New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics
When the two eyes are presented with highly distinct stimuli, the resulting visual percept generally switches every few seconds between the two monocular images in an irregular fashion, giving rise to a phenomenon known as binocular rivalry. While a host of theoretical studies have explored potentia...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2023.1137015/full |
| _version_ | 1797861104771137536 |
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| author | Kenneth Barkdoll Yuhua Lu Victor J. Barranca |
| author_facet | Kenneth Barkdoll Yuhua Lu Victor J. Barranca |
| author_sort | Kenneth Barkdoll |
| collection | DOAJ |
| description | When the two eyes are presented with highly distinct stimuli, the resulting visual percept generally switches every few seconds between the two monocular images in an irregular fashion, giving rise to a phenomenon known as binocular rivalry. While a host of theoretical studies have explored potential mechanisms for binocular rivalry in the context of evoked model dynamics in response to simple stimuli, here we investigate binocular rivalry directly through complex stimulus reconstructions based on the activity of a two-layer neuronal network model with competing downstream pools driven by disparate monocular stimuli composed of image pixels. To estimate the dynamic percept, we derive a linear input-output mapping rooted in the non-linear network dynamics and iteratively apply compressive sensing techniques for signal recovery. Utilizing a dominance metric, we are able to identify when percept alternations occur and use data collected during each dominance period to generate a sequence of percept reconstructions. We show that despite the approximate nature of the input-output mapping and the significant reduction in neurons downstream relative to stimulus pixels, the dominant monocular image is well-encoded in the network dynamics and improvements are garnered when realistic spatial receptive field structure is incorporated into the feedforward connectivity. Our model demonstrates gamma-distributed dominance durations and well obeys Levelt's four laws for how dominance durations change with stimulus strength, agreeing with key recurring experimental observations often used to benchmark rivalry models. In light of evidence that individuals with autism exhibit relatively slow percept switching in binocular rivalry, we corroborate the ubiquitous hypothesis that autism manifests from reduced inhibition in the brain by systematically probing our model alternation rate across choices of inhibition strength. We exhibit sufficient conditions for producing binocular rivalry in the context of natural scene stimuli, opening a clearer window into the dynamic brain computations that vary with the generated percept and a potential path toward further understanding neurological disorders. |
| first_indexed | 2024-04-09T21:56:41Z |
| format | Article |
| id | doaj.art-b3628dc2826041ed851b5edf6df235f7 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-09T21:56:41Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-b3628dc2826041ed851b5edf6df235f72023-03-24T04:33:50ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882023-03-011710.3389/fncom.2023.11370151137015New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamicsKenneth BarkdollYuhua LuVictor J. BarrancaWhen the two eyes are presented with highly distinct stimuli, the resulting visual percept generally switches every few seconds between the two monocular images in an irregular fashion, giving rise to a phenomenon known as binocular rivalry. While a host of theoretical studies have explored potential mechanisms for binocular rivalry in the context of evoked model dynamics in response to simple stimuli, here we investigate binocular rivalry directly through complex stimulus reconstructions based on the activity of a two-layer neuronal network model with competing downstream pools driven by disparate monocular stimuli composed of image pixels. To estimate the dynamic percept, we derive a linear input-output mapping rooted in the non-linear network dynamics and iteratively apply compressive sensing techniques for signal recovery. Utilizing a dominance metric, we are able to identify when percept alternations occur and use data collected during each dominance period to generate a sequence of percept reconstructions. We show that despite the approximate nature of the input-output mapping and the significant reduction in neurons downstream relative to stimulus pixels, the dominant monocular image is well-encoded in the network dynamics and improvements are garnered when realistic spatial receptive field structure is incorporated into the feedforward connectivity. Our model demonstrates gamma-distributed dominance durations and well obeys Levelt's four laws for how dominance durations change with stimulus strength, agreeing with key recurring experimental observations often used to benchmark rivalry models. In light of evidence that individuals with autism exhibit relatively slow percept switching in binocular rivalry, we corroborate the ubiquitous hypothesis that autism manifests from reduced inhibition in the brain by systematically probing our model alternation rate across choices of inhibition strength. We exhibit sufficient conditions for producing binocular rivalry in the context of natural scene stimuli, opening a clearer window into the dynamic brain computations that vary with the generated percept and a potential path toward further understanding neurological disorders.https://www.frontiersin.org/articles/10.3389/fncom.2023.1137015/fullneuronal networksbinocular rivalrystimulus encodingcompressive sensingnon-linear dynamicsinput-output mapping |
| spellingShingle | Kenneth Barkdoll Yuhua Lu Victor J. Barranca New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics Frontiers in Computational Neuroscience neuronal networks binocular rivalry stimulus encoding compressive sensing non-linear dynamics input-output mapping |
| title | New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics |
| title_full | New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics |
| title_fullStr | New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics |
| title_full_unstemmed | New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics |
| title_short | New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics |
| title_sort | new insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics |
| topic | neuronal networks binocular rivalry stimulus encoding compressive sensing non-linear dynamics input-output mapping |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2023.1137015/full |
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