ArticleJournal of neural engineering2011
A sequential Monte Carlo approach to estimate biophysical neural models from spikes.
Article in Journal of neural engineering, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Fast reconstruction of degenerate populations of conductance-based neuron models from spike times.PLoS computational biology · 2026Article
- A biophysical and statistical modeling paradigm for connecting neural physiology and function.Journal of computational neuroscience · 2023Article
- Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis-Hastings Method.Entropy (Basel, Switzerland) · 2022Article
- Capturing Multiple Timescales of Adaptation to Second-Order Statistics With Generalized Linear Models: Gain Scaling and Fractional Differentiation.Frontiers in systems neuroscience · 2020Article
- Article
- Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity.Journal of neurophysiology · 2018Article
- Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter.Neural computation · 2015Article
- Identifying and tracking simulated synaptic inputs from neuronal firing: insights from in vitro experiments.PLoS computational biology · 2015Article
- Segmental Bayesian estimation of gap-junctional and inhibitory conductance of inferior olive neurons from spike trains with complicated dynamics.Frontiers in computational neuroscience · 2015Article
- A unified approach to linking experimental, statistical and computational analysis of spike train data.PloS one · 2014Article
- Control strategies for underactuated neural ensembles driven by optogenetic stimulation.Frontiers in neural circuits · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Realistic computational models of neuronal activity typically involve many variables and parameters, most of which remain unknown or poorly constrained. Moreover, experimental observations of the neuronal system are typically limited to the times of action potentials, or spikes. One important component of developing a computational model is the optimal incorporation of these sparse experimental data. Here, we use point process statistical theory to develop a procedure for estimating parameters and hidden variables in neuronal computational models given only the observed spike times. We discuss the implementation of a sequential Monte Carlo method for this procedure and apply it to three simulated examples of neuronal spiking activity. We also address the issues of model identification and misspecification, and show that accurate estimates of model parameters and hidden variables are possible given only spike time data.
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