Fitting predictive coding to the neurophysiological data (Spratling 2019)

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MATLAB code for simulating the response properties of V1 mismatch neurons and for testing the ability of predictive coding algorithms to scale. This code performs the experiments described in: Spratling MW (2019) Abstract: "Recent neurophysiological data showing the effects of locomotion on neural activity in mouse primary visual cortex has been interpreted as providing strong support for the predictive coding account of cortical function. Specifically, this work has been interpreted as providing direct evidence that prediction-error, a distinguishing property of predictive coding, is encoded in cortex. This article evaluates these claims and highlights some of the discrepancies between the proposed predictive coding model and the neuro-biology. Furthermore, it is shown that the model can be modified so as to fit the empirical data more successfully."
1 . Spratling MW (2019) Fitting predictive coding to the neurophysiological data. Brain Res 1720:146313 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Predictive Coding Network;
Brain Region(s)/Organism: Neocortex; Mouse;
Cell Type(s):
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Vision; Posture and locomotion; Sensory processing;
Implementer(s): Spratling, MW [michael.spratling at];
function position=maxsubplot(rows,cols,ind,fac)
%Create subplots that are larger than those produced by the standard subplot command.
%Good for plots with no axis labels, tick labels or titles.
%*NOTE*, unlike subplot new axes are drawn on top of old ones; use clf first
%if you don't want this to happen.
%*NOTE*, unlike subplot the first axes are drawn at the bottom-left of the

if nargin<4, fac=0.05; end

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