Michigan State University - Research Groups

Understanding the mechanism underlying distributed
neural coding is a fundamental goal in computational
neuroscience. With the ability to simultaneously observe the
activity of large networks of neurons in response to external
stimuli, a natural question arises: how the outside world is
represented in the collective activity of these neurons? In this
work, we provide an information theoretic approach for
determining the role of cooperation among neurons in encoding
external stimuli. Specifically, we show that statistical
independence between neuronal outputs may not provide the best
coding strategy when these outputs depend on the history of
other neuronal constituents in the network. Rather, cooperation
among neurons can provide a near optimal and lossless coding
strategy under specific constraints governing their network
structure. Using a statistical learning model, we demonstrate the
performance of the proposed approach in decoding a motor task
with both discrete targets and continuous trajectory using spike
trains from a small subset of a large network. We demonstrate its
superiority in minimizing the decoding error compared to a
statistically independent model and to other classical decoders
reported in the literature.