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

Receptive Field Formation in Natural Scene Environments. Comparison of Single-Cell Learning Rules

Blais1, Intrator, Shouval

  • 1Brown University, Physics Department and Institute for Brain and Neural Systems, Providence RI, US, Box 1843, 02912. bblais@cns.brown.edu

Neural Computation
|September 23, 1998
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

MALDI TOF mass spectrometry for the characterization of phosphorus-containing dendrimers. Scope and limitations

Analytical chemistry·2000
Same author

Foraging methods can affect patch choice: an experimental study in Mallard (Anas platyrhynchos).

Behavioural processes·2000
Same author

Molecular batteries: ferrocenylsilylation of dendrons, dendritic cores, and dendrimers: new convergent and divergent routes to ferrocenyl dendrimers with stable redox activity

Chemistry (Weinheim an der Bergstrasse, Germany)·2000
Same author

Regioselective chlorocarbonylation of polybenzyl cores and functionalization using dendritic and organometallic nucleophiles

The Journal of organic chemistry·2000
Same author

Decontamination of Fly Ash and Used Lime from Municipal Waste Incinerator Using Thiobacillus ferrooxidans.

Environmental management·1999
Same author

Evaluation of an electronic blood pressure device for epidemiological studies.

Blood pressure monitoring·1999
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

We compared different learning rules for neuronal feature extraction. The Bienenstock-Cooper-Munro (BCM) rule showed kurtosis-like behavior, offering computational simplicity for neuronal coding.

Area of Science:

  • Computational neuroscience
  • Machine learning
  • Visual processing

Background:

  • Neuronal learning rules shape feature extraction.
  • Understanding these rules is key to decoding brain function.
  • Comparing different rules isolates their specific contributions.

Purpose of the Study:

  • To compare feature extraction and neuronal coding properties of various learning rules.
  • To analyze kurtosis maximization, skewness maximization, Bienenstock-Cooper-Munro (BCM) rule, and single-cell independent component analysis.
  • To investigate how these rules utilize data distributions.

Main Methods:

  • Utilized a consistent visual environment with natural scenes.
  • Employed a single-cell neuronal architecture for all rules.

Related Experiment Videos

  • Applied a structure removal method to analyze receptive field dependencies.
  • Compared kurtosis maximization, skewness maximization, BCM rule, and independent component analysis.

    • Main Results:

    • Receptive fields derived from these rules depend on limited data distribution aspects.
    • The quadratic BCM rule mimics kurtosis maximization when distributions have kurtotic directions.
    • BCM rule modification equations are computationally less complex.

    Conclusions:

    • The Bienenstock-Cooper-Munro (BCM) learning rule offers an efficient alternative for kurtosis-like feature extraction in neuronal coding.
    • Learning rule properties significantly influence neuronal receptive field development.
    • Computational efficiency can be achieved without sacrificing key feature extraction capabilities.