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Lubica Benuskova, Department of Computer Science


How neurons "learn": insights from computational models


Archway 2 - 1:00 pm, Friday 13 March


All of our cognitive functions, including our sense of identity, are underpinned by what we have learned and what we can remember. To understand learning and memory formation in the brain, it is necessary to discover the rules according to which neurons change their mutual connections, i.e. the rules of synaptic plasticity. I will demonstrate the predictive power of computational modelling by presenting a novel model of heterosynaptic plasicity in the hippocampal dentate gyrus. This model combines computational properties of both metaplasticity and spike timing-dependent plasticity. As a result it can reproduce:

  1. homosynaptic long-term potentiation of the tetanized input,
  2. heterosynaptic long-term depression of the untetanized input, and
  3. dependence of heterosynaptic plasticity upon spontaneous activity,

as observed in real experiments.

Last modified: Tuesday, 03-Mar-2009 10:58:22 NZDT

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