Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Sparse component analysis: A method that uncovers separable computations within neural population activity.

Neuron·2026

Same author

State-switching navigation strategies in <i>Caenorhabditis elegans</i> are beneficial for chemotaxis.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Beast3D: Animal behavioral analysis and neural encoding from multi-view video via Gaussian splatting.

ArXiv·2026

Same author

Lightning Pose 3D: an uncertainty-aware framework for data-efficient multi-view animal pose estimation.

bioRxiv : the preprint server for biology·2026

Same author

Computational optimization of two-photon holographic stimulation sites<i>in vivo</i>.

Journal of neural engineering·2026

Same author

Animal behavioral analysis and neural encoding with transformer-based self-supervised pretraining.

ArXiv·2026

Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026

Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026

Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026

Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026

Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026

Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 7, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Efficient Markov chain Monte Carlo methods for decoding neural spike trains.

Yashar Ahmadian¹, Jonathan W Pillow, Liam Paninski

¹Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, New York 10027, USA. yashar@stat.columbia.edu

Neural Computation

|October 23, 2010

Summary

This summary is machine-generated.

Bayesian decoding methods using generalized linear models (GLMs) can estimate neural stimuli. Markov chain Monte Carlo (MCMC) algorithms improve accuracy, especially with non-Gaussian priors, offering better stimulus reconstruction than maximum a posteriori (MAP) estimates.

More Related Videos

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

Related Experiment Videos

Last Updated: Jun 7, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

Area of Science:

Computational Neuroscience
Machine Learning for Neuroscience
Neural Decoding

Background:

Understanding neural representations of sensory and motor information is crucial.
Bayesian decoding, particularly using generalized linear models (GLMs), is a key tool.
Maximum a posteriori (MAP) estimation is efficient but can be inaccurate for non-Gaussian posteriors.

Purpose of the Study:

To compare Markov chain Monte Carlo (MCMC) algorithms for Bayesian stimulus decoding.
To evaluate the performance of different MCMC methods for various prior distributions.
To assess the utility of MCMC for calculating posterior expectations and mutual information.

Main Methods:

Encoding generalized linear model (GLM) for neural spike train generation.
Implementation and comparison of various MCMC algorithms (Hybrid Monte Carlo, Hit-and-Run).
Calculation of posterior means and mutual information using MCMC.

Main Results:

Hybrid Monte Carlo (HMC) excels with Gaussian priors.
Hit-and-Run algorithm outperforms others for non-Gaussian priors with sharp features.
Posterior mean estimates show lower average error than MAP for non-Gaussian priors.
MCMC validates Laplace approximation for mutual information, enabling scalable analysis.

Conclusions:

MCMC methods offer significant improvements in stimulus decoding accuracy over MAP, particularly for non-Gaussian priors.
Algorithm choice (HMC vs. Hit-and-Run) depends on prior distribution characteristics.
MCMC-based mutual information calculation is a tractable and powerful tool for neural decoding.