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

[Vector coding and neuronal maps]

E N Sokolov

    Zhurnal Vysshei Nervnoi Deiatelnosti Imeni I P Pavlova
    |January 1, 1996
    PubMed
    Summary
    This summary is machine-generated.

    A novel vector coding model explains how neuronal ensembles represent stimuli. This model also sheds light on associative learning and memory mechanisms.

    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

    [Responses of rabbit's visual cortex neurons to changes in intensity and orientation of visual stimuli].

    Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova·2010
    Same author

    Segmentation, grouping and accentuation during stimulus perception.

    Neuroscience and behavioral physiology·2010
    Same author

    Evoked potentials in the rabbit visual cortex reflect changes in line orientation and intensity.

    Neuroscience and behavioral physiology·2009
    Same author

    [Segmentation, grouping and accentuation during stimuli perception].

    Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova·2009
    Same author

    [Visual evoked potentials in rabbit's visual cortex reflect variations in orientation and intensity of lines].

    Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova·2009
    Same author

    Assessment of brightness and color differences by neurons in the superior colliculus of the rabbit.

    Neuroscience and behavioral physiology·2008
    Same journal

    [Altered Expression of Neurotransmitters Systems' Genes in the Ventral Tegmental Area of Depressive Male Mice: Data of RNA-Seq].

    Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova·2019
    Same journal

    [Behavioral Phenotype of Mice with Alkali Sensor IRR Gene Knockout].

    Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova·2019
    Same journal

    [Play Fighting in Rats, Selected for the Elimination and the Enhancement of Aggressiveness Towards Human].

    Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova·2019
    Same journal

    [Experience of the Continuous Real Time fMRI Biofeedback of the Primary Motor Cortex Using a 1.5 T MR Scanner].

    Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova·2019
    Same journal

    [Perception of Verbal Stimuli in the Norm and in Schizophrenia].

    Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova·2019
    Same journal

    [Aging-Induced Reorganization of Association Between Intelligence and Characteristics of Attention and Memory].

    Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova·2019
    See all related articles

    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Cognitive Science

    Context:

    • Neuronal ensembles generate complex patterns of activity.
    • Understanding how these patterns encode information is crucial.
    • Existing models may not fully capture stimulus representation and associative learning.

    Purpose:

    • To propose a novel model of vector coding for neural information processing.
    • To explain how excitation vectors represent input stimuli.
    • To elucidate the role of vector coding in associative learning and memory.

    Summary:

    • A vector coding model is presented, where excitation vectors from neuronal ensembles activate selective detectors.
    • This activation creates localized excitation maxima, representing specific input stimuli.

    Related Experiment Videos

  • The model suggests vector coding underlies associative learning and memory, with output responses determined by command neurons.

    • Impact:

    • Provides a new framework for understanding neural representation.
    • Offers insights into the neural basis of learning and memory.
    • Potential applications in artificial intelligence and brain-computer interfaces.