ArticleComputational intelligence and neuroscience2010
State-space algorithms for estimating spike rate functions.
Article in Computational intelligence and neuroscience, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- Monitoring surgical nociception using multisensor physiological models.Proceedings of the National Academy of Sciences of the United States of America · 2024Observational
- Nonlinear model predictive control of a conductance-based neuron model via data-driven forecasting.Journal of neural engineering · 2024Article
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- Multifunctional fibers enable modulation of cortical and deep brain activity during cognitive behavior in macaques.Science advances · 2023Article
- The contributions of entorhinal cortex and hippocampus to error driven learning.Communications biology · 2021Article
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- Deciphering neuronal population codes for acute thermal pain.Journal of neural engineering · 2017Article
- Estimation of the synaptic input firing rates and characterization of the stimulation effects in an auditory neuron.Frontiers in computational neuroscience · 2015Article
- A brain-machine interface for control of medically-induced coma.PLoS computational biology · 2013Article
- Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression.Journal of neural engineering · 2013Article
- An overview of Bayesian methods for neural spike train analysis.Computational intelligence and neuroscience · 2013Review
- Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness.Proceedings of the National Academy of Sciences of the United States of America · 2012Article
- Neural population partitioning and a concurrent brain-machine interface for sequential motor function.Nature neuroscience · 2012Article
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6 authors.
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Abstract
The accurate characterization of spike firing rates including the determination of when changes in activity occur is a fundamental issue in the analysis of neurophysiological data. Here we describe a state-space model for estimating the spike rate function that provides a maximum likelihood estimate of the spike rate, model goodness-of-fit assessments, as well as confidence intervals for the spike rate function and any other associated quantities of interest. Using simulated spike data, we first compare the performance of the state-space approach with that of Bayesian adaptive regression splines (BARS) and a simple cubic spline smoothing algorithm. We show that the state-space model is computationally efficient and comparable with other spline approaches. Our results suggest both a theoretically sound and practical approach for estimating spike rate functions that is applicable to a wide range of neurophysiological data.
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