HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

An integrate-and-fire model to generate spike trains with long-range dependence

Abstract : Long-range dependence (LRD) has been observed in a variety of phenomena in nature, and for several years also in the spiking activity of neurons. Often, this is interpreted as originating from a non-Markovian system. Here we show that a purely Markovian integrate-and-fire (IF) model, with a noisy slow adaptation term, can generate interspike intervals (ISIs) that appear as having LRD. However a proper analysis shows that this is not the case asymptotically. For comparison, we also consider a new model of individual IF neuron with fractional (non-Markovian) noise. The correlations of its spike trains are studied and proven to have LRD, unlike classical IF models. On the other hand, to correctly measure long-range dependence, it is usually necessary to know if the data are stationary. Thus, a methodology to evaluate stationarity of the ISIs is presented and applied to the various IF models. We explain that Markovian IF models may seem to have LRD because of non-stationarities.
Document type :
Journal articles
Complete list of metadata

Contributor : Etienne Tanré Connect in order to contact the contributor
Submitted on : Wednesday, March 28, 2018 - 9:03:52 AM
Last modification on : Wednesday, February 2, 2022 - 3:55:07 PM
Long-term archiving on: : Thursday, September 13, 2018 - 9:38:35 AM


Files produced by the author(s)



Alexandre Richard, Patricio Orio, Etienne Tanré. An integrate-and-fire model to generate spike trains with long-range dependence. Journal of Computational Neuroscience, Springer Verlag, 2018, 44 (3), pp.297-312. ⟨10.1007/s10827-018-0680-1⟩. ⟨hal-01521891v2⟩



Record views


Files downloads