http://www.cnr.it/ontology/cnr/individuo/prodotto/ID144144
Dynamics of networks of leaky-integrate and fire neurons (Contributo in volume (capitolo o saggio))
- Type
- Label
- Dynamics of networks of leaky-integrate and fire neurons (Contributo in volume (capitolo o saggio)) (literal)
- Anno
- 2010-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1007/978-1-84996-396-1_11 (literal)
- Alternative label
Antonio Politi (1,2); Stefano Luccioli (1,2,3) (2010)
Dynamics of networks of leaky-integrate and fire neurons
in Network Science : Complexity in Nature and Technology, 2010
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Antonio Politi (1,2); Stefano Luccioli (1,2,3) (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#citta
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://www.springerlink.com/content/g80687wk4h63716h/ (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
- Network Science : Complexity in Nature and Technology (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
- In: Network Science : Complexity in Nature and Technology. pp. 217 - 242. E. Estrada, M. Fox, D.J. Higham, G.-L. Oppo (eds.). London: Springer-Verlag, 2010. (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
- The dynamics of pulse-coupled leaky-integrate-and-fire neurons is dis- cussed in networks with arbitrary structure and in the presence of delayed inter- actions. The evolution equations are formally recasted as an event-driven map in a general context, where the pulses are assumed to have a finite width. The final structure of the mathematical model is simple enough to allow for an easy imple- mentation of standard nonlinear dynamics tools. We also discuss the properties of the transient dynamics in the presence of quenched disorder (and _-like pulses). We find that the length of the transient depends strongly on the number N of neurons. It can be as long as 106-107 inter-spike intervals for relatively small networks, but it decreases upon increasing N because of the presence of stable clustered states. Finally, we discuss the same problem in the presence of randomly uctuating synap- tic connections (annealed disorder). The stationary state turns out to be strongly affected by finite-s (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- (1) Istituto dei Sistemi Complessi, Consiglio Nazionale delle Ricerche, via Madonna del Piano 10, I-50019 Sesto Fiorentino, Italy
(2) Centro Studi Dinamiche Complesse, via Sansone 1, I-50019 Sesto Fiorentino, Italy
(3) Istituto Nazionale di Fisica Nucleare, Sez. Firenze, via Sansone 1, I-50019 Sesto Fiorentino, Italy (literal)
- Titolo
- Dynamics of networks of leaky-integrate and fire neurons (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#inCollana
- Network Science : Complexity in Nature and Technology (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- 978-1-84996-395-4 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
- E. Estrada, M. Fox, D.J. Higham, G.-L. Oppo (eds.) (literal)
- Abstract
- The dynamics of pulse-coupled leaky-integrate-and-fire neurons is dis- cussed in networks with arbitrary structure and in the presence of delayed inter- actions. The evolution equations are formally recasted as an event-driven map in a general context, where the pulses are assumed to have a finite width. The final structure of the mathematical model is simple enough to allow for an easy imple- mentation of standard nonlinear dynamics tools. We also discuss the properties of the transient dynamics in the presence of quenched disorder (and _-like pulses). We find that the length of the transient depends strongly on the number N of neurons. It can be as long as 106-107 inter-spike intervals for relatively small networks, but it decreases upon increasing N because of the presence of stable clustered states. Finally, we discuss the same problem in the presence of randomly uctuating synap- tic connections (annealed disorder). The stationary state turns out to be strongly affected by finite-size corrections, to the extent that the number of clusters depends on the network size even for N 20, 000. (literal)
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