Possible visual pathways to the cat vestibular nuclei involving the nucleus prepositus hypoglossi

Magnin, M.; Courjon, J; Flandrin, J. M.

doi:10.1007/bf00237206

Cited by 62 publications

(16 citation statements)

References 23 publications

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“…Visual input to the VNC arises from a subcortical pathway which includes the accessory optic tract, the medullary reticular formation, and prepositus hypoglossi (e.g., Magnin et al, 1983); however, visual information also converges on the VNC via the cerebellar flocculus and inferior olive, and a diversity of other sensory information (e.g., proprioceptive and somatosensory input from the spinal cord) is also integrated within the VNC (e.g., Waespe et al, 1983). In addition to these inputs, many VNC neurons also receive disynaptic or monosynaptic input from the contralateral VNC via the brainstem commissures, and the functional effect of this input is usually inhibition; therefore, there exists a lateral inhibitory network between the bilateral VNC which enhances the dynamic sensitivity of the response of VNC neurons to head movement (e.g., Shimazu and Precht, 1966;Nakao et al, 1982).…”

Section: The Vestibular Systemmentioning

confidence: 99%

Vestibular-hippocampal interactions

Smith

1997

Hippocampus

140

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Section: The Vestibular Systemmentioning

confidence: 99%

Vestibular-hippocampal interactions

Smith

1997

Hippocampus

140

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“…A similar model has been suggested for the floccular control of the optokinetic eye movement response (OKR). The nucleus reticularis tegmenti pointis mediates visual signals to the flocculus via mossy fibers [33] and at the same time relays them to vestibular nuclei, either directly [2] or indirectly [34]. The H-zone of the flocculus thus forms sidepaths to both the VOR and OKR brain stem pathway, and acts to modify either the VOR or OKR by referring to visual climbing fiber signals.…”

Section: Roles Of the Cerebellar Neuronal Networkmentioning

confidence: 99%

The Modifiable Neuronal Network of the Cerebellum

Ito

1984

Jpn.J.Physiol

124

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An outstanding feature of the cerebellum known since the classic histological work of Ramon y Cajal is the relative simplicity and highly ordered geometry of its neuronal network. The cerebellar neuronal network consists of five major types of cortical neurons (Purkinje, basket, stellate, Golgi, and granule cells), two major types of afferents (mossy and climbing fibers), and the subcortical cells in the vestibular and cerebellar nuclei. When the neuronal network structure of the cerebellum was comprehensively dissected and described in great detail in the 1960s, and summarized by ECCLES, ITO, and SZ.ENTAGOTHAI [8], it was taken as a challenge to understand more completely the complex functions of the central nervous system using these fundamental studies of the brain as groundwork for future elucidation. As the gap between our understanding of the brain and our knowledge of neuronal networks is in general so large, hope has arisen that cerebellar physiology may successfully fill the gap rather soon. Rigorous efforts using numerous newly-introduced techniques have been devoted during the past two decades, yielding remarkable progress that meets expectations at least to some extent. A number of problems remain to be solved, however, before we fully understand the cerebellum. It seems important at this point to review the major achievements of the past two decades in order to gain a clearer perspective of cerebellar physiology for the coming decade. I have recently written a monograph entitled "The Cerebellum and Neural Control" [23] in which I have taken this point of view. This article has a similar aim, but its theme is specifically centered around the plasticity of the cerebellar neuronal network and its function in cerebellar control mechanisms. A. STRUCTURES OF THE CEREBELLAR NEURONAL NETWORKDissection of structures in the cerebellar neuronal network has advanced greatly in the 1970s with the aid of numerous new experimental techniques and materials, such as immunohistochemistry, marker techniques utilizing axonal flow, high voltage electronmicroscopy, intracellular dye injection, in vitro preparations, mutant animals, tissue cultures, etc. The success in identifying the

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“…The algorithm specifies that the change in input-to-hidden weights is mainly proportional to the error signal (retinal slip) and current input-unit (canal afferent) firing rate (ibid.). The retinal slip signal can be found in the accessory optic system and cerebellum-the latter is known to send a heavy, direct projection to the vestibular nuclei (Wilson and Melvill Jones 1979) and recent anatomical studies in the cat suggest that an indirect projection from the former also exists (Magnin et al 1983). Also, the ability of retinal slip to produce plastic changes in the operation of the VOR is well documented (Wilson and MelviU Jones 1979).…”

Section: Neurophysiological Validitymentioning

confidence: 90%

Distributed parallel processing in the vertical vestibulo-ocular reflex: Learning networks compared to tensor theory

Anastasio

Robinson

1990

Biol. Cybern.

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The vestibulo-ocular reflex (VOR) is capable of producing compensatory eye movements in three dimensions. It utilizes the head rotational velocity signals from the semicircular canals to control the contractions of the extraocular muscles. Since canal and muscle coordinate frames are not orthogonal and differ from one another, a sensorimotor transformation must be produced by the VOR neural network. Tensor theory has been used to construct a linear transformation that can model the three-dimensional behavior of the VOR. But tensor theory does not take the distributed, redundant nature of the VOR neural network into account. It suggests that the neurons subserving the VOR, such as vestibular nucleus neurons, should have specific sensitivity-vectors. Actual data, however, are not in accord. Data from the cat show that the sensitivity-vectors of vestibular nucleus neurons, rather than aligning with any specific vectors, are dispersed widely. As an alternative to tensor theory, we modeled the vertical VOR as a three-layered neural network programmed using the back-propagation learning algorithm. Units in mature networks had divergent sensitivity-vectors which resembled those of actual vestibular nucleus neurons in the cat. This similarity suggests that the VOR sensorimotor transformation may be represented redundantly rather than uniquely. The results demonstrate how vestibular nucleus neurons can encode the VOR sensorimotor transformation in a distributed manner.

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Possible visual pathways to the cat vestibular nuclei involving the nucleus prepositus hypoglossi

Cited by 62 publications

References 23 publications

Vestibular-hippocampal interactions

Vestibular-hippocampal interactions

The Modifiable Neuronal Network of the Cerebellum

Distributed parallel processing in the vertical vestibulo-ocular reflex: Learning networks compared to tensor theory

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