ReviewComputational intelligence and neuroscience2013
An overview of Bayesian methods for neural spike train analysis.
Review in Computational intelligence and neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Drift-diffusion dynamics of hippocampal replay.PLoS computational biology · 2026Article
- A sticky Poisson Hidden Markov Model for solving the problem of over-segmentation and rapid state switching in cortical datasets.bioRxiv : the preprint server for biology · 2025Article
- A sticky Poisson Hidden Markov Model for solving the problem of over-segmentation and rapid state switching in cortical datasets.PloS one · 2025Article
- Event detection and classification from multimodal time series with application to neural data.Journal of neural engineering · 2024Article
- Calibrating Bayesian Decoders of Neural Spiking Activity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- An in silico model for determining the influence of neuronal co-activity on rodent spatial behavior.Journal of neuroscience methods · 2022Article
- Metastable dynamics of neural circuits and networks.Applied physics reviews · 2022Review
- Multimodal parameter spaces of a complex multi-channel neuron model.Frontiers in systems neuroscience · 2022Article
- Prediction of the Compressive Strength of Recycled Aggregate Concrete Based on Artificial Neural Network.Materials (Basel, Switzerland) · 2021Article
- Slower prefrontal metastable dynamics during deliberation predicts error trials in a distance discrimination task.Cell reports · 2021Article
- Upper Limit on the Thermodynamic Information Content of an Action Potential.Frontiers in computational neuroscience · 2020Article
- Deciphering neuronal population codes for acute thermal pain.Journal of neural engineering · 2017Article
- A hidden Markov model for decoding and the analysis of replay in spike trains.Journal of computational neuroscience · 2016Article
- From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining.Journal of physiology, Paris · 2016Review
- The Cluster Variation Method: A Primer for Neuroscientists.Brain sciences · 2016Article
- A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation.Journal of neuroscience methods · 2016Article
- Comparing offline decoding performance in physiologically defined neuronal classes.Journal of neural engineering · 2016Article
- Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks.Computational intelligence and neuroscience · 2016Article
- Article
- Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM.Computational intelligence and neuroscience · 2015Article
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Abstract
Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.
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