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This folder contains Matlab (R2020a) and C codes for the manuscript: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the
relationship between behavior and correlated variability. eLife. (in press)
++++++++++++++++++++++++++++++++
To use, first compile all the C codes with the mex compiler in Matlab. Specifically, in the Matlab command line, run the following commands:
mex EIF1DRFfastslowSyn.c
mex spktime2count.c
Sim_Ori_gabor_L2.m simulates a three-layer network with different attentional modulation. Simulation code for Fig. 2. Saves spike counts. CollectSpk.m collect spike counts from every 500 simulations into one file, which will be used to compute model statistics, Fisher information and decoder performance. ModelStat.m Compute model statistics for different attentional modulation (Fig 2D-F) FIdecoder_cluster_L1.m computes Fisher information for the specific decoder (Fig. 3B). Calls function FIdecoder.m GD_th_cluster.m computes Fisher information for the general decoder (Fig. 3B). CollectData.m Collects Fisher information vs. number of neurons data from different parameter sets.
Local_global_svm.m Compare performance of specific and general decoders with small number of neurons (Fig. 3D)
RF2D3layer.m is the main simulation function. It contains default parameter values and uses the mex file EIF1DRFfastslowSyn.c for integration. genXspk.m generate spike trains of input from Layer 1. gen_weights.m generates weight matrices without tuning-dependent connections ori_map.m generates a columnar orientation map. spktime2count.c converts spike time data to spike counts. raster2D_ani.m generates movie of spike rasters from 2D spatial networks. +++++++++++++++++++++++++++++++++++
One simulation of a three-layer network for 20 sec takes about 2 hours CPU time and under 3 gb memory. (Sim_Ori_gabor_L2.m)

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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This folder contains Matlab (R2020a) and C codes for the manuscript: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the
relationship between behavior and correlated variability. eLife. (in press)
++++++++++++++++++++++++++++++++
To use, first compile all the C codes with the mex compiler in Matlab. Specifically, in the Matlab command line, run the following commands:
mex EIF1DRFfastslowSyn.c
mex spktime2count.c
Sim_Ori_gabor_L2.m simulates a three-layer network with different attentional modulation. Simulation code for Fig. 2. Saves spike counts. CollectSpk.m collect spike counts from every 500 simulations into one file, which will be used to compute model statistics, Fisher information and decoder performance. ModelStat.m Compute model statistics for different attentional modulation (Fig 2D-F) FIdecoder_cluster_L1.m computes Fisher information for the specific decoder (Fig. 3B). Calls function FIdecoder.m GD_th_cluster.m computes Fisher information for the general decoder (Fig. 3B). CollectData.m Collects Fisher information vs. number of neurons data from different parameter sets.
Local_global_svm.m Compare performance of specific and general decoders with small number of neurons (Fig. 3D)
RF2D3layer.m is the main simulation function. It contains default parameter values and uses the mex file EIF1DRFfastslowSyn.c for integration. genXspk.m generate spike trains of input from Layer 1. gen_weights.m generates weight matrices without tuning-dependent connections ori_map.m generates a columnar orientation map. spktime2count.c converts spike time data to spike counts. raster2D_ani.m generates movie of spike rasters from 2D spatial networks. +++++++++++++++++++++++++++++++++++
One simulation of a three-layer network for 20 sec takes about 2 hours CPU time and under 3 gb memory. (Sim_Ori_gabor_L2.m)

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Codes for paper: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the relationship between behavior and correlated variability. eLife. (in press)
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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This folder contains Matlab (R2020a) and C codes for the manuscript: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the
relationship between behavior and correlated variability. eLife. (in press)
++++++++++++++++++++++++++++++++
To use, first compile all the C codes with the mex compiler in Matlab. Specifically, in the Matlab command line, run the following commands:
mex EIF1DRFfastslowSyn.c
mex spktime2count.c
Sim_Ori_gabor_L2.m simulates a three-layer network with different attentional modulation. Simulation code for Fig. 2. Saves spike counts. CollectSpk.m collect spike counts from every 500 simulations into one file, which will be used to compute model statistics, Fisher information and decoder performance. ModelStat.m Compute model statistics for different attentional modulation (Fig 2D-F) FIdecoder_cluster_L1.m computes Fisher information for the specific decoder (Fig. 3B). Calls function FIdecoder.m GD_th_cluster.m computes Fisher information for the general decoder (Fig. 3B). CollectData.m Collects Fisher information vs. number of neurons data from different parameter sets.
Local_global_svm.m Compare performance of specific and general decoders with small number of neurons (Fig. 3D)
RF2D3layer.m is the main simulation function. It contains default parameter values and uses the mex file EIF1DRFfastslowSyn.c for integration. genXspk.m generate spike trains of input from Layer 1. gen_weights.m generates weight matrices without tuning-dependent connections ori_map.m generates a columnar orientation map. spktime2count.c converts spike time data to spike counts. raster2D_ani.m generates movie of spike rasters from 2D spatial networks. +++++++++++++++++++++++++++++++++++
One simulation of a three-layer network for 20 sec takes about 2 hours CPU time and under 3 gb memory. (Sim_Ori_gabor_L2.m)

