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

2 Citations (Scopus)

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

Fingerprint

Cerebellum
Membrane Potential
Plasticity
Membranes
Depolarization
Rule Learning
Burst
Modeling
Computational Model
Fiber
Fibers
Cell

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

Realistic modeling of large-scale networks : Spatio-temporal dynamics and long-term synaptic plasticity in the cerebellum. / D'Angelo, Egidio; Solinas, Sergio.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6691 LNCS PART 1. ed. 2011. p. 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).

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

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 edn, vol. 6691 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6691 LNCS, pp. 547-553, 11th International Work-Conference on on Artificial Neural Networks, IWANN 2011, Torremolinos-Malaga, Spain, 6/8/11. https://doi.org/10.1007/978-3-642-21501-8_68
D'Angelo E, Solinas S. 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. 2011. p. 547-553. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-21501-8_68
D'Angelo, Egidio ; Solinas, Sergio. / Realistic modeling of large-scale networks : Spatio-temporal dynamics and long-term synaptic plasticity in the cerebellum. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6691 LNCS PART 1. ed. 2011. pp. 547-553 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
@inproceedings{c655a9fe30134a14951a94d059ce3328,
title = "Realistic modeling of large-scale networks: Spatio-temporal dynamics and long-term synaptic plasticity in the cerebellum",
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",
keywords = "cerebellum, granule cells, LTD, LTP, modeling, NEURON",
author = "Egidio D'Angelo and Sergio Solinas",
year = "2011",
doi = "10.1007/978-3-642-21501-8_68",
language = "English",
isbn = "9783642215001",
volume = "6691 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "547--553",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 1",

}

TY - GEN

T1 - Realistic modeling of large-scale networks

T2 - Spatio-temporal dynamics and long-term synaptic plasticity in the cerebellum

AU - D'Angelo, Egidio

AU - Solinas, Sergio

PY - 2011

Y1 - 2011

N2 - 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

AB - 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

KW - cerebellum

KW - granule cells

KW - LTD

KW - LTP

KW - modeling

KW - NEURON

UR - http://www.scopus.com/inward/record.url?scp=79957963002&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79957963002&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-21501-8_68

DO - 10.1007/978-3-642-21501-8_68

M3 - Conference contribution

SN - 9783642215001

VL - 6691 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 547

EP - 553

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -