Visual Receptive-field Properties of Single Cells in the Rat Superior Colliculus

Fukuda, Yutaka; Iwama, Kitsuya

doi:10.2170/jjphysiol.28.385

Cited by 56 publications

(12 citation statements)

References 45 publications

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“…On light or electrical stimulation of embryonic rat retinal transplants in the tectum of newborn rats, light evoked slow wave potentials could be recorded from the transplants and the tectum (Simons and Lund, 1985), closely conforming with observations on normal rodent colliculus (Fukuda and Iwama, 1978). Responses could also be recorded from cortical area 18a, and they were most likely mediated by circuitry involving intermediate relays (Craner et al, 1989).…”

Section: Connections With Host Target Tissuesupporting

confidence: 78%

Development of Retinal Transplants

Sharma

1998

Acta Ophthalmologica Scandinavica

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Section: Connections With Host Target Tissuesupporting

confidence: 78%

Development of Retinal Transplants

Sharma

1998

Acta Ophthalmologica Scandinavica

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“…In the mouse, superficial SC neurons respond best to stimuli moved slowly through the RF (Drager and Hubel 1975). In rats, Fukuda and Iwama (1978) showed that most superficial layer neurons responded poorly to velocities faster than 40°/s. In the cat, however, only 23% of neurons were selective for slowly moving stimuli (Dreher and Hoffman 1973).…”

Section: Comparison Of Surround Inhibition and Velocity Tuning With Omentioning

confidence: 96%

Neural Mechanisms of Stimulus Velocity Tuning in the Superior Colliculus

Razak¹,

Pallas²

2005

Journal of Neurophysiology

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Superior colliculus (SC)-mediated control of visuomotor behavior depends on neuronal selectivity for stimulus velocity. However, the mechanism responsible for velocity tuning in SC neurons is unclear. It was shown in a previous study of anesthetized, decorticate hamsters that the number and distribution of feed-forward retinal inputs are not critical for velocity tuning. Here the alternate hypothesis that inhibition from the surround determines velocity tuning of SC neurons was tested. Surround inhibition was present in 65% (43/66) of SC neurons recorded in the superficial gray layer. Neurons within this group that were selective for slowly moving stimuli exhibited spatially asymmetric surround inhibition, and their velocity tuning arose by preferential suppression of responses to rapidly moving stimuli. In the other 35% (23/66) of SC neurons recorded, surround inhibition was weak or absent and did not play a role in velocity tuning. Most neurons with surround inhibition were nonselective for the duration of stationary flashed stimuli, whereas neurons without surround inhibition were selective for stimulus duration. The majority of neurons that preferred intermediate or rapidly moving stimuli exhibited spatially symmetric surround inhibition. In these neurons, occluding the surround reduced velocity selectivity by enhancing responses to slowly moving stimuli. Based on these data, a model is proposed suggesting spatiotemporal interactions between inhibition and excitation that could underlie velocity tuning.

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“…This property is closely matched with the notion of feature-agnostic saliency map (figure 3 a ). Some directional selectivity has been seen in cats [61], rats [62] and mice [63], but our focus is on macaque monkeys, whose response characteristics are closer to humans. Recently, more direct evidence has emerged supporting salience signals in SGS.…”

Section: Visual Pathways For Saliencementioning

confidence: 99%

How is visual salience computed in the brain? Insights from behaviour, neurobiology and modelling

Veale

Hafed²,

Yoshida

2017

Phil. Trans. R. Soc. B

144

143

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Inherent in visual scene analysis is a bottleneck associated with the need to sequentially sample locations with foveating eye movements. The concept of a ‘saliency map’ topographically encoding stimulus conspicuity over the visual scene has proven to be an efficient predictor of eye movements. Our work reviews insights into the neurobiological implementation of visual salience computation. We start by summarizing the role that different visual brain areas play in salience computation, whether at the level of feature analysis for bottom-up salience or at the level of goal-directed priority maps for output behaviour. We then delve into how a subcortical structure, the superior colliculus (SC), participates in salience computation. The SC represents a visual saliency map via a centre-surround inhibition mechanism in the superficial layers, which feeds into priority selection mechanisms in the deeper layers, thereby affecting saccadic and microsaccadic eye movements. Lateral interactions in the local SC circuit are particularly important for controlling active populations of neurons. This, in turn, might help explain long-range effects, such as those of peripheral cues on tiny microsaccades. Finally, we show how a combination of in vitro neurophysiology and large-scale computational modelling is able to clarify how salience computation is implemented in the local circuit of the SC.This article is part of the themed issue ‘Auditory and visual scene analysis’.

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Visual Receptive-field Properties of Single Cells in the Rat Superior Colliculus

Abstract: Receptive-field properties were

Cited by 56 publications

References 45 publications

Development of Retinal Transplants

Development of Retinal Transplants

Neural Mechanisms of Stimulus Velocity Tuning in the Superior Colliculus

How is visual salience computed in the brain? Insights from behaviour, neurobiology and modelling

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