Stabilising Hebbian learning with a third factor in a food retrieval task

Thompson, A.M., Porr, B. and Worgotter, F. (2006) Stabilising Hebbian learning with a third factor in a food retrieval task. Lecture Notes in Computer Science, 2006, pp. 313-322. (doi: 10.1007/11840541_26)

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When neurons fire together they wire together. This is Donald Hebb's famous postulate. However, Hebbian learning is inherently unstable because synaptic weights will self amplify themselves: the more a synapse is able to drive a postsynaptic cell the more the synaptic weight will grow. We present a new biologically realistic way how to stabilise synaptic weights by introducing a third factor which switches on or off learning so that self amplification is minimised. The third factor can be identified by the activity of dopaminergic neurons in VTA which fire when a reward has been encountered. This leads to a new interpretation of the dopamine signal which goes beyond the classical prediction error hypothesis. The model is tested by a real world task where a robot has to find "food disks" in an environment.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Porr, Dr Bernd
Authors: Thompson, A.M., Porr, B., and Worgotter, F.
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Q Science > QH Natural history > QH301 Biology
College/School:College of Science and Engineering > School of Engineering > Biomedical Engineering
Journal Name:Lecture Notes in Computer Science
ISSN (Online):1611-3349
Published Online:26 September 2006

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