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 Experiment Videos

Neural correlation via random connections

J Chover1

  • 1Department of Mathematics, University of Wisconsin-Madison 53706, USA.

Neural Computation
|November 15, 1996
PubMed
Summary
This summary is machine-generated.

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

Alternating dominance of NMDA and AMPA for learning and recall: a computer model.

Neuroreport·2001
Same author

Modelling afferent connectivity, postsynaptic plasticity, and signal discrimination.

Synapse (New York, N.Y.)·1989
Same author

Kinetics of diethylnitrosamine hepatocarcinogenesis in the infant mouse.

Cancer research·1985
Same author

The early growth of cancer.

Journal of mathematical biology·1985
Same author

Optimal use of sampled tissue sections for estimating the number of hepatocellular foci.

Cancer research·1984
Same author

Reliable stereological method for estimating the number of microscopic hepatocellular foci from their transections.

Cancer research·1983
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

This study explores a simple neural network model with sparse, random, and plastic connections. The model demonstrates associative recall capabilities, mimicking Hebb-like synaptic plasticity within biological parameters.

Area of Science:

  • Computational Neuroscience
  • Artificial Neural Networks

Background:

  • Understanding the computational principles of neural networks is crucial for neuroscience and AI.
  • Neural networks with sparse, plastic connections and feedback loops are biologically plausible models.

Purpose of the Study:

  • To investigate the associative recall capacity of a simple neural network model.
  • To analyze the role of Hebb-like synaptic changes in network function.

Main Methods:

  • Simulated a neural network with sparse, random, excitatory connections and feedback loops.
  • Constrained time to discrete instants with synchronous firing.
  • Employed parameter values within biological ranges.

Main Results:

Related Experiment Videos

  • The model exhibited associative recall capabilities.
  • Controlled extraneous firing was observed.
  • Results align with Hebb-like synaptic plasticity principles.

    • Conclusions:

    • Simple neural networks with specific connection properties can perform associative recall.
    • Hebb-like synaptic changes are a viable mechanism for associative memory in such systems.