ArticlePLoS computational biology2014
Sparsity and compressed coding in sensory systems.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 32 citations in OpenAlex.
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- New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics.Frontiers in computational neuroscience · 2023Article
- Sensory Coding of Limb Kinematics in Motor Cortex across a Key Developmental Transition.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- The impact of spike-frequency adaptation on balanced network dynamics.Cognitive neurodynamics · 2019Article
- Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics.Frontiers in neuroscience · 2019Article
- Simulation of retinal ganglion cell response using fast independent component analysis.Cognitive neurodynamics · 2018Article
- Nanowire FET Based Neural Element for Robotic Tactile Sensing Skin.Frontiers in neuroscience · 2017Article
- Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling.Scientific reports · 2016Article
- Sensory-evoked perturbations of locomotor activity by sparse sensory input: a computational study.Journal of neurophysiology · 2015Article
- Learning structure of sensory inputs with synaptic plasticity leads to interference.Frontiers in computational neuroscience · 2015Article
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Authors and funding
4 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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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