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      Attention model of binocular rivalry

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          Significance

          Binocular rivalry provides a unique opportunity to characterize intrinsic neural dynamics of cortical processing. A computational model was developed as a parsimonious explanation of the empirical phenomena of rivalry for which there was no previous explanation. The key idea in the model is that rivalry relies on interactions between sensory processing and attentional modulation with distinct dynamics and selectivity. Bifurcation theory was used to identify the parameter regime in which the behavior of the model was consistent with empirical findings. The model explained a wide range of phenomena, including ( i) that binocular rivalry requires attention, ( ii) that different perceptual states emerge when the two images are swapped between the eyes, and ( iii) how dominance duration changes as a function of stimulus input strength.

          Abstract

          When the corresponding retinal locations in the two eyes are presented with incompatible images, a stable percept gives way to perceptual alternations in which the two images compete for perceptual dominance. As perceptual experience evolves dynamically under constant external inputs, binocular rivalry has been used for studying intrinsic cortical computations and for understanding how the brain regulates competing inputs. Converging behavioral and EEG results have shown that binocular rivalry and attention are intertwined: binocular rivalry ceases when attention is diverted away from the rivalry stimuli. In addition, the competing image in one eye suppresses the target in the other eye through a pattern of gain changes similar to those induced by attention. These results require a revision of the current computational theories of binocular rivalry, in which the role of attention is ignored. Here, we provide a computational model of binocular rivalry. In the model, competition between two images in rivalry is driven by both attentional modulation and mutual inhibition, which have distinct selectivity (feature vs. eye of origin) and dynamics (relatively slow vs. relatively fast). The proposed model explains a wide range of phenomena reported in rivalry, including the three hallmarks: ( i) binocular rivalry requires attention; ( ii) various perceptual states emerge when the two images are swapped between the eyes multiple times per second; ( iii) the dominance duration as a function of input strength follows Levelt’s propositions. With a bifurcation analysis, we identified the parameter space in which the model’s behavior was consistent with experimental results.

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          Most cited references74

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          Visual attention: the past 25 years.

          This review focuses on covert attention and how it alters early vision. I explain why attention is considered a selective process, the constructs of covert attention, spatial endogenous and exogenous attention, and feature-based attention. I explain how in the last 25 years research on attention has characterized the effects of covert attention on spatial filters and how attention influences the selection of stimuli of interest. This review includes the effects of spatial attention on discriminability and appearance in tasks mediated by contrast sensitivity and spatial resolution; the effects of feature-based attention on basic visual processes, and a comparison of the effects of spatial and feature-based attention. The emphasis of this review is on psychophysical studies, but relevant electrophysiological and neuroimaging studies and models regarding how and where neuronal responses are modulated are also discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.
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            The importance of mixed selectivity in complex cognitive tasks.

            Single-neuron activity in the prefrontal cortex (PFC) is tuned to mixtures of multiple task-related aspects. Such mixed selectivity is highly heterogeneous, seemingly disordered and therefore difficult to interpret. We analysed the neural activity recorded in monkeys during an object sequence memory task to identify a role of mixed selectivity in subserving the cognitive functions ascribed to the PFC. We show that mixed selectivity neurons encode distributed information about all task-relevant aspects. Each aspect can be decoded from the population of neurons even when single-cell selectivity to that aspect is eliminated. Moreover, mixed selectivity offers a significant computational advantage over specialized responses in terms of the repertoire of input-output functions implementable by readout neurons. This advantage originates from the highly diverse nonlinear selectivity to mixtures of task-relevant variables, a signature of high-dimensional neural representations. Crucially, this dimensionality is predictive of animal behaviour as it collapses in error trials. Our findings recommend a shift of focus for future studies from neurons that have easily interpretable response tuning to the widely observed, but rarely analysed, mixed selectivity neurons.
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              Normalization as a canonical neural computation.

              There is increasing evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations to different problems. A promising candidate for such a computation is normalization, in which the responses of neurons are divided by a common factor that typically includes the summed activity of a pool of neurons. Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions. Normalization may underlie operations such as the representation of odours, the modulatory effects of visual attention, the encoding of value and the integration of multisensory information. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that it serves as a canonical neural computation.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                25 July 2017
                10 July 2017
                : 114
                : 30
                : E6192-E6201
                Affiliations
                [1] aDepartment of Psychology, New York University , New York, NY 10003;
                [2] bCenter for Neural Science, New York University , New York, NY 10003;
                [3] cCollege of Engineering, Mathematics & Physical Sciences, University of Exeter , Exeter EX4 4QJ, United Kingdom;
                [4] dCourant Institute of Mathematical Sciences, New York University , New York, NY 10012
                Author notes
                1To whom correspondence may be addressed. Email: hsin.hung.li@ 123456nyu.edu or david.heeger@ 123456nyu.edu .

                Edited by Wilson S. Geisler, University of Texas at Austin, Austin, TX, and approved June 5, 2017 (received for review December 19, 2016)

                Author contributions: H.-H.L., M.C., and D.J.H. designed research; H.-H.L. and J. Rankin performed research; H.-H.L. and J. Rankin analyzed the model; J. Rinzel, M.C., and D.J.H. supervised the research; and H.-H.L., J. Rankin, J. Rinzel, M.C., and D.J.H. wrote the paper.

                Author information
                http://orcid.org/0000-0003-3245-2071
                Article
                PMC5544261 PMC5544261 5544261 201620475
                10.1073/pnas.1620475114
                5544261
                28696323
                453204df-4296-47a7-9366-97675a0f1534
                History
                Page count
                Pages: 10
                Funding
                Funded by: HHS | NIH | National Eye Institute (NEI) 100000053
                Award ID: R01-EY019693
                Funded by: HHS | NIH | National Eye Institute (NEI) 100000053
                Award ID: R01-EY025673
                Funded by: HHS | National Institutes of Health (NIH) 100000002
                Award ID: R90DA043849
                Funded by: Swartz Fundation
                Award ID: Swartz Fundation
                Categories
                PNAS Plus
                Biological Sciences
                Neuroscience
                Social Sciences
                Psychological and Cognitive Sciences
                PNAS Plus

                computational model,binocular rivalry,bistable perception,visual attention,dynamical system

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