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 print_fig(fileName)

if exist('export_fig')==2 %&& exist('resize_matlab_figure_window')==2
  %set figure window to standard size, as export_fig uses onscreen size to define printed size
  system(['wmctrl -r Figures -e 0,-1,-1,800,500']); %if more than one MATLAB window this command will resize first one only, which is not necessarily the one being printed!
  system(['./resize_matlab_figure_window ',int2str(feature('getpid'))]); %will resize the figure window for this instance of MATLAB, but not 100% reliable!

  set(gcf, 'Color', 'w'); 
  disp('WARNING: using std print command');

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