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Visual nonclassical receptive field effects emerge from sparse coding in a dynamical system.

Zhu M, Rozell CJ - PLoS Comput. Biol. (2013)

Bottom Line: Models seeking to explain these non-classical receptive field (nCRF) effects in terms of circuit mechanisms, input-output descriptions, or individual visual tasks provide limited insight into the functional significance of these response properties, because they do not connect the full range of nCRF effects to optimal sensory coding strategies.Furthermore, when the population diversity of an nCRF effect has also been reported in the literature, we 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.

View Article: PubMed Central - PubMed

Affiliation: Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America.

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 provide limited insight into the functional significance of these response properties, because they do not connect the full range of nCRF effects to optimal sensory coding strategies. The (population) sparse coding hypothesis conjectures an optimal sensory coding approach 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 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.

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End-stopping.(A) End-stopping response in a simple cell from cat V1 responding to an optimally-oriented light bar stimulus (data replotted from [32], Figure 1). (B) The length tuning curve of a simulated sparse coding model neuron (target) demonstrates end-stopping behavior.
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pcbi-1003191-g002: End-stopping.(A) End-stopping response in a simple cell from cat V1 responding to an optimally-oriented light bar stimulus (data replotted from [32], Figure 1). (B) The length tuning curve of a simulated sparse coding model neuron (target) demonstrates end-stopping behavior.

Mentions: Stimuli in the region surrounding the CRF can have a modulatory effect on a neuron's response despite not inducing significant response in isolation (by definition of the CRF). In perhaps the simplest form of this suppressive modulation, it has long been known that some V1 neurons exhibit end-stopping where the spike rate decreases for a cell responding to an optimally-oriented bar stimulus when the bar length is increased beyond the CRF boundaries. An example figure depicting the end-stopping effect as observed in cat electrophysiology recordings [32] is reproduced in Fig. 2A. When simulating this experiment [32] on the sparse coding model, some of the model cells (such as the target cell shown in Fig. 2B) exhibit the same characteristic suppression with increasing bar length. The end-stopping effect was previously shown in [33] to emerge in the sparse coding model. The end-stopping effect can be simply understood in terms of the goals of sparse coding. When the bar is short, the CRF of the target cell is the most efficient description of the stimulus and that cell has the strongest response. However, when the bar is long enough that it is better explained by the CRFs of other cells, the target cell becomes suppressed by these competitors so as to maintain a sparse representation. The Discussion section contains a detailed look at how the network interactions supporting the sparse coding model can produce this effect.


Visual nonclassical receptive field effects emerge from sparse coding in a dynamical system.

Zhu M, Rozell CJ - PLoS Comput. Biol. (2013)

End-stopping.(A) End-stopping response in a simple cell from cat V1 responding to an optimally-oriented light bar stimulus (data replotted from [32], Figure 1). (B) The length tuning curve of a simulated sparse coding model neuron (target) demonstrates end-stopping behavior.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3757072&req=5

pcbi-1003191-g002: End-stopping.(A) End-stopping response in a simple cell from cat V1 responding to an optimally-oriented light bar stimulus (data replotted from [32], Figure 1). (B) The length tuning curve of a simulated sparse coding model neuron (target) demonstrates end-stopping behavior.
Mentions: Stimuli in the region surrounding the CRF can have a modulatory effect on a neuron's response despite not inducing significant response in isolation (by definition of the CRF). In perhaps the simplest form of this suppressive modulation, it has long been known that some V1 neurons exhibit end-stopping where the spike rate decreases for a cell responding to an optimally-oriented bar stimulus when the bar length is increased beyond the CRF boundaries. An example figure depicting the end-stopping effect as observed in cat electrophysiology recordings [32] is reproduced in Fig. 2A. When simulating this experiment [32] on the sparse coding model, some of the model cells (such as the target cell shown in Fig. 2B) exhibit the same characteristic suppression with increasing bar length. The end-stopping effect was previously shown in [33] to emerge in the sparse coding model. The end-stopping effect can be simply understood in terms of the goals of sparse coding. When the bar is short, the CRF of the target cell is the most efficient description of the stimulus and that cell has the strongest response. However, when the bar is long enough that it is better explained by the CRFs of other cells, the target cell becomes suppressed by these competitors so as to maintain a sparse representation. The Discussion section contains a detailed look at how the network interactions supporting the sparse coding model can produce this effect.

Bottom Line: Models seeking to explain these non-classical receptive field (nCRF) effects in terms of circuit mechanisms, input-output descriptions, or individual visual tasks provide limited insight into the functional significance of these response properties, because they do not connect the full range of nCRF effects to optimal sensory coding strategies.Furthermore, when the population diversity of an nCRF effect has also been reported in the literature, we 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.

View Article: PubMed Central - PubMed

Affiliation: Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America.

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 provide limited insight into the functional significance of these response properties, because they do not connect the full range of nCRF effects to optimal sensory coding strategies. The (population) sparse coding hypothesis conjectures an optimal sensory coding approach 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 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.

Show MeSH
Related in: MedlinePlus