
||||
|
||||
def setup():
|
||||
fig, axes = plt.subplots()
|
||||
|
||||
lines = [
|
||||
axes.plot([], [], label='right choice')[0],
|
||||
axes.plot([], [], label='left choice')[0]
|
||||
]
|
||||
|
||||
axes.set(
|
||||
ylabel=r'$\sqrt{N_\mathrm{spikes}}$',
|
||||
xlabel='time [ms]',
|
||||
xlim=(0, 800)
|
||||
)
|
||||
axes.legend(loc='upper right')
|
||||
|
||||
return {'fig': fig, 'axes': axes, 'lines': lines}
|
||||
|
||||
state = setup()
|
||||
|
||||
def update_plot(delta_threshold):
|
||||
maybe_setup(setup, state)
|
||||
|
||||
state['lines'][0].set_data(time, (mean_spikes_right * np.sign(deltas))[:, np.abs(deltas) > delta_threshold].mean(axis=1))
|
||||
state['lines'][1].set_data(time, (mean_spikes_left * np.sign(deltas))[:, np.abs(deltas) > delta_threshold].mean(axis=1))
|
||||
|
||||
state['axes'].relim()
|
||||
state['axes'].autoscale(axis='y')
|
||||
state['axes'].set(
|
||||
title=f'|Δ| > {delta_threshold:.2f}'
|
||||
)
|
||||
state['fig'].tight_layout()
|
||||
draw_figure(state['fig'])
|
||||
|
||||
sliders = {
|
||||
'delta_threshold': FloatSlider(min=0, max=np.abs(deltas).max() - 1e-3, description='threshold |Δ|', layout=Layout(width='800px'), continuous_update=continuous_update)
|
||||
}
|
||||
|
||||
interact(update_plot, **sliders)
|
||||
|
||||
|
||||
def simulate_conditions(mat_data, alpha, sigma_a, sigma_s, lambda_):
|
||||
dot_coh = mat_data['dot_coh'].flatten()
|
||||
dot_dir = mat_data['dot_dir'].flatten()
|
||||
targ_cor = mat_data['targ_cor'].flatten()
|
||||
|
||||
C = np.array([0] + [1]*16)
|
||||
|
||||
dot_coh[dot_coh == 0] = 1e-12
|
||||
k = np.unique(dot_coh)
|
||||
|
||||
# map directions: 0 -> 1 (right), 180 -> -1 (left)
|
||||
d = np.copy(dot_dir)
|
||||
d[dot_dir == 0] = 1
|
||||
d[dot_dir == 180] = -1
|
||||
|
||||
a, _, tau, dt = generate_sims(np.outer(d, C), dot_coh, alpha, sigma_a, sigma_s, lambda_)
|
||||
a = a[:, tau-1::tau]
|
||||
|
||||
# determine choices and correctness
|
||||
cho = (a[:, -1] > 0).astype(int)
|
||||
cho[cho == 0] = 2 # 2 is left, 1 is right
|
||||
isCorr = cho == targ_cor
|
||||
|
||||
# separate correct and incorrect trials
|
||||
a_Cor = a[isCorr, :]
|
||||
d_Cor = d[isCorr]
|
||||
cho_Cor = cho[isCorr]
|
||||
coh_Cor = dot_coh[isCorr]
|
||||
|
||||
a_Inc = a[~isCorr, :]
|
||||
d_Inc = d[~isCorr]
|
||||
cho_Inc = cho[~isCorr]
|
||||
coh_Inc = dot_coh[~isCorr]
|
||||
|
||||
# plot average accumulation for correct trials by direction
|
||||
unq_dir = np.unique(d)
|
||||
|
||||
means_a = []
|
||||
for dir_ in unq_dir:
|
||||
mean_a = np.mean(a_Cor[d_Cor == dir_, :], axis=0)
|
||||
means_a += [mean_a]
|
||||
|
||||
return means_a
|
||||
|
||||
|
||||
def plot_sims_conditions(mat_data):
|
||||
setup_matplotlib_magic()
|
||||
|
||||
def setup():
|
||||
fig, axes = plt.subplots(figsize=(6.5, 5))
|
||||
|
||||
evidence_line = axes.plot([], [], color='C2', alpha=1)[0]
|
||||
sim_lines = []
|
||||
for choice in ['right choice', 'left choice']:
|
||||
sim_line = axes.plot([], [], label=choice)[0]
|
||||
sim_lines += [sim_line]
|
||||
|
||||
axes.set(
|
||||
ylabel="mean $a$",
|
||||
xlabel="time $t$",
|
||||
xlim=(0, 800),
|
||||
ylim=(-0.5, .5)
|
||||
)
|
||||
|
||||
axes.legend(loc='upper right')
|
||||
plt.tight_layout()
|
||||
|
||||
return {'fig': fig, 'axes': axes, 'sim_lines': sim_lines}
|
||||
|
||||
state = setup()
|
||||
state['random_seed'] = 42
|
||||
|
||||
def update_plot(alpha, sigma_a, sigma_s, lambda_, fixed_noise):
|
||||
maybe_setup(setup, state)
|
||||
|
||||
if fixed_noise == 'redraw noise':
|
||||
state['random_seed'] = np.random.randint(0, 2**32)
|
||||
np.random.seed(state['random_seed'])
|
||||
|
||||
means_a = simulate_conditions(mat_data, alpha, sigma_a, sigma_s, lambda_)
|
||||
|
||||
for mean_a, line in zip(means_a[::-1], state['sim_lines'], strict=True):
|
||||
line.set_data(np.arange(len(mean_a)) * 50, mean_a)
|
||||
|
||||
state['axes'].relim()
|
||||
state['axes'].autoscale(axis='y')
|
||||
state['fig'].tight_layout()
|
||||
draw_figure(state['fig'])
|
||||
|
||||
style = {'description_width': '150px'}
|
||||
layout = Layout(width='600px')
|
||||
sliders = {
|
||||
'sigma_s': FloatSlider(min=0, max=3, step=0.01, value=0., description='fast noise (input)', style=style, layout=layout, continuous_update=continuous_update),
|
||||
'alpha': FloatSlider(min=0, max=1, step=0.01, value=0., description='slow noise (brain)', style=style, layout=layout, continuous_update=continuous_update),
|
||||
'sigma_a': FloatSlider(min=0, max=1, step=0.01, value=0., description='fast inner noise (brain)', style=style, layout=layout, continuous_update=continuous_update),
|
||||
'lambda_': FloatSlider(min=-5, max=5, step=0.01, value=0., description='leakiness', style=style, layout=layout, continuous_update=continuous_update),
|
||||
'fixed_noise': ToggleButtons(options=['fix noise', 'redraw noise'], value='fix noise', description=' '),
|
||||
}
|
||||
|
||||
interact(update_plot, **sliders)
|
||||
Loading…
Reference in New Issue