ArticleFrontiers in computational neuroscience2016
A Diffusion Approximation and Numerical Methods for Adaptive Neuron Models with Stochastic Inputs.
Article in Frontiers in computational neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Learning and inference with correlated neural variability.PNAS nexus · 2025Article
- Model-agnostic neural mean field with a data-driven transfer function.Neuromorphic computing and engineering · 2024Article
- Model-Agnostic Neural Mean Field With The Refractory SoftPlus Transfer Function.bioRxiv : the preprint server for biology · 2024Article
- Autonomous learning of nonlocal stochastic neuron dynamics.Cognitive neurodynamics · 2022Article
- Balanced networks under spike-time dependent plasticity.PLoS computational biology · 2021Article
- Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation.PLoS computational biology · 2017Article
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Abstract
Characterizing the spiking statistics of neurons receiving noisy synaptic input is a central problem in computational neuroscience. Monte Carlo approaches to this problem are computationally expensive and often fail to provide mechanistic insight. Thus, the field has seen the development of mathematical and numerical approaches, often relying on a Fokker-Planck formalism. These approaches force a compromise between biological realism, accuracy and computational efficiency. In this article we develop an extension of existing diffusion approximations to more accurately approximate the response of neurons with adaptation currents and noisy synaptic currents. The implementation refines existing numerical schemes for solving the associated Fokker-Planck equations to improve computationally efficiency and accuracy. Computer code implementing the developed algorithms is made available to the public.
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