Sparsity and Compressed Coding in Sensory Systems |
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Authors: | Victor J Barranca Gregor Kova?i? Douglas Zhou David Cai |
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Institution: | 1.Courant Institute of Mathematical Sciences and Center for Neural Science, New York University, New York, New York, United States of America;2.NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates;3.Mathematical Sciences Department, Rensselaer Polytechnic Institute, Troy, New York, United States of America;4.Department of Mathematics, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China;Indiana University, United States of America |
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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. |
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