A novel stochastic Hebb-like learning rule for neural networks

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dc.creatorEmmert-Streib, Frank
dc.date2003-07-28
dc.date.accessioned2026-07-07T02:52:38Z
dc.date.available2026-07-07T02:52:38Z
dc.descriptionWe present a novel stochastic Hebb-like learning rule for neural networks. This learning rule is stochastic with respect to the selection of the time points when a synaptic modification is induced by pre- and postsynaptic activation. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron which is called homosynaptic plasticity but also on further remote synapses of the pre- and postsynaptic neuron. This form of plasticity has recently come into the light of interest of experimental investigations and is called heterosynaptic plasticity. Our learning rule gives a qualitative explanation of this kind of synaptic modification.
dc.description10 pages, 7 figures
dc.identifierhttps://arxiv.org/abs/cond-mat/0307666
dc.identifierhttp://arxiv.org/abs/cond-mat/0307666
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/21913
dc.subjectDisordered Systems and Neural Networks
dc.subjectQuantitative Biology
dc.titleA novel stochastic Hebb-like learning rule for neural networks
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