A novel stochastic learning rule for neural networks

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

Abstrakti

The purpose of this article is the introduction of a novel stochastic Hebb-like learning rule for neural networks which combines features of unsupervised (Hebbian) and supervised (reinforcement) learning. This learning rule is stochastic with respect to the selection of the time points when a synaptic modification is induced by simultantious activation of the pre- and postsynaptic neuron. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron which is called homosynaptic plasticity but effects also further remote synapses of the pre- and postsynaptic neuron. This more complex form of plasticity has recently come into the light of interest of experimental investigations in neurobiology and is called heterosynaptic plasticity. Our learning rule is motivated by these experimental findings and gives a qualitative explanation of this kind of synaptic plasticity. Additionally, we give some numerical results that demonstrate that our learning rule works well in training neural networks, even in the presence of noise.

AlkuperäiskieliEnglanti
OtsikkoLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
KustantajaSpringer Verlag
Sivut414-423
Sivumäärä10
Vuosikerta3971 LNCS
ISBN (painettu)9783540344391
DOI - pysyväislinkit
TilaJulkaistu - 2006
Julkaistu ulkoisestiKyllä
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
Tapahtuma3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, Kiina
Kesto: 28 toukok. 20061 kesäk. 2006

Julkaisusarja

NimiLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vuosikerta3971 LNCS
ISSN (painettu)03029743
ISSN (elektroninen)16113349

Conference

Conference3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
Maa/AlueKiina
KaupunkiChengdu
Ajanjakso28/05/061/06/06

!!ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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