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Cerebellum as a neuronal machine: Behavioural electrophysiological and computational analysis of classical conditioning

ReferenceBBS/B/16984
Principal Investigator / Supervisor Professor Christopher Yeo
Co-Investigators /
Co-Supervisors
Institution University College London
DepartmentCell and Developmental Biology
Funding typeResearch
Value (£) 330,321
StatusCompleted
TypeResearch Grant
Start date 17/01/2005
End date 16/03/2008
Duration38 months

Abstract

The cerebellum has been likened to a neuronal machine making critical contributions to sensory-motor control, motor learning and cognition. Its cortical neurons are in a regularly repeating, geometrical array that suggests an information-processing algorithm consistent across every region. Classical conditioning of the eyeblink response is an excellent model of associative learning and recent work has defined cerebellar circuitry essential for its learning and expression. These advances provide an outstanding opportunity to analyse an identified neural network operating under natural conditions to develop a specified and accurately measurable learned behaviour. Three laboratories will work closely together to characterise the cerebellar algorithm using behavioural/pharmacological, multiple single-unit electrophysiology and computational methods. (Joint with BBS/B/1700X and BBS/B/17026).

Summary

unavailable
Committee Closed Committee - Animal Sciences (AS)
Research TopicsX – not assigned to a current Research Topic
Research PriorityX – Research Priority information not available
Research Initiative Integrative Analysis of Brain and Behaviour (IABB) [2003]
Funding SchemeX – not Funded via a specific Funding Scheme
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