Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Probability in Statistics01:14

Probability in Statistics

Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
Probability Distributions01:32

Probability Distributions

The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...
Probability Histograms01:17

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

You might also read

Related Articles

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

Sort by
Same author

Focal thalamic infrared neural stimulation propagates dynamical transformations in auditory cortex.

Journal of neural engineering·2025
Same author

Self-Aligned Multilayered Nitrogen Vacancy Diamond Nanoparticles for High Spatial Resolution Magnetometry of Microelectronic Currents.

Nano letters·2025
Same author

Focal Infrared Neural Stimulation Propagates Dynamical Transformations in Auditory Cortex.

bioRxiv : the preprint server for biology·2025
Same author

Field-Programmable Gate Array-Based Ultra-Low Power Discrete Fourier Transforms for Closed-Loop Neural Sensing.

bioRxiv : the preprint server for biology·2025
Same author

Characterization and closed-loop control of infrared thalamocortical stimulation produces spatially constrained single-unit responses.

PNAS nexus·2024
Same author

Rapid and objective assessment of auditory temporal processing using dynamic amplitude-modulated stimuli.

bioRxiv : the preprint server for biology·2024

Related Experiment Video

Updated: Jun 16, 2026

Brain Banking: Making the Most of your Research Specimens
08:12

Brain Banking: Making the Most of your Research Specimens

Published on: July 24, 2009

10.0K

Practical Bayesian Inference in Neuroscience: Or How I Learned to Stop Worrying and Embrace the Distribution.

Brandon S Coventry1, Edward L Bartlett2

  • 1Department of Neurological Surgery and the Wisconsin Institute for Translational Neuroengineering, University of Wisconsin-Madison, Madison, Wisconsin 53705.

Eneuro
|June 25, 2024
PubMed
Summary

Bayesian inference offers an interpretable alternative to traditional statistical methods in biology, addressing replication issues. This approach, powered by computational advances, provides robust analysis for neuroscientific data.

Keywords:
Bayesian inferenceauditoryneural codingneural data analysisstatistical inference

More Related Videos

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.9K

Related Experiment Videos

Last Updated: Jun 16, 2026

Brain Banking: Making the Most of your Research Specimens
08:12

Brain Banking: Making the Most of your Research Specimens

Published on: July 24, 2009

10.0K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.9K

Area of Science:

  • Neuroscience
  • Biostatistics

Background:

  • Replication crisis in biological sciences challenges traditional null hypothesis significance testing (NHST).
  • p-values and NHST designs present interpretation difficulties.
  • Bayesian inference offers an alternative with clearer interpretation and explicit prior assumptions.

Purpose of the Study:

  • To provide a practical tutorial on applying Bayesian inference in neuroscience.
  • To demonstrate Bayesian regression and ANOVA models for neuroscientific data analysis.
  • To introduce an open-source toolbox for facilitating Bayesian analysis.

Main Methods:

  • Tutorial on Bayes' rule and Bayesian inference.
  • Formulation of Bayesian regression and ANOVA models.
  • Application to rat electrophysiological and computational modeling data.

Main Results:

  • Demonstration of easily interpretable data analysis using Bayesian inference.
  • Successful application of Bayesian models to neuroscientific datasets.
  • Availability of an open-source toolbox for practical implementation.

Conclusions:

  • Bayesian inference is a viable and interpretable alternative/complement to NHST in biological sciences.
  • Computational advancements enable complex Bayesian modeling for robust data analysis.
  • The presented tutorial and toolbox lower the barrier for adopting Bayesian methods in neuroscience research.