ArticleJournal of mathematical biology2012
Finite volume and asymptotic methods for stochastic neuron models with correlated inputs.
Article in Journal of mathematical biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A Diffusion Approximation and Numerical Methods for Adaptive Neuron Models with Stochastic Inputs.Frontiers in computational neuroscience · 2016Article
- Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics.PLoS computational biology · 2015Article
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5 authors.
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Abstract
We consider a pair of stochastic integrate and fire neurons receiving correlated stochastic inputs. The evolution of this system can be described by the corresponding Fokker-Planck equation with non-trivial boundary conditions resulting from the refractory period and firing threshold. We propose a finite volume method that is orders of magnitude faster than the Monte Carlo methods traditionally used to model such systems. The resulting numerical approximations are proved to be accurate, nonnegative and integrate to 1. We also approximate the transient evolution of the system using an Ornstein-Uhlenbeck process, and use the result to examine the properties of the joint output of cell pairs. The results suggests that the joint output of a cell pair is most sensitive to changes in input variance, and less sensitive to changes in input mean and correlation.
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