ArticleBrain sciences2019
Estimating the Parameters of Fitzhugh-Nagumo Neurons from Neural Spiking Data.
Article in Brain sciences, 2019. 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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4 citing papers in PubMed.
- Parameter estimation of the network of FitzHugh-Nagumo neurons based on the speed-gradient and filtering.Journal of computational neuroscience · 2025Article
- Spiking Neuron Mathematical Models: A Compact Overview.Bioengineering (Basel, Switzerland) · 2023Review
- Unbiased estimation of the Hessian for partially observed diffusions.Proceedings. Mathematical, physical, and engineering sciences · 2022Article
- Multimodal parameter spaces of a complex multi-channel neuron model.Frontiers in systems neuroscience · 2022Article
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2 authors.
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Abstract
A theoretical and computational study on the estimation of the parameters of a single Fitzhugh-Nagumo model is presented. The difference of this work from a conventional system identification is that the measured data only consist of discrete and noisy neural spiking (spike times) data, which contain no amplitude information. The goal can be achieved by applying a maximum likelihood estimation approach where the likelihood function is derived from point process statistics. The firing rate of the neuron was assumed as a nonlinear map (logistic sigmoid) relating it to the membrane potential variable. The stimulus data were generated by a phased cosine Fourier series having fixed amplitude and frequency but a randomly shot phase (shot at each repeated trial). Various values of amplitude, stimulus component size, and sample size were applied to examine the effect of stimulus to the identification process. Results are presented in tabular and graphical forms, which also include statistical analysis (mean and standard deviation of the estimates). We also tested our model using realistic data from a previous research (H1 neurons of blowflies) and found that the estimates have a tendency to converge.
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