Max Planck Institute
Discovery of salient low-dimensional dynamical structure in neuronal population activity using Hopfield networks
Tuesday 21st of July 2015 at 02:00pm
We present a novel method for the classical task of finding and
extracting recurring spatiotemporal patterns in recorded spiking
activity of neuronal populations.
In contrast to previously proposed methods it does not seek to
classify exactly recurring patterns, but rather approximate versions
possibly differing by a certain number of missed, shifted or excess
We achieve this by fitting large Hopfield networks to windowed, binned
spiking activity in an unsupervised way using minimum probability flow
parameter estimation and then collect Hopfield memories over the raw
This procedure results in a drastic reduction of pattern counts and
can be exploited to identify prominently recurring spatiotemporal
Modeling furthermore the sequence of occurring Hopfield memories over
the original data as a Markov process, we are able to extract
low-dimensional representations of neural population activity on
longer time scales.
We demonstrate the approach on a data set obtained in rat barrel
cortex and show that it is able to extract a remarkably
low-dimensional, yet accurate representation of average population
activity observed during the experiment.
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