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

Poisson Probability Distribution01:09

Poisson Probability Distribution

12.3K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
12.3K
Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

4.5K
The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
4.5K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

309
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...
309
Associative Learning01:27

Associative Learning

1.7K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.7K
Observational Learning01:12

Observational Learning

1.2K
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
1.2K
Neural Circuits01:25

Neural Circuits

3.2K
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...
3.2K

You might also read

Related Articles

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

Sort by
Same author

Perinatal Arterial Ischemic Stroke Related to Internal Carotid Artery Occlusion: Two Case Reports.

Clinical case reports·2026
Same authorSame journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same author

Ganoderic Acid A Mitigates Sleep Deprivation-Induced Behavioral Deficits and Aging-like Phenotypes by Cryptochrome 1-Mediated Suppression of Ferroptosis and Pyroptosis.

Journal of agricultural and food chemistry·2026
Same author

Multiple Superconducting Phases in <i>m</i>-TaS<sub>3</sub> under Extreme Compression.

Journal of the American Chemical Society·2026
Same author

Pharmacological induction of mitochondria-lysosome hyper-tethering elicits synthetic lethality in glioblastoma.

Cancer letters·2026
Same author

Wavelet time-frequency analysis enhances noise-resilient quantification of corneal wave velocity and natural frequency in optical coherence elastography.

Biomedical optics express·2026

Related Experiment Video

Updated: Mar 18, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

12.1K

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

Yanping Huang1, Rajesh P N Rao2

  • 1Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, U.S.A. huangyp@cs.washington.edu.

Neural Computation
|June 28, 2016
PubMed
Summary

This study introduces a neural network model that performs Bayesian inference and learning for hidden Markov models. The network uses spiking variability for sampling, approximating Bayesian computation in the brain.

More Related Videos

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.2K

Related Experiment Videos

Last Updated: Mar 18, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

12.1K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.2K

Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Growing evidence suggests the brain performs Bayesian computations.
  • Understanding neural mechanisms for probabilistic inference is crucial.

Purpose of the Study:

  • To demonstrate a recurrent neural network capable of Bayesian inference and learning.
  • To model how neural networks can approximate Bayesian computation for hidden Markov models.

Main Methods:

  • Utilized a two-layer recurrent network of Poisson neurons.
  • Implemented synaptic plasticity and a Hebbian learning rule.
  • Modeled inference using spiking activity as samples from a posterior distribution.

Main Results:

  • The network successfully performed approximate Bayesian inference for hidden Markov models.
  • Demonstrated learning of likelihood and transition probabilities.
  • Spiking variability was integral to the sampling process for inference.

Conclusions:

  • Neural networks can implement Bayesian inference and learning mechanisms.
  • Spiking variability is a functional component of neural computation for probabilistic inference.
  • The model offers insights into neural computation and artificial intelligence.