by M. Zhu and C.J. Rozell
Abstract:
Extensive electrophysiology studies have shown that many V1 simple cells have nonlinear response properties to stimuli within their classical receptive field (CRF) and receive contextual influence from stimuli outside the CRF modulating the cell's response. Models seeking to explain these non-classical receptive field (nCRF) effects in terms of circuit mechanisms, input-output descriptions, or individual visual tasks do not connect this broad range of response properties to optimal sensory coding strategies. The (population) sparse coding hypothesis conjectures an optimal sensory coding strategy where a neural population uses as few active units as possible to represent a stimulus. We demonstrate that a wide variety of nCRF effects are emergent properties of a single sparse coding model implemented in a neurally plausible network structure (requiring no parameter tuning to produce different effects). Specifically, we replicate a wide variety of nCRF electrophysiology experiments (e.g., end-stopping, surround suppression, contrast invariance of orientation tuning, cross-orientation suppression, etc.) on a dynamical system implementing sparse coding, showing that this model produces individual units that reproduce the canonical nCRF effects. Furthermore, when the population diversity of an nCRF effect has also been reported in the literature, we also show that this model produces many of the same population characteristics. These results show that the sparse coding hypothesis, when coupled with a biophysically plausible implementation, can provide a unified high-level functional interpretation to many response properties that have generally been viewed through distinct mechanistic or phenomenological models.
Reference:
Visual nonclassical receptive field effects emerge from sparse coding in a dynamical systemM. Zhu and C.J. Rozell. PLoS Computational Biology, 9(8), pp. e1003191, August 2013.
Bibtex Entry:
@Article{zhu.12b,
author = {Zhu, M. and Rozell, C.J.},
title = {Visual nonclassical receptive field effects emerge from sparse coding in a dynamical system},
abstract = {Extensive electrophysiology studies have shown that many V1 simple cells have nonlinear response properties to stimuli within their classical receptive field (CRF) and receive contextual influence from stimuli outside the CRF modulating the cell's response. Models seeking to explain these non-classical receptive field (nCRF) effects in terms of circuit mechanisms, input-output descriptions, or individual visual tasks do not connect this broad range of response properties to optimal sensory coding strategies. The (population) sparse coding hypothesis conjectures an optimal sensory coding strategy where a neural population uses as few active units as possible to represent a stimulus. We demonstrate that a wide variety of nCRF effects are emergent properties of a single sparse coding model implemented in a neurally plausible network structure (requiring no parameter tuning to produce different effects). Specifically, we replicate a wide variety of nCRF electrophysiology experiments (e.g., end-stopping, surround suppression, contrast invariance of orientation tuning, cross-orientation suppression, etc.) on a dynamical system implementing sparse coding, showing that this model produces individual units that reproduce the canonical nCRF effects. Furthermore, when the population diversity of an nCRF effect has also been reported in the literature, we also show that this model produces many of the same population characteristics. These results show that the sparse coding hypothesis, when coupled with a biophysically plausible implementation, can provide a unified high-level functional interpretation to many response properties that have generally been viewed through distinct mechanistic or phenomenological models.},
journal = {PLoS Computational Biology},
year = 2013,
pages = {e1003191},
month = aug,
volume = 9,
number = 8,
url = {http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003191}
}