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Stefano Fusi Computational Neuroscience, Institute of Physiology, University of Bern, Switzerland |
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Wednesday 4 May
2005
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| 16:00 | |
| B10 Seminar Room Alexandra House, 17 Queen Square, LONDON, WC1N 3AR |
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| Learning on Multiple Time Scales: Theory and In Vivo Experiments
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Storing memories of ongoing, everyday experiences
requires a high degree of plasticity, but retaining these memories demands
protection against changes induced by further activity and experience.
Models in which memories are stored through switch-like transitions in
synaptic efficacy are good at storing but bad at retaining memories if these
transitions are likely, and poor at storage but good at retention if they
are unlikely. We construct and study a model in which each synapse has a
cascade of states with different levels of plasticity, connected by
metaplastic transitions. This cascade model combines high levels of memory
storage with long retention times and significantly outperforms alternative
models. As a result, we suggest that memory storage requires synapses with
multiple states exhibiting dynamics over a wide range of timescales. We then
analyze and model the behavioural and the neural data of the experiment
performed by W.F. Asaad, G. Rainer and E.K. Miller (Neuron, 1998) in which
two monkeys are trained to learn and forget visuo-motor associations.
Synaptic plasticity on multiple timescales is shown to be crucial to allow
the model to quickly forget the old cue-motor response associations, as well
as to capture the statistics of correct associations across many blocks of
trials. The cascade model has been developed with L.F. Abbott and P.J. Drew.
The model of the experimental data with X.-J. Wang, W.F. Asaad and E.K.
Miller.
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