Corticofugal feedback influences the generation of length tuning in the visual pathway

Murphy, Patrick D.; Sillito, Adam M.

doi:10.1038/329727a0

Cited by 246 publications

(202 citation statements)

References 14 publications

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“…Thus, the level of cortical activity could regulate the sleep-related activity of thalamic neurons via mGlu1 receptor activation (Hughes et al 2002). However, it is also highly likely that there is a contribution from corticofugal systems to sensory responses in vivo, and this has been investigated over many years (Tsumoto et al 1978;Murphy & Sillito 1987;Sillito et al 1994) (and see Sherman & Guillery (2002) and Sillito & Jones (2002)). It has been speculated that the in uence of the cortical input may operate via NMDA receptors or mGlu receptors (Koch 1987;Sherman & Guillery 2000), largely because transmission via these receptor types would allow the non-linear ampli cation of excitatory inputs mediated via, for example, AMPA receptors.…”

Section: Sensory Responses Of Thalmic Relay Neurons In Vivo: Recruitmmentioning

confidence: 99%

Glutamate receptor functions in sensory relay in the thalamus

Salt

2002

Phil. Trans. R. Soc. Lond. B

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It is known that glutamate is a major excitatory transmitter of sensory and cortical afferents to the thalamus. These actions are mediated via several distinct receptors with postsynaptic excitatory effects predominantly mediated by ionotropic receptors of the a-amino-3-hydroxy-5-methyl-4-isoxazolepropionate (AMPA) and N-methyl-d-aspartate varieties (NMDA). However, there are also other kinds of glutamate receptor present in the thalamus, notably the metabotropic and kainate types, and these may have more complex or subtle roles in sensory transmission. This paper describes recent electrophysiological experiments done in vitro and in vivo which aim to determine how the metabotropic and kainate receptor types can in uence transmission through the sensory thalamic relay. A particular focus will be how such mechanisms might operate under physiological conditions.

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Section: Sensory Responses Of Thalmic Relay Neurons In Vivo: Recruitmmentioning

confidence: 99%

Glutamate receptor functions in sensory relay in the thalamus

Salt

2002

Phil. Trans. R. Soc. Lond. B

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“…This has been established most thoroughly in the visual system, where lower (primary) areas receive sensory input and higher areas adopt a multimodal or associational role. The neurobiological notion of a hierarchy rests upon the distinction between forward and backward connections (Rockland & Pandya 1979;Murphy & Sillito 1987;Felleman & Van Essen 1991;Sherman & Guillery 1998;Angelucci et al 2002). This distinction is based upon the specificity of cortical layers that are the predominant sources and origins of extrinsic connections.…”

Section: Hierarchical Models In the Brainmentioning

confidence: 99%

Predictive coding under the free-energy principle

Friston

Kiebel

2009

Phil. Trans. R. Soc. B

1,260

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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“…Murphy and Sillito (1987) showed that cortical feedback causes signi cant length-tuning in cat LGN cells. As in cortical endstopping, the response to a line grows rapidly as a function of line length and then abruptly declines for longer lines.…”

Section: Resonant Dynamics During Brightness Perceptionmentioning

confidence: 99%

The Link Between Brain Learning, Attention, and Consciousness

Grossberg

2009

Causality, Meaningful Complexity and Embodied Cognition

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Corticofugal feedback influences the generation of length tuning in the visual pathway

Cited by 246 publications

References 14 publications

Glutamate receptor functions in sensory relay in the thalamus

Glutamate receptor functions in sensory relay in the thalamus

Predictive coding under the free-energy principle

The Link Between Brain Learning, Attention, and Consciousness

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