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Codes for paper: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the relationship between behavior and correlated variability. eLife. (in press)
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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This folder contains Matlab (R2020a) and C codes for the manuscript: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the
relationship between behavior and correlated variability. eLife. (in press)
++++++++++++++++++++++++++++++++
To use, first compile all the C codes with the mex compiler in Matlab. Specifically, in the Matlab command line, run the following commands:
mex EIF1DRFfastslowSyn.c
mex spktime2count.c
Sim_Ori_gabor_L2.m simulates a three-layer network with different attentional modulation. Simulation code for Fig. 2. Saves spike counts. CollectSpk.m collect spike counts from every 500 simulations into one file, which will be used to compute model statistics, Fisher information and decoder performance. ModelStat.m Compute model statistics for different attentional modulation (Fig 2D-F) FIdecoder_cluster_L1.m computes Fisher information for the specific decoder (Fig. 3B). Calls function FIdecoder.m GD_th_cluster.m computes Fisher information for the general decoder (Fig. 3B). CollectData.m Collects Fisher information vs. number of neurons data from different parameter sets.
Local_global_svm.m Compare performance of specific and general decoders with small number of neurons (Fig. 3D)
RF2D3layer.m is the main simulation function. It contains default parameter values and uses the mex file EIF1DRFfastslowSyn.c for integration. genXspk.m generate spike trains of input from Layer 1. gen_weights.m generates weight matrices without tuning-dependent connections ori_map.m generates a columnar orientation map. spktime2count.c converts spike time data to spike counts. raster2D_ani.m generates movie of spike rasters from 2D spatial networks. +++++++++++++++++++++++++++++++++++
One simulation of a three-layer network for 20 sec takes about 2 hours CPU time and under 3 gb memory. (Sim_Ori_gabor_L2.m)

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Codes for paper: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the relationship between behavior and correlated variability. eLife. (in press)
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if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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This folder contains Matlab (R2020a) and C codes for the manuscript: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the
relationship between behavior and correlated variability. eLife. (in press)
++++++++++++++++++++++++++++++++
To use, first compile all the C codes with the mex compiler in Matlab. Specifically, in the Matlab command line, run the following commands:
mex EIF1DRFfastslowSyn.c
mex spktime2count.c
Sim_Ori_gabor_L2.m simulates a three-layer network with different attentional modulation. Simulation code for Fig. 2. Saves spike counts. CollectSpk.m collect spike counts from every 500 simulations into one file, which will be used to compute model statistics, Fisher information and decoder performance. ModelStat.m Compute model statistics for different attentional modulation (Fig 2D-F) FIdecoder_cluster_L1.m computes Fisher information for the specific decoder (Fig. 3B). Calls function FIdecoder.m GD_th_cluster.m computes Fisher information for the general decoder (Fig. 3B). CollectData.m Collects Fisher information vs. number of neurons data from different parameter sets.
Local_global_svm.m Compare performance of specific and general decoders with small number of neurons (Fig. 3D)
RF2D3layer.m is the main simulation function. It contains default parameter values and uses the mex file EIF1DRFfastslowSyn.c for integration. genXspk.m generate spike trains of input from Layer 1. gen_weights.m generates weight matrices without tuning-dependent connections ori_map.m generates a columnar orientation map. spktime2count.c converts spike time data to spike counts. raster2D_ani.m generates movie of spike rasters from 2D spatial networks. +++++++++++++++++++++++++++++++++++
One simulation of a three-layer network for 20 sec takes about 2 hours CPU time and under 3 gb memory. (Sim_Ori_gabor_L2.m)

