ArticleJournal of mathematical neuroscience2014
Fokker-Planck and Fortet Equation-Based Parameter Estimation for a Leaky Integrate-and-Fire Model with Sinusoidal and Stochastic Forcing.
Article in Journal of mathematical neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Autonomous learning of nonlocal stochastic neuron dynamics.Cognitive neurodynamics · 2022Article
- Neural decoding with visual attention using sequential Monte Carlo for leaky integrate-and-fire neurons.PloS one · 2019Article
- How does transient signaling input affect the spike timing of postsynaptic neuron near the threshold regime: an analytical study.Journal of computational neuroscience · 2018Article
- Responses of Leaky Integrate-and-Fire Neurons to a Plurality of Stimuli in Their Receptive Fields.Journal of mathematical neuroscience · 2016Article
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Authors and funding
3 authors.
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Abstract
Analysis of sinusoidal noisy leaky integrate-and-fire models and comparison with experimental data are important to understand the neural code and neural synchronization and rhythms. In this paper, we propose two methods to estimate input parameters using interspike interval data only. One is based on numerical solutions of the Fokker-Planck equation, and the other is based on an integral equation, which is fulfilled by the interspike interval probability density. This generalizes previous methods tailored to stationary data to the case of time-dependent input. The main contribution is a binning method to circumvent the problems of nonstationarity, and an easy-to-implement initializer for the numerical procedures. The methods are compared on simulated data. LIST OF ABBREVIATIONS LIF: Leaky integrate-and-fireISI: Interspike intervalSDE: Stochastic differential equationPDE: Partial differential equation.
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