- Poster presentation
- Open Access
Should Hebbian learning be selective for negative excess kurtosis?
© Gros et al. 2015
- Published: 18 December 2015
- Firing Rate
- Independent Component Analysis
- Synaptic Strength
- Hebbian Learning
- Input Distribution
Within the Hebbian learning paradigm, synaptic plasticity results in potentiation whenever pre- and postsynaptic activities are correlated, and in depression otherwise. This requirement is however not sufficient to determine the precise functional form for Hebbian learning, and a range of distinct formulations have been proposed hitherto. They differ, in particular, in the way runaway synaptic growth is avoided; by either imposing a hard upper bound for the synaptic strength, overall synaptic scaling, or additive synaptic decay . Here we propose  a multiplicative Hebbian learning rule which is, at the same time, self-limiting and selective for negative excess kurtosis (for the case of symmetric input distributions).
The support of the German Science Foundation (DFG) and the German Academic Exchange Service (DAAD) are acknowledged.
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