ArticleFrontiers in computational neuroscience2017
Bayesian Estimation of Phase Dynamics Based on Partially Sampled Spikes Generated by Realistic Model Neurons.
Article in Frontiers in computational neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- An extended Hilbert transform method for reconstructing the phase from an oscillatory signal.Scientific reports · 2023Article
- Coupling between Blood Pressure and Subarachnoid Space Width Oscillations during Slow Breathing.Entropy (Basel, Switzerland) · 2021Article
- Interaction mechanisms quantified from dynamical features of frog choruses.Royal Society open science · 2020Article
- Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2019Article
- A practical method for estimating coupling functions in complex dynamical systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2019Article
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3 authors.
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Abstract
A dynamic system showing stable rhythmic activity can be represented by the dynamics of phase oscillators. This would provide a useful mathematical framework through which one can understand the system's dynamic properties. A recent study proposed a Bayesian approach capable of extracting the underlying phase dynamics directly from time-series data of a system showing rhythmic activity. Here we extended this method to spike data that otherwise provide only limited phase information. To determine how this method performs with spike data, we applied it to simulated spike data generated by a realistic neuronal network model. We then compared the estimated dynamics obtained based on the spike data with the dynamics theoretically derived from the model. The method successfully extracted the modeled phase dynamics, particularly the interaction function, when the amount of available data was sufficiently large. Furthermore, the method was able to infer synaptic connections based on the estimated interaction function. Thus, the method was found to be applicable to spike data and practical for understanding the dynamic properties of rhythmic neural systems.
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