Computational Neuroscience Seminar - LCN
20.11.09 Friday, 12h15,
BC 01
Robert Legenstein, Institute for Theoretical Computer Science, TU Graz (Homepage)
A reward-modulated Hebbian learning rule can explain
experimentally observed network reorganization in a brain control task
Abstract:
It has recently been shown in a brain-computer interface experiment that
motor cortical neurons change their tuning properties selectively to
compensate for errors induced by displaced decoding parameters. In
particular, it was shown that the 3D tuning curves of neurons whose
decoding parameters were re-assigned changed more than those of neurons
whose decoding parameters had not been re-assigned. In this article, we
propose a simple learning rule that can reproduce this effect. Our
learning rule uses Hebbian weight updates driven by a global reward
signal and neuronal noise. In contrast to most previously proposed
learning rules, this approach does not require extrinsic information to
separate noise from signal. The learning rule is able to optimize the
performance of a model system within biologically realistic periods of
time under high noise levels. Furthermore, when the model parameters are
matched to data recorded during the brain-computer interface learning
experiments described above, the model produces learning effects
strikingly similar to those found in the experiments.
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