TY - JOUR
T1 - Sparsity and Compressed Coding in Sensory Systems
AU - Barranca, Victor J.
AU - Kovačič, Gregor
AU - Zhou, Douglas
AU - Cai, David
N1 - Funding Information:
The work was supported by grants NSF DMS-0636358 (for VJB), 10PJ1406300, NSFC-11101275, and NSFC-91230202 (for DZ), NSF DMS-1009575 (DC), SRF for ROCS, SEM (for DZ), and the NYU Abu Dhabi Institute G1301. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2014 Barranca et al.
PY - 2014/8/21
Y1 - 2014/8/21
N2 - 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.
AB - 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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U2 - 10.1371/journal.pcbi.1003793
DO - 10.1371/journal.pcbi.1003793
M3 - Article
C2 - 25144745
AN - SCOPUS:84927973198
VL - 10
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 8
M1 - e1003793
ER -