Literature DB >> 16999572

The spike-triggered average of the integrate-and-fire cell driven by gaussian white noise.

Liam Paninski1.   

Abstract

We compute the exact spike-triggered average (STA) of the voltage for the nonleaky integrate-and-fire (IF) cell in continuous time, driven by gaussian white noise. The computation is based on techniques from the theory of renewal processes and continuous-time hidden Markov processes (e.g., the backward and forward Fokker-Planck partial differential equations associated with first-passage time densities). From the STA voltage, it is straightforward to derive the STA input current. The theory also gives an explicit asymptotic approximation for the STA of the leaky IF cell, valid in the low-noise regime sigma --> 0. We consider both the STA and the conditional average voltage given an observed spike "doublet" event, that is, two spikes separated by some fixed period of silence. In each case, we find that the STA as a function of time-preceding-spike, tau, has a square root singularity as tau approaches zero from below and scales linearly with the scale of injected noise current. We close by briefly examining the discrete-time case, where similar phenomena are observed.

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Year:  2006        PMID: 16999572     DOI: 10.1162/neco.2006.18.11.2592

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  10 in total

1.  Inferring synaptic inputs given a noisy voltage trace via sequential Monte Carlo methods.

Authors:  Liam Paninski; Michael Vidne; Brian DePasquale; Daniel Gil Ferreira
Journal:  J Comput Neurosci       Date:  2011-11-17       Impact factor: 1.621

2.  The most likely voltage path and large deviations approximations for integrate-and-fire neurons.

Authors:  Liam Paninski
Journal:  J Comput Neurosci       Date:  2006-04-22       Impact factor: 1.621

3.  The dynamical response properties of neocortical neurons to temporally modulated noisy inputs in vitro.

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Journal:  Cereb Cortex       Date:  2008-02-09       Impact factor: 5.357

4.  Relating neural dynamics to neural coding.

Authors:  G Bard Ermentrout; Roberto F Galán; Nathaniel N Urban
Journal:  Phys Rev Lett       Date:  2007-12-14       Impact factor: 9.161

5.  Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-space models.

Authors:  Shinsuke Koyama; Liam Paninski
Journal:  J Comput Neurosci       Date:  2009-04-28       Impact factor: 1.621

6.  Stimulus features, resetting curves, and the dependence on adaptation.

Authors:  Joseph G Arthur; Shawn D Burton; G Bard Ermentrout
Journal:  J Comput Neurosci       Date:  2012-11-30       Impact factor: 1.621

7.  From spiking neuron models to linear-nonlinear models.

Authors:  Srdjan Ostojic; Nicolas Brunel
Journal:  PLoS Comput Biol       Date:  2011-01-20       Impact factor: 4.475

8.  Spike-interval triggered averaging reveals a quasi-periodic spiking alternative for stochastic resonance in catfish electroreceptors.

Authors:  Martin J M Lankheet; P Christiaan Klink; Bart G Borghuis; André J Noest
Journal:  PLoS One       Date:  2012-03-05       Impact factor: 3.240

9.  Higher-order spike triggered analysis of neural oscillators.

Authors:  Keisuke Ota; Toshiaki Omori; Hiroyoshi Miyakawa; Masato Okada; Toru Aonishi
Journal:  PLoS One       Date:  2012-11-30       Impact factor: 3.240

10.  Ephaptic entrainment in hybrid neuronal model.

Authors:  Gabriel Moreno Cunha; Gilberto Corso; José Garcia Vivas Miranda; Gustavo Zampier Dos Santos Lima
Journal:  Sci Rep       Date:  2022-01-31       Impact factor: 4.996

  10 in total

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