Neural model of temporal and stochastic properties of binocular rivalry

Kalarickal, George J.; Marshall, Jonathan A.

doi:10.1016/s0925-2312(00)00252-6

Cited by 53 publications

(39 citation statements)

References 14 publications

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“…16 and associated text). The range of formal approaches to binocular rivalry in the literature is quite diverse (Blake, 1989;Borisyuk, Chik, & Kazanovich, 2009;Curtu et al, 2008;Gigante et al, 2009;Grossberg, 1987;Grossberg et al, 2008;Hohwy, Roepstorff, & Friston, 2008;Kalarickal & Marshall, 2000;Laing and Chow, 2002;Lehky, 1988;Moreno-Bote et al, 2007, 2010Mueller, 1990;Noest et al, 2007;Noest & van Wezel, 2012;Said & Heeger, 2013;Seely & Chow, 2011;Shpiro et al, 2009;Stollenwerk & Bode, 2003;Sundareswara & Schrater, 2008;Wilson, 2003Wilson, , 2007. Nevertheless, certain commonalities can be identified across many models that display aspects of the behavior reviewed above.…”

Section: Implications Of the (Modified) Propositions For Neural Mechamentioning

confidence: 99%

The ‘laws’ of binocular rivalry: 50 years of Levelt’s propositions

2015

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It has been fifty years since Levelt's monograph On Binocular Rivalry (1965) was published, but its four propositions that describe the relation between stimulus strength and the phenomenology of binocular rivalry remain a benchmark for theorists and experimentalists even today. In this review, we will revisit the original conception of the four propositions and the scientific landscape in which this happened. We will also provide a brief update concerning distributions of dominance durations, another aspect of Levelt's monograph that has maintained a prominent presence in the field. In a critical evaluation of Levelt's propositions against current knowledge of binocular rivalry we will then demonstrate that the original propositions are not completely compatible with what is known today, but that they can, in a straightforward way, be modified to encapsulate the progress that has been made over the past fifty years. The resulting modified, propositions are shown to apply to a broad range of bistable perceptual phenomena, not just binocular rivalry, and they allow important inferences about the underlying neural systems. We argue that these inferences reflect canonical neural properties that play a role in visual perception in general, and we discuss ways in which future research can build on the work reviewed here to attain a better understanding of these properties.

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Section: Implications Of the (Modified) Propositions For Neural Mechamentioning

confidence: 99%

The ‘laws’ of binocular rivalry: 50 years of Levelt’s propositions

2015

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“…A number of formal models have been developed which are able to reproduce aspects of binocular rivalry (Dayan, 1998;Grossberg & Mingolla, 1985;Kalarickal, 2000;Kawamoto & Anderson, 1985;Koene, 2006;Laing & Chow, 2002;Lehky, 1988;Lumer, 1998;Matsuoka, 1984;Noest et al, 2007;Wilson, 2003;Wilson, 2007;Zhou, Gao, White, Merk, & Yao, 2004). The majority of these models analyse binocular rivalry a priori as a phenomenon driven by adaptation of the winning neural population, and lateral and/or top down inhibition of the competing population.…”

Section: Recent Models Of Binocular Rivalrymentioning

confidence: 99%

Predictive coding explains binocular rivalry: An epistemological review

2008

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a b s t r a c tBinocular rivalry occurs when the eyes are presented with different stimuli and subjective perception alternates between them. Though recent years have seen a number of models of this phenomenon, the mechanisms behind binocular rivalry are still debated and we still lack a principled understanding of why a cognitive system such as the brain should exhibit this striking kind of behaviour. Furthermore, psychophysical and neurophysiological (single cell and imaging) studies of rivalry are not unequivocal and have proven difficult to reconcile within one framework. This review takes an epistemological approach to rivalry that considers the brain as engaged in probabilistic unconscious perceptual inference about the causes of its sensory input. We describe a simple empirical Bayesian framework, implemented with predictive coding, which seems capable of explaining binocular rivalry and reconciling many findings. The core of the explanation is that selection of one stimulus, and subsequent alternation between stimuli in rivalry occur when: (i) there is no single model or hypothesis about the causes in the environment that enjoys both high likelihood and high prior probability and (ii) when one stimulus dominates, the bottom-up, driving signal for that stimulus is explained away while, crucially, the bottom-up signal for the suppressed stimulus is not, and remains as an unexplained but explainable prediction error signal. This induces instability in perceptual dynamics that can give rise to perceptual transitions or alternations during rivalry.

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“…slow oscillations (Dayan 1998;Freeman 2005;Stollenwerk and Bode 2003;Ashwin and Lavric 2010). Often, noise is added to these models to produce the observed distribution of dominance durations (Kalarickal and Marshall 2000;Lago-Fernandez and Deco 2002). Others have studied more realistic models of biophysically-based spiking neurons, with some of these authors also discussing rate models (Laing and Chow 2002;Moreno-Bote et al 2007;Wilson 2003).…”

Section: Introductionmentioning

confidence: 99%

Reduced models for binocular rivalry

2010

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Binocular rivalry occurs when two very different images are presented to the two eyes, but a subject perceives only one image at a given time. A number of computational models for binocular rivalry have been proposed; most can be categorised as either "rate" models, containing a small number of variables, or as more biophysicallyrealistic "spiking neuron" models. However, a principled derivation of a reduced model from a spiking model is lacking. We present two such derivations, one heuristic and a second using recently-developed data-mining techniques to extract a small number of "macroscopic" variables from the results of a spiking neuron model simulation. We also consider bifurcations that can occur as parameters are varied, and the role of noise in such systems. Our methods are applicable to a number of other models of interest.

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Neural model of temporal and stochastic properties of binocular rivalry

Cited by 53 publications

References 14 publications

The ‘laws’ of binocular rivalry: 50 years of Levelt’s propositions

The ‘laws’ of binocular rivalry: 50 years of Levelt’s propositions

Predictive coding explains binocular rivalry: An epistemological review

Reduced models for binocular rivalry

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