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Abstract:
A correlation-based learning rule at the spike level is
formulated, mathematically analyzed, and compared to learning in a
firing-rate description. A differential equation for the learning
dynamics is derived under the assumption that the time scales of
learning and spiking can be separated. Using a linear Poissonian
neuron model which receives time-dependent stochastic input we show
that spike correlations on a millisecond time scale play indeed a
role under reasonable neurobiological conditions. It is shown that
correlations between input and output spikes tend to stabilize
structure formation, provided that the form of the learning window
is in accordance with Hebb's principle.
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