Postdoctoral Research Associate
Courant Institute of Mathematical Sciences and NYU Abu Dhabi
New York University
October 1, 2013
Sparsity and Compressed Coding in Sensory Systems
Considering many natural stimuli are sparse, can a sensory system evolve to take advantage of this sparsity? We explore this question and show that significant downstream reductions in the number of neurons transmitting stimuli observed in early sensory pathways might be a consequence of this sparsity. First, we model an early sensory pathway using an idealized neuronal network comprising of receptors and downstream sensory neurons. Then, by revealing a hidden linear structure intrinsic to neuronal network dynamics, our work points to a potential mechanism for transmitting sparse stimuli, related to compressed-sensing (CS) type data acquisition. Through simulation, we examine the characteristics of networks that are optimal in sparsity encoding, and the impact of localized receptive fields in going beyond conventional CS theory. We expect our CS network mechanism to provide guidance for studying sparse stimulus transmission along realistic sensory pathways as well as engineering network designs that utilize sparsity encoding.