This code implements the simulation results reported in:

M. W. Spratling (2019) Fitting Predictive Coding to the Neurophysiological Data. Brain Research, 1720: 146313.

Please cite this paper if this code is used in, or to motivate, any publications.

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USAGE

This code was tested with MATLAB Version 9.5.0.944444 (R2018b) running on Ubuntu Linux version 16.04. It also runs using GNU Octave Version 4.0.0.

To re-run the experiments described in the paper, do the following.

Fig. 3b and c can be reproduced using the command:

    keller_fit;

This produces three figures. The first reproduces the fit to the data
proposed by Zmarz and Keller (2016) see their Fig 4D. The 2nd and 3rd images
correspond to Fig. 3b and 3c in the current paper.

Fig. 3d can be reproduced using the command:

    stability;

This produces two figures. The first is Fig 3d which shows the difference in
response between the prediction neuron representing the cause underlying the
input and the next most active prediction neuron. The second shows the same
results, but when plotting the ratio between the response of the prediction
neuron representing the cause underlying the input and the sum of all the
prediction neuron responses.

Results for other versions of the Rao and Ballard algorithm (see footnote
"a") can be obtained by editing randb_pc_activation.m to replace the method
of calculating "dy" by one of the other three versions that are currently
commented out.

Fig. 3e can be reproduced using the command:

    multisensory_integration_log('randb');

The first 6 images show the activities of the error and prediction neurons
to different combinations of running speed (p) and visual flow (v). Image 11
corresponds to Fig. 3e.

Fig. 3f can be reproduced using the command:

    multisensory_integration_log('DIM');

The first 6 images show the activities of the error and prediction neurons
to different combinations of running speed (p) and visual flow (v). Image 11
corresponds to Fig. 3f.