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Codes for paper: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the relationship between behavior and correlated variability. eLife. (in press)
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
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try {
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This folder contains Matlab (R2020a) and C codes for the manuscript: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the
relationship between behavior and correlated variability. eLife. (in press)
++++++++++++++++++++++++++++++++
To use, first compile all the C codes with the mex compiler in Matlab. Specifically, in the Matlab command line, run the following commands:
mex EIF1DRFfastslowSyn.c
mex spktime2count.c
Sim_Ori_gabor_L2.m simulates a three-layer network with different attentional modulation. Simulation code for Fig. 2. Saves spike counts. CollectSpk.m collect spike counts from every 500 simulations into one file, which will be used to compute model statistics, Fisher information and decoder performance. ModelStat.m Compute model statistics for different attentional modulation (Fig 2D-F) FIdecoder_cluster_L1.m computes Fisher information for the specific decoder (Fig. 3B). Calls function FIdecoder.m GD_th_cluster.m computes Fisher information for the general decoder (Fig. 3B). CollectData.m Collects Fisher information vs. number of neurons data from different parameter sets.
Local_global_svm.m Compare performance of specific and general decoders with small number of neurons (Fig. 3D)
RF2D3layer.m is the main simulation function. It contains default parameter values and uses the mex file EIF1DRFfastslowSyn.c for integration. genXspk.m generate spike trains of input from Layer 1. gen_weights.m generates weight matrices without tuning-dependent connections ori_map.m generates a columnar orientation map. spktime2count.c converts spike time data to spike counts. raster2D_ani.m generates movie of spike rasters from 2D spatial networks. +++++++++++++++++++++++++++++++++++
One simulation of a three-layer network for 20 sec takes about 2 hours CPU time and under 3 gb memory. (Sim_Ori_gabor_L2.m)

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Codes for paper: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the relationship between behavior and correlated variability. eLife. (in press)
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if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
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var __m = "*";
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This folder contains Matlab (R2020a) and C codes for the manuscript: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the
relationship between behavior and correlated variability. eLife. (in press)
++++++++++++++++++++++++++++++++
To use, first compile all the C codes with the mex compiler in Matlab. Specifically, in the Matlab command line, run the following commands:
mex EIF1DRFfastslowSyn.c
mex spktime2count.c
Sim_Ori_gabor_L2.m simulates a three-layer network with different attentional modulation. Simulation code for Fig. 2. Saves spike counts. CollectSpk.m collect spike counts from every 500 simulations into one file, which will be used to compute model statistics, Fisher information and decoder performance. ModelStat.m Compute model statistics for different attentional modulation (Fig 2D-F) FIdecoder_cluster_L1.m computes Fisher information for the specific decoder (Fig. 3B). Calls function FIdecoder.m GD_th_cluster.m computes Fisher information for the general decoder (Fig. 3B). CollectData.m Collects Fisher information vs. number of neurons data from different parameter sets.
Local_global_svm.m Compare performance of specific and general decoders with small number of neurons (Fig. 3D)
RF2D3layer.m is the main simulation function. It contains default parameter values and uses the mex file EIF1DRFfastslowSyn.c for integration. genXspk.m generate spike trains of input from Layer 1. gen_weights.m generates weight matrices without tuning-dependent connections ori_map.m generates a columnar orientation map. spktime2count.c converts spike time data to spike counts. raster2D_ani.m generates movie of spike rasters from 2D spatial networks. +++++++++++++++++++++++++++++++++++
One simulation of a three-layer network for 20 sec takes about 2 hours CPU time and under 3 gb memory. (Sim_Ori_gabor_L2.m)

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Codes for paper: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the relationship between behavior and correlated variability. eLife. (in press)
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injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
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This folder contains Matlab (R2020a) and C codes for the manuscript: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the
relationship between behavior and correlated variability. eLife. (in press)
++++++++++++++++++++++++++++++++
To use, first compile all the C codes with the mex compiler in Matlab. Specifically, in the Matlab command line, run the following commands:
mex EIF1DRFfastslowSyn.c
mex spktime2count.c
Sim_Ori_gabor_L2.m simulates a three-layer network with different attentional modulation. Simulation code for Fig. 2. Saves spike counts. CollectSpk.m collect spike counts from every 500 simulations into one file, which will be used to compute model statistics, Fisher information and decoder performance. ModelStat.m Compute model statistics for different attentional modulation (Fig 2D-F) FIdecoder_cluster_L1.m computes Fisher information for the specific decoder (Fig. 3B). Calls function FIdecoder.m GD_th_cluster.m computes Fisher information for the general decoder (Fig. 3B). CollectData.m Collects Fisher information vs. number of neurons data from different parameter sets.
Local_global_svm.m Compare performance of specific and general decoders with small number of neurons (Fig. 3D)
RF2D3layer.m is the main simulation function. It contains default parameter values and uses the mex file EIF1DRFfastslowSyn.c for integration. genXspk.m generate spike trains of input from Layer 1. gen_weights.m generates weight matrices without tuning-dependent connections ori_map.m generates a columnar orientation map. spktime2count.c converts spike time data to spike counts. raster2D_ani.m generates movie of spike rasters from 2D spatial networks. +++++++++++++++++++++++++++++++++++
One simulation of a three-layer network for 20 sec takes about 2 hours CPU time and under 3 gb memory. (Sim_Ori_gabor_L2.m)

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Codes for paper: A.M. Ni, C. Huang, B. Doiron and M.R. Cohen (2022) A general decoding strategy explains the relationship between behavior and correlated variability. eLife. (in press)
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