GitHub - ModelDBRepository/234097: Hebbian learning in a random network for PFC modeling (Lindsay, et al. 2017)

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calc_clustering.m		calc_clustering.m
example.jpg		example.jpg
nonliner.m		nonliner.m
perf_ANOV.m		perf_ANOV.m
readme.html		readme.html
runHebbian.m		runHebbian.m
runRCN_track.m		runRCN_track.m
thvar_er.m		thvar_er.m
weight_maker.m		weight_maker.m

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<html><pre>This is the readme for the models associated with the paper:

Lindsay GW, Rigotti M, Warden MR, Miller EK, Fusi S (2017) Hebbian
Learning in a Random Network Captures Selectivity Properties of
Prefrontal Cortex. J Neurosci

This archive was contributed by G Lindsay.

You will need to first install the highdim matlab package by downloading the
matlab scripts and adding the toplevel folder to your matlab path:
<a href="https://github.com/brian-lau/highdim">https://github.com/brian-lau/highdim</a>

Then run this code in matlab by typing on the command line:
runHebbian

After a minute or so you should see a graph like:
<img src="./example.jpg" alt="example" width="550">

The first 5 graphs are similar to Figure 6A, except the values are
plotted only for a single network (rather than the average values over
many networks, for different learning variants). The final graph
(lower right of the graphs) is similar to Figure 6C(left), but again
only for a single network, and represents the model results after the
final learning step. In all, black lines/bars are data values.
</pre></html>