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The neural basis of sequence learning: an empirical and computational assessment of hippocampal neuronal activity at choice points

Reference	BB/C516079/1
Principal Investigator / Supervisor	Dr Paul Dudchenko
Co-Investigators / Co-Supervisors	Professor James Ainge, Professor Florentin Woergoetter
Institution	University of Stirling
Department	Psychology
Funding type	Research
Value (£)	209,890
Status	Completed
Type	Research Grant
Start date	07/02/2005
End date	06/06/2007
Duration	28 months

Abstract

The proposed research will address the problem of how specific behaviours come to be associated with reward when a delay exists between the two. Our interest is in the neural bases of this learning, and we hypothesise that neurons in the hippocampus alter their firing properties when the animal is faced with a choice between a behaviour that is followed by reward and one that is not. We will test this in three ways. First, we will record hippocampal place cells as the rat performs a sequential choice task on a concatenated Y-maze apparatus. Our prediction is that place fields at the choice points that are decisive ie. where one choice will lead to reinforcement and the other will head to a dead end, will acquire conditional firing. In contrast, place fields at non-decisive choice points will exhibit traditional omni-directional activity. Our second experimental series will test whether the discriminada in a conditional visual discrimination task will serve as contextual cues for place fields at the choice point of a Y-maze. We predict that if the hippocampus encodes both spatial location and conditional stimuli, place fields will begin to encode the different visual stimuli as the animal acquires the task. If this representation underlies behaviour, changes in place fields should parallel the animals learning of the task. In addition, the visual discriminada may begin to drive place fields that occur just past the choice point, causing these fields to anticipate the rats choices. The final experimental series will integrate these empirical data with a model of place cell plasticity based on a three factor learning rule. In its second stage, this computational work will seek to develop a biophysically realistic model of hippocampal neuronal plasticity.

Summary

unavailable

Committee	Closed Committee - Animal Sciences (AS)
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	X – not Funded via a specific Funding Scheme

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