Transient Dynamics for Neural Processing

Rabinovich, M. I.; Huerta, Ramón; Laurent, Gilles

doi:10.1126/science.1155564

Cited by 465 publications

(398 citation statements)

References 14 publications

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“…It is important to note that we do not focus on any global fixed point reached by the system under the stimulation, but rather on the evoked "succession of transients," which are reliable and noise resistant. This concept has already been the subject of several studies (Rabinovich et al, 2008), and our results are a possible example of this concept in large-scale network models.…”

Section: Reliable Response/completion Despite Irregular Background Acmentioning

confidence: 91%

Reliable Recall of Spontaneous Activity Patterns in Cortical Networks

Marre¹,

Yger²,

Davison³

et al. 2009

J. Neurosci.

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Irregular ongoing activity in cortical networks is often modeled as arising from recurrent connectivity. Yet it remains unclear to what extent its presence corrupts sensory signal transmission and network computational capabilities. In a recurrent cortical-like network, we have determined the activity patterns that are better transmitted and self-sustained by the network. We show that reproducible spiking and subthreshold dynamics can be triggered if the statistics of the imposed external drive are consistent with patterns previously seen in the ongoing activity. A subset of neurons in the network, constrained to replay temporal pattern segments extracted from the recorded ongoing activity of the same network, reliably drives the remaining, free-running neurons to call the rest of the pattern. Comparison with surrogate Poisson patterns indicates that the efficiency of the recall and completion process depends on the similarity between the statistical properties of the input with previous ongoing activity The reliability of evoked dynamics in recurrent networks is thus dependent on the stimulus used, and we propose that the similarity between spontaneous and evoked activity in sensory cortical areas could be a signature of efficient transmission and propagation across cortical networks.

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Section: Reliable Response/completion Despite Irregular Background Acmentioning

confidence: 91%

Reliable Recall of Spontaneous Activity Patterns in Cortical Networks

Marre¹,

Yger²,

Davison³

et al. 2009

J. Neurosci.

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“…These sequences can be simple, such as the quasi-periodic attractors of central pattern generators (McCrea & Rybak 2008), or can exhibit complicated sequences of the sort associated with chaotic and itinerant dynamics (e.g. Haken et al 1990;Friston 1997;Jirsa et al 1998;Kopell et al 2000;Breakspear & Stam 2005;Canolty et al 2006;Rabinovich et al 2008). The notion of attractors as the basis of generative models extends the notion of generalized coordinates, encoding trajectories, to families of trajectories that lie on attractor manifolds, i.e.…”

Section: L4mentioning

confidence: 99%

Predictive coding under the free-energy principle

Friston

Kiebel

2009

Phil. Trans. R. Soc. B

1,307

1,070

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This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain. We assume that the brain models the world as a hierarchy or cascade of dynamical systems that encode causal structure in the sensorium. Perception is equated with the optimization or inversion of these internal models, to explain sensory data. Given a model of how sensory data are generated, we can invoke a generic approach to model inversion, based on a free energy bound on the model's evidence. The ensuing free-energy formulation furnishes equations that prescribe the process of recognition, i.e. the dynamics of neuronal activity that represent the causes of sensory input. Here, we focus on a very general model, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states. We first review hierarchical dynamical models and their inversion. We then show that the brain has the necessary infrastructure to implement this inversion and illustrate this point using synthetic birds that can recognize and categorize birdsongs.Keywords: generative models; predictive coding; hierarchical; birdsong INTRODUCTIONThis paper reviews generic models of our sensorium and a Bayesian scheme for their inversion. We then show that the brain has the necessary anatomical and physiological equipment to invert these models, given sensory data. Critically, the scheme lends itself to a relatively simple neural network implementation that shares many features with real cortical hierarchies in the brain. The basic idea that the brain tries to infer the causes of sensations dates back to Helmholtz (e.g. Helmholtz 1860/1962Barlow 1961;Neisser 1967;Ballard et al. 1983;Mumford 1992;Kawato et al. 1993;Dayan et al. 1995;Rao & Ballard 1998), with a recent emphasis on hierarchical inference and empirical Bayes (Friston 2003(Friston , 2005Friston et al. 2006). Here, we generalize this idea to cover dynamics in the world and consider how neural networks could be configured to invert hierarchical dynamical models and deconvolve sensory causes from sensory input. This paper comprises four sections. In §1, we introduce hierarchical dynamical models and their inversion. These models cover most of the models encountered in the statistical literature. An important aspect of these models is their formulation in generalized coordinates of motion, which lends them a hierarchal form in both structure and dynamics. These hierarchies induce empirical priors that provide structural and dynamical constraints, which can be exploited during inversion. In §2, we show how inversion can be formulated as a simple gradient ascent using neuronal networks; in §3, we consider how evoked brain responses might be understood in terms of inference under hierarchical dynamical models of sensory input. 1

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“…In particular, there may be novel dynamical mechanisms for neural processes that do not fit into the energy landscape paradigm. A particular example of non-variational dynamics called "Winnerless Competition" (WLC) [22,23,24] was introduced by Rabinovich, Huerta and co-workers to explain a variety of switching-type responses and sequence generation for low-level neural microcircuits. Such dynamical models have robust attractors that are composed of a network of unstable states of saddle type connected by their unstable manifolds.…”

Section: Winnerless Competition Models For Cognitive Processesmentioning

confidence: 99%

A low-dimensional model of binocular rivalry using winnerless competition

Ashwin

Lavric

2010

Physica D: Nonlinear Phenomena

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We discuss a novel minimal model for binocular rivalry (and more generally perceptual dominance) effects. The model has only three state variables, but nonetheless exhibits a wide range of input and noise-dependent switching. The model has two reciprocally inhibiting input variables that represent perceptual processes active during the recognition of one of two possible states and a third variable that represents the perceived output. Sensory inputs only affect the input variables.We observe, for rivalry-inducing inputs, the appearance of winnerless competition in the perceptual system. This gives rise to behaviour that conforms to well-known principles describing binocular rivalry (Levelt's propositions, in particular proposition IV: monotonic response of residence time as a function of image contrast) down to very low levels of stimulus intensity.

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Transient Dynamics for Neural Processing

Cited by 465 publications

References 14 publications

Reliable Recall of Spontaneous Activity Patterns in Cortical Networks

Reliable Recall of Spontaneous Activity Patterns in Cortical Networks

Predictive coding under the free-energy principle

A low-dimensional model of binocular rivalry using winnerless competition

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