Coding stimulus information with cooperative neural populations

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.