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

Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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...
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...
Introduction to Normal Distributions01:29

Introduction to Normal Distributions

Standardized test scores often follow a symmetric distribution that can be modeled with the normal distribution, a fundamental concept in statistics. This distribution is particularly useful for interpreting test performance fairly across populations, as it provides a mathematical framework for understanding variability and central tendency in large datasets.From Histogram to Frequency DistributionRaw test data are often displayed using histograms, where the height of each bar represents the...
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 Laws01:49

Probability Laws

Overview

You might also read

Related Articles

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

Sort by
Same author

A unified neurocomputational model of prospective and retrospective timing.

Psychological review·2025
Same author

Modelling neural probabilistic computation using vector symbolic architectures.

Cognitive neurodynamics·2024
Same author

A spiking neural model of decision making and the speed-accuracy trade-off.

Psychological review·2024
Same author

A scalable spiking amygdala model that explains fear conditioning, extinction, renewal and generalization.

The European journal of neuroscience·2024
Same author

Exploiting semantic information in a spiking neural SLAM system.

Frontiers in neuroscience·2023
Same author

Biologically-Based Computation: How Neural Details and Dynamics Are Suited for Implementing a Variety of Algorithms.

Brain sciences·2023
Same journal

Harmonic memory in phasor neural networks.

Biological cybernetics·2026
Same journal

Correction: Decreased spinal inhibition leads to undiversified locomotor patterns.

Biological cybernetics·2026
Same journal

Foundational issues of network models in biology.

Biological cybernetics·2026
Same journal

Dynamical mechanisms for coordinating long-term working memory based on the precision of spike-timing in cortical neurons.

Biological cybernetics·2026
Same journal

Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.

Biological cybernetics·2026
Same journal

Fluctuation-response relations for a two-stage population of spiking neurons stimulated by common noise.

Biological cybernetics·2026
See all related articles

Related Experiment Videos

Normalization for probabilistic inference with neurons.

Chris Eliasmith1, James Martens

  • 1Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, ON N2L3G1, Canada. celiasmith@uwaterloo.ca

Biological Cybernetics
|May 17, 2011
PubMed
Summary
This summary is machine-generated.

Researchers integrated normalization and probabilistic inference in neural networks. This biologically plausible approach avoids divisive normalization and pooling, enabling efficient computation in recurrent spiking neural networks.

Related Experiment Videos

Area of Science:

  • Computational neuroscience
  • Machine learning
  • Neural networks

Background:

  • Biologically plausible neural networks are being explored for probabilistic inference.
  • Proper normalization of represented distributions is crucial for repeated inference.
  • Previous methods often treated normalization separately from inference, using pooling-based divisive normalization.

Purpose of the Study:

  • To develop a unified mechanism for normalization and probabilistic inference in neural networks.
  • To eliminate the need for pooling or division-like operations in neural computation.
  • To demonstrate the biological plausibility and applicability of the integrated approach.

Main Methods:

  • Algebraically integrating normalization directly into the connection matrix.
  • Developing a unified connection matrix for inference and normalization.
  • Implementing the integrated solution in a recurrent spiking neural network model.

Main Results:

  • Demonstrated that normalization and inference can be combined within the connection matrix.
  • Showed this integration eliminates the need for separate pooling or division operations.
  • Successfully implemented the unified approach in a functional recurrent spiking neural network.

Conclusions:

  • The proposed method offers a biologically plausible and computationally efficient way to perform probabilistic inference in neural networks.
  • This unified approach simplifies neural computation by embedding normalization within the connection matrix.
  • The implementation in a spiking neural network validates its relevance for neural computation.