General differential Hebbian learning: Capturing temporal relations between events in neural networks and the brain.

ABSTRACT: Learning in biologically relevant neural-network models usually relies on Hebb learning rules. The typical implementations of these rules change the synaptic strength on the basis of the co-occurrence of the neural events taking place at a certain time in the pre- and post-synaptic neurons. Differential Hebbian learning (DHL) rules, instead, are able to update the synapse by taking into account the temporal relation, captured with derivatives, between the neural events happening in the recent past. The few DHL rules proposed so far can update the synaptic weights only in few ways: this is a limitation for the study of dynamical neurons and neural-network models. Moreover, empirical evidence on brain spike-timing-dependent plasticity (STDP) shows that different neurons express a surprisingl

SUBMITTER: Zappacosta S 

PROVIDER: S-EPMC6130884 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature