Realistic modeling of large-scale networks: Spatio-temporal dynamics and long-term synaptic plasticity in the cerebellum

Egidio D'Angelo, Sergio Solinas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A large-scale computational model of the cerebellum granular layer has been adapted to generate long-term synaptic plasticity in response to afferent mossy fiber bursts. A simple learning rule was elaborated in order to link the average granule cell depolarization to LTP and LTD. Briefly, LTP was generated for membrane potentials >-40 mV and LTD for membrane potentials

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages547-553
Number of pages7
Volume6691 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2011
Event11th International Work-Conference on on Artificial Neural Networks, IWANN 2011 - Torremolinos-Malaga, Spain
Duration: Jun 8 2011Jun 10 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6691 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th International Work-Conference on on Artificial Neural Networks, IWANN 2011
CountrySpain
CityTorremolinos-Malaga
Period6/8/116/10/11

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Keywords

  • cerebellum
  • granule cells
  • LTD
  • LTP
  • modeling
  • NEURON

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

D'Angelo, E., & Solinas, S. (2011). Realistic modeling of large-scale networks: Spatio-temporal dynamics and long-term synaptic plasticity in the cerebellum. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 6691 LNCS, pp. 547-553). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6691 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-21501-8_68