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

Constrained optimization for neural map formation: a unifying framework for weight growth and normalization

L Wiskott, T Sejnowski

    Neural Computation
    |April 4, 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

    Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics.

    Journal of neuroscience methods·2018
    Same author

    From grids to places.

    Journal of computational neuroscience·2006
    Same author

    The role of RNA editing of kainate receptors in synaptic plasticity and seizures.

    Neuron·2001
    Same author

    Spiking-bursting activity in the thalamic reticular nucleus initiates sequences of spindle oscillations in thalamic networks.

    Journal of neurophysiology·2000
    Same author

    Recognizing faces by dynamic link matching.

    NeuroImage·1996
    Same author

    Inhibition synchronizes sparsely connected cortical neurons within and between columns in realistic network models.

    Journal of computational neuroscience·1996
    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 unifies computational models of neural map formation by deriving various weight dynamics from a constrained optimization framework. This approach clarifies relationships between different modeling levels and aids in understanding neural map development.

    Area of Science:

    • Computational neuroscience
    • Theoretical neuroscience
    • Mathematical biology

    Background:

    • Neural map formation is modeled at multiple abstraction levels, including detailed neural activity, adiabatic approximations of weight dynamics, and constrained optimization.
    • Constrained optimization offers a framework to derive weight growth and normalization rules from objective functions and constraints.

    Purpose of the Study:

    • To demonstrate how optimization problems can be derived from detailed nonlinear neural dynamics.
    • To systematically investigate the derivation of various weight dynamics from objective function terms and constraints.
    • To explore the role of coordinate transformations in deriving different weight dynamics from a unified optimization problem.

    Main Methods:

    • Derivation of optimization problems from detailed nonlinear neural dynamics.

    Related Experiment Videos

  • Systematic analysis of weight dynamics using objective function terms and constraints.
  • Application of coordinate transformations to explore variations in weight dynamics.
  • Illustrative examples of the constrained optimization framework for neural map formation models.

    • Main Results:

    • Demonstrated a method to derive optimization problems from detailed neural dynamics.
    • Showed that different previously introduced weight dynamics can be systematically derived from specific objective function terms and constraints.
    • Highlighted the utility of coordinate transformations in generating diverse weight dynamics from a single optimization framework.
    • Confirmed dynamic link matching as a special case within this framework.

    Conclusions:

    • The constrained optimization framework provides a unified approach to understanding, generating, and comparing computational models of neural map formation.
    • This framework offers insights into the relationships between different levels of abstraction in neural modeling.
    • The presented techniques can potentially be applied to analyze other types of neural dynamics.