The Superior Colliculus and Visual Function

Schiller, Peter H.

doi:10.1002/cphy.cp010311

Cited by 32 publications

(29 citation statements)

References 250 publications

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“…The superior colliculus is ; I layered structure of the roof of the midbrain, the superficial layers of which receive a direct projection from the retina that is topographically organized (for reviews o f primate superior colliculus see, eg., Schiller, 1985;Sparks & Mays, 1990;Wurtz & Albano, 1980). Underlying layers contain cells that fire about 20 msec before saccades (the saccade related burst neurons or SWNs).…”

Section: The Problemmentioning

confidence: 99%

Learning and Maintaining Saccadic Accuracy: A Model of Brainstem–Cerebellar Interactions

Dean¹,

Mayhew²,

Langdon

1994

Journal of Cognitive Neuroscience

122

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Saccadic accuracy requires that the control signal sent to the motor neurons must be the right size to bring the fovea to the target, whatever the initial position of the eyes (and corresponding state of the eye muscles). Clinical and experimental evidence indicates that the basic machinery for generating saccadic eye movements, located in the brainstem, is not accurate: learning to make accurate saccades requires cerebellar circuitry located in the posterior vermis and fastigial nucleus. How do these two circuits interact to achieve adaptive control of saccades? A model of this interaction is described, based on Kawato's principle of feedback-error-learning. Its three components were (1) a simple controller with no knowledge of initial eye position, corresponding to the superior colliculus; (2) Robinson's internal feedback model of the saccadic burst generator, corresponding to preoculomotor areas in the brain-stem; and (3) Albus's Cerebellar Model Arithmetic Computer (CMK), a neural net model of the cerebellum. The connections between these components were (I) the simple feedback controller passed a (usually inaccurate) command to the pulse generator, and (2) a copy of this command to the CMAC; (3) the CMAC combined the copy with information about initial eye position to (4) alter the gain on the pulse generator's internal feedback loop, thereby adjusting the size of burst sent to the motor neurons. (5) If the saccade were inaccurate, an error signal from the feedback controller adjusted the weights in the CMAC. It was proposed that connection (2) corresponds to the mossy fiber projection from superior colliculus to oculomotor vermis via the nucleus reticularis tegmenti pontis, and connection (5) to the climbing fiber projection from superior colliculus to the oculomotor vermis via the inferior olive. Plausible initialization values were chosen so that the system produced hypometric saccades (as do human infants) at the start of learning, and position-dependent hypermetric saccades when the cerebellum was removed. Simulations for horizontal eye movements showed that accurate saccades from any starting position could be learned rapidly, even if the error signal conveyed only whether the initial saccade were too large or too small. In subsequent tests the model adapted realistically both to simulated weakening of the eye muscles, and to intrasaccadic displacement of the target, thereby mimicking saccadic plasticity in adults. The architecture of the model may therefore offer a functional explanation of hitherto mysterious tectocerebellar projections, and a framework for investigating in greater detail how the cerebellum adaptively controls saccadic accuracy.

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Section: The Problemmentioning

confidence: 99%

Learning and Maintaining Saccadic Accuracy: A Model of Brainstem–Cerebellar Interactions

Dean¹,

Mayhew²,

Langdon

1994

Journal of Cognitive Neuroscience

122

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“…The presence of maps in the tectum is a common feature in lower vertebrates (Schiller 1984;Vanegas 1984). To a first approximation, tectal maps of different modalities are in register.…”

Section: Spatial Arrangement Of Unitsmentioning

confidence: 99%

Representation of lateral line and electrosensory systems in the midbrain of the axolotl, Ambystoma mexicanum

1990

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The study focussed on the representation of the electrosensory and lateral line units in the midbrain of the axolotl Ambystoma mexicanum. In addition, the responses to photic and acoustic/vibrational stimuli were determined. Unit properties were characterized with respect to baseline activity, sensitivity, latency, directional specificity and number of input modalities. The anatomical arrangement of the units was determined using stereotactic and histological measurements of the electrode positions.Of 106 units recorded, 29 units were unimodal, 77 units responded to more than one modality. Most units discharged only in response to stimuli. Thresholds of electrosensory units were about 100 gV/cm field strength; lateral line units had thresholds below 5 gm pp amplitude. The shortest latencies (8-17 ms) were found for responses to visual stimuli. Lateral line and vestibular units responded after 35-58 ms, electroreceptive units after 79-150 ms. All electrosensory and about 50% of the lateral line units were sharply tuned to definite stimulus directions.Electrosensory and lateral line units formed topographical maps in the tectum. The map in each tectal hemisphere contained information about the contralateral surroundings. The electrosensory, lateral line and visual representations were only partly in register; especially in the caudal areas of the midbrain the alignment was poor.

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“…Each such retinal cell, or small group of them, constitutes the receptive field of the collicular cell to which it projects. As a whole, the topmost layer of the superior colliculus constitutes a metrically deformed topographic map of the retinal surface (see Figure 6b) (Goldberg and Robinson, 1978;Cynader and Berman, 1972;Gordon, 1973;Schiller 1984).…”

Section: B a Specific Case: The Superior Colliculusmentioning

confidence: 99%

“…Vertical elements connect this visual layer to the deepest layer of the superior colliculus, the motor layer (Schiller, 1984). The cells in this layer project their output axons away from the colliculus along two distinct pathways, one that leads ultimately to the pair of eye muscles responsible for vertical eye movements, and the other to the pair of muscles that control horizontal eye movements (Huerta and Harting, 1984).…”

Section: B a Specific Case: The Superior Colliculusmentioning

confidence: 99%

“…In them, the dendrites of the lower cells project upwards to make contact with the cells in the visual layer. This may happen in mammals as well, but here the principal vertical elements appear to consist in a vertical sequence of two or three small interneurons, which connect the top and bottom layers through one or two intermediate layers (Schiller, 1984), of which more presently. Further, position registration within the two maps is not so punctiform as the preceding discussion would imply.…”

Section: B a Specific Case: The Superior Colliculusmentioning

confidence: 99%

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Cognitive neurobiology: a computational hypothesis for laminar cortex

Churchland

1986

Biol Philos

105

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This paper outlines the functional capacities of a novel scheme for cognitive representation and computation, and it explores the possible implementation of this scheme in the massively parallel organization of the empirical brain. The suggestion is that the brain represents reality by means of positions in suitably constituted phase spaces; and the brain performs computations on these representations by means of coordinate transformations from one phase space to another. This scheme may be implemented in the brain in two distinct forms: (1) as a "phase-space sandwich", which may explain certain laminar structures, such as cerebral cortex and the superior colliculus; and (2) as a "neural matrix", which may explain other structures, such as the beautifully orthogonal architecture of the cerebellum.On the sublime question of how the brain works, recent science has been badly schizophrenic. Computational theorists have pursued abstract computational stories, and neuroscientists have pursued cellular connections and neural activity. But rarely has the work of either informed the work of the other. This is unfortunate. Experimental neurobiology needs increasingly to be guided by the selective and organizing vision of large-scale computational theories; and these latter need desperately to address, and to be informed by, the marvellous structural detail that neuroanatomists and neurophysiologists have brought to light.If computational theory and neurobiological experiment can be brought into systematic interaction, then we may expect progress in both fields to accelerate sharply. The aim of this paper is to take some substantive steps towards realizing such interaction. In what follows I shall elaborate a general scheme of representation and computation that (a) accounts in a unified way for a significant class of cognitive activities; and (b) finds explicit biological structures in the brain that implement such representational and computational activity.Given a general scheme that meets both conditions, structural features of the brain can suggest computational undertakings that went unnoticed Biology and Philosophy 1 (1986) 25-51.

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The Superior Colliculus and Visual Function

Cited by 32 publications

References 250 publications

Learning and Maintaining Saccadic Accuracy: A Model of Brainstem–Cerebellar Interactions

Learning and Maintaining Saccadic Accuracy: A Model of Brainstem–Cerebellar Interactions

Representation of lateral line and electrosensory systems in the midbrain of the axolotl, Ambystoma mexicanum

Cognitive neurobiology: a computational hypothesis for laminar cortex

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