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.
Highlights
It is well known that natural stimuli, such as visual images, are sparse in the sense that they can be well represented by a small number of dominant components, typically in an appropriate frequency space [1]
How is sensory information preserved along such a pathway? In this work, we put forth a possible answer to this question using compressed sensing, a recent advance in the field of signal processing that demonstrates how sparse signals can be reconstructed using very few samples
We discover that stimuli can be recovered from ganglion-cell dynamics, and demonstrate how localized receptive fields improve stimulus encoding
Summary
It is well known that natural stimuli, such as visual images, are sparse in the sense that they can be well represented by a small number of dominant components, typically in an appropriate frequency space [1]. We may naturally expect that organisms’ sensing has evolved to be adapted to such sparsity. One sign of this adaptation may be the great reduction in numbers between the receptor cells and the sensory neurons in the immediate downstream layers along the early stages of sensory pathways [2,3]. In the retina, the stimuli received by ,150 million rods and cones are transmitted through only ,1.5 million retinal ganglion cells [2]. How have the networks along these pathways evolved so that they can best transmit sparse stimuli and the least amount of information is lost through network dynamics [4,5]?
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