Literature DB >> 20366796

Correlations and synchrony in threshold neuron models.

Tatjana Tchumatchenko1, Aleksey Malyshev, Theo Geisel, Maxim Volgushev, Fred Wolf.   

Abstract

We study how threshold models and neocortical neurons transfer temporal and interneuronal input correlations to correlations of spikes. In both, we find that the low common input regime is governed by firing rate dependent spike correlations which are sensitive to the detailed structure of input correlation functions. In the high common input regime, the spike correlations are largely insensitive to the firing rate and exhibit a universal peak shape. We further show that pairs with different firing rates driven by common inputs in general exhibit asymmetric spike correlations.

Mesh:

Year:  2010        PMID: 20366796     DOI: 10.1103/PhysRevLett.104.058102

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  38 in total

1.  A-current and type I/type II transition determine collective spiking from common input.

Authors:  Andrea K Barreiro; Evan L Thilo; Eric Shea-Brown
Journal:  J Neurophysiol       Date:  2012-06-06       Impact factor: 2.714

2.  Single neuron firing properties impact correlation-based population coding.

Authors:  Sungho Hong; Stéphanie Ratté; Steven A Prescott; Erik De Schutter
Journal:  J Neurosci       Date:  2012-01-25       Impact factor: 6.167

3.  Correlation-distortion based identification of Linear-Nonlinear-Poisson models.

Authors:  Michael Krumin; Avner Shimron; Shy Shoham
Journal:  J Comput Neurosci       Date:  2009-09-15       Impact factor: 1.621

Review 4.  The mechanics of state-dependent neural correlations.

Authors:  Brent Doiron; Ashok Litwin-Kumar; Robert Rosenbaum; Gabriel K Ocker; Krešimir Josić
Journal:  Nat Neurosci       Date:  2016-03       Impact factor: 24.884

5.  Correlations in spiking neuronal networks with distance dependent connections.

Authors:  Birgit Kriener; Moritz Helias; Ad Aertsen; Stefan Rotter
Journal:  J Comput Neurosci       Date:  2009-07-01       Impact factor: 1.621

6.  Finite volume and asymptotic methods for stochastic neuron models with correlated inputs.

Authors:  Robert Rosenbaum; Fabien Marpeau; Jianfu Ma; Aditya Barua; Krešimir Josić
Journal:  J Math Biol       Date:  2011-06-30       Impact factor: 2.259

7.  Signatures of synchrony in pairwise count correlations.

Authors:  Tatjana Tchumatchenko; Theo Geisel; Maxim Volgushev; Fred Wolf
Journal:  Front Comput Neurosci       Date:  2010-04-08       Impact factor: 2.380

8.  Multivariate autoregressive modeling and granger causality analysis of multiple spike trains.

Authors:  Michael Krumin; Shy Shoham
Journal:  Comput Intell Neurosci       Date:  2010-04-29

9.  Correlation-based analysis and generation of multiple spike trains using hawkes models with an exogenous input.

Authors:  Michael Krumin; Inna Reutsky; Shy Shoham
Journal:  Front Comput Neurosci       Date:  2010-11-19       Impact factor: 2.380

10.  Modeling Population Spike Trains with Specified Time-Varying Spike Rates, Trial-to-Trial Variability, and Pairwise Signal and Noise Correlations.

Authors:  Dmitry R Lyamzin; Jakob H Macke; Nicholas A Lesica
Journal:  Front Comput Neurosci       Date:  2010-11-15       Impact factor: 2.380

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