Oral / Poster
G.010 | A comparative study of three different noise-driven integrate-and-fire neuron models | Autores: | Rafael Dias Vilela (UFABC - Universidade Federal do ABCMPIPKS - Max Planck Institute for the Physics of Complex Systems) ; Benjamin Lindner (MPIPKS - Max Planck Institute for the Physics of Complex Systems) |
Resumo Stochastic integrate-and-fire (IF) neuron models have found
widespread applications in computational neuroscience. I will report
results on the white-noise-driven perfect, leaky, and quadratic IF
models, focusing on the spectral statistics (power spectra, cross
spectra, and coherence functions) in different dynamical regimes
(noise-induced and tonic firing regimes with low or moderate noise).
We have made the models comparable by tuning parameters such that the
mean value and the coefficient of variation of the interspike interval
match for all of them (J.Theor.Biol. 257:90,2009). We have found that, under these conditions, the power spectrum under white-noise stimulation is often very similar, while the response characteristics, described by the cross spectrum
between a fraction of the input noise and the output spike train, can
differ drastically. We have also investigated how the spike trains of
two neurons of the same kind (e.g. two leaky IF neurons) correlate if
they share a common noise input. I will show that, depending on the
dynamical regime, either two quadratic IF models or two leaky IFs are
more strongly correlated. Our results suggest that, when choosing
among simple IF models for network simulations, the details of the
model have a strong effect on correlation and regularity of the
output (Phys.Rev.E 80:031909,2009). Palavras-chave: Integrate-and-fire neurons, Mathematical modelling, Noise |