Fast and accurate low-dimensional reduction of biophysically detailed neuron models (Articolo in rivista)

Type
Label
  • Fast and accurate low-dimensional reduction of biophysically detailed neuron models (Articolo in rivista) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1038/srep00928 (literal)
Alternative label
  • Marasco A, Limongiello A, Migliore M (2012)
    Fast and accurate low-dimensional reduction of biophysically detailed neuron models
    in Scientific reports (Nature Publishing Group); Nature Publishing Group, London (Regno Unito)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Marasco A, Limongiello A, Migliore M (literal)
Pagina inizio
  • art. 928 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.nature.com/srep/2012/121205/srep00928/full/srep00928.html (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 2 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR, Inst Biophys, I-90146 Palermo, Italy; Univ Naples Federico II, Dept Math & Applicat R Caccioppoli, I-80126 Naples, Italy (literal)
Titolo
  • Fast and accurate low-dimensional reduction of biophysically detailed neuron models (literal)
Abstract
  • Realistic modeling of neurons are quite successful in complementing traditional experimental techniques. However, their networks require a computational power beyond the capabilities of current supercomputers, and the methods used so far to reduce their complexity do not take into account the key features of the cells nor critical physiological properties. Here we introduce a new, automatic and fast method to map realistic neurons into equivalent reduced models running up to > 40 times faster while maintaining a very high accuracy of the membrane potential dynamics during synaptic inputs, and a direct link with experimental observables. The mapping of arbitrary sets of synaptic inputs, without additional fine tuning, would also allow the convenient and efficient implementation of a new generation of large-scale simulations of brain regions reproducing the biological variability observed in real neurons, with unprecedented advances to understand higher brain functions. (literal)
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