Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloGeneral differential Hebbian learning: Capturing temporal relations between events in neural networks and the brain
Anno di pubblicazione2018
Formato-
Autore/iZappacosta, Stefano; Mannella, Francesco; Mirolli, Marco; Baldassarre, Gianluca
Affiliazioni autoriCNR
Autori CNR e affiliazioni
  • GIANLUCA BALDASSARRE
  • MARCO MIROLLI
  • FRANCESCO MANNELLA
Lingua/e
  • inglese
AbstractLearning 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 postsynaptic 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 of artificial neurons and by fitting several different STDP experimental data sets. To these purposes, we propose techniques to pre-process the neural signals and capture the temporal relations between the neural events of interest. We also propose a procedure to automatically identify the rule components and parameters that best fit different STDP data sets, and show how the identified components might be used to heuristically guide the search of the biophysical mechanisms underlying STDP. Overall, the results show that the G-DHL rule represents a useful means to study time-sensitive learning processes in both artificial neural networks and brain.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da-
Pagine a-
Pagine totali30
RivistaPLOS computational biology (Online)
Attiva dal 2005
Editore: Public Library of Science, - San Francisco, CA
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1553-7358
Titolo chiave: PLOS computational biology (Online)
Titolo proprio: PLoS computational biology (Online)
Titolo abbreviato: PLOS comput. biol. (Online)
Titoli alternativi:
  • Public Library of Science computational biology (Online)
  • Computational biology (Online)
Numero volume della rivista14
Fascicolo della rivista8
DOI10.1371/journal.pcbi.1006227
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000443298500007)
Parole chiaveneural networks, Hebbian learning
Link (URL, URI)https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006227
Titolo parallelo-
LicenzaCC-BY-4.0
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • ISTC — Istituto di scienze e tecnologie della cognizione
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
General differential Hebbian learning: Capturing temporal relations between events in neural networks and the brain
Descrizione: Plos Computational Biology, Article
Tipo documento: application/pdf