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

Recognition by variance: learning rules for spatiotemporal patterns.

Omri Barak1, Misha Tsodyks

  • 1omri.barak@weizmann.ac.il

Neural Computation
|August 16, 2006
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

When predict can also explain: Few-shot prediction to select better neural latents.

PLoS computational biology·2025
Same author

Interactions between long- and short-term synaptic plasticity transform temporal neural representations into spatial.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Corrigendum to "Mathematical models of learning and what can be learned from them" [Curr Opin Neurobiol 80 (2023)].

Current opinion in neurobiology·2025
Same author

Random Tree Model of Meaningful Memory.

Physical review letters·2025
Same author

Information rate of meaningful communication.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Large-scale study of human memory for meaningful narratives.

Learning & memory (Cold Spring Harbor, N.Y.)·2025
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 introduces a novel learning rule for neurons to recognize complex spatiotemporal patterns. The model enhances pattern recognition by increasing input current variance, demonstrating robustness to timing variations.

Area of Science:

  • Computational neuroscience
  • Neural network modeling
  • Spatiotemporal pattern recognition

Background:

  • The nervous system must recognize complex spatiotemporal activity patterns occurring at timescales larger than neuronal constants.
  • Auditory processing serves as a key example of this neural demand.

Purpose of the Study:

  • To develop a computational model for a single neuron to recognize specific spatiotemporal spiking input patterns.
  • To derive learning rules that enhance the discrimination of learned patterns from background activity.

Main Methods:

  • Utilizing the relationship between mean/variance of input current and neuron spiking output.
  • Developing learning rules to increase the variance of input current for learned patterns.
  • Employing a leaky integrate-and-fire neuron model for simulation.

Related Experiment Videos

Main Results:

  • The model successfully recognizes a large number of spatiotemporal patterns.
  • Performance shows slow degradation with an increasing number of learned patterns.
  • The model exhibits emergent robustness to time warping in input patterns.

Conclusions:

  • The derived learning rules enable effective spatiotemporal pattern recognition in a single neuron model.
  • The approach is robust and adaptable, showing potential for understanding neural computation in sensory processing.