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

PLoS Computational Biology
Stefano ZappacostaGianluca Baldassarre

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 surprisingly rich repertoire of different learning processes going far beyond existing DHL rules. This opens up a second problem of how capturing such processes with DHL rules. Here we propose a general DHL (G-DHL) rule generating the existing rules and many others. The rule has a high expressiveness as it combines in different ways the pre- and post-synaptic neuron signals and derivatives. The rule flexibility is shown by applying it to various signals ...Continue Reading

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Citations

Dec 9, 2020·PLoS Computational Biology·Ruggero BasanisiGianluca Baldassarre
Sep 10, 2019·Neural Networks : the Official Journal of the International Neural Network Society·Daniel J SaundersRobert Kozma

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