title: Sparsity and compressed coding in sensory systems.
abstract: Considering that 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 numbers 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 comprised of receptors and downstream sensory neurons. Then, by revealing a 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 beyond conventional CS theory. The results of this work suggest a new network framework of signal sparsity, freeing the notion from any dependence on specific component-space representations. 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.
authors.affiliation: Courant Institute of Mathematical Sciences and Center for Neural Science, New York University, New York, New York, United States of America; NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
authors.forename: Victor J
authors.initials: VJ
authors.lastname: Barranca
authors.affiliation: Mathematical Sciences Department, Rensselaer Polytechnic Institute, Troy, New York, United States of America.
authors.forename: Gregor
authors.initials: G
authors.lastname: Kovačič
authors.affiliation: Department of Mathematics, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Douglas
authors.initials: D
authors.lastname: Zhou
authors.affiliation: Courant Institute of Mathematical Sciences and Center for Neural Science, New York University, New York, New York, United States of America; NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates; Department of Mathematics, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: David
authors.initials: D
authors.lastname: Cai
mesh_terms: D000465:Algorithms; D000818:Animals; D002415:Cats; D003198:Computer Simulation; D008959:Models, Neurological; D012160:Retina / Q000502:physiology*; D012165:Retinal Ganglion Cells / Q000502:physiology*; D015398:Signal Transduction / Q000502:physiology*