Jove
Visualize
Contact Us

Related Experiment Videos

Invariance and selectivity in the ventral visual pathway.

Stuart Geman1

  • 1Division of Applied Mathematics, Brown University Providence, RI 02912, USA. geman@dam.brown.edu

Journal of Physiology, Paris
|March 6, 2007
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

Capacities and efficient computation of first-passage probabilities.

Physical review. E·2020
Same author

Base-pair ambiguity and the kinetics of RNA folding.

BMC bioinformatics·2019
Same author

Transsacadic Information and Corollary Discharge in Local Field Potentials of Macaque V1.

Frontiers in integrative neuroscience·2019
Same author

Opinion: Science in the age of selfies.

Proceedings of the National Academy of Sciences of the United States of America·2016
Same author

Ambiguity and nonidentifiability in the statistical analysis of neural codes.

Proceedings of the National Academy of Sciences of the United States of America·2015
Same author

Visual Turing test for computer vision systems.

Proceedings of the National Academy of Sciences of the United States of America·2015
Same journal

Role of synchronized physiological and interpersonal rhythms in typical and atypical development.

Journal of physiology, Paris·2017
Same journal

Suicide attempts in children and adolescents: The place of clock genes and early rhythm dysfunction.

Journal of physiology, Paris·2017
Same journal

Editorial.

Journal of physiology, Paris·2017
Same journal

Dyssynchrony and perinatal psychopathology impact of child disease on parents-child interactions, the paradigm of Prader Willi syndrom.

Journal of physiology, Paris·2017
Same journal

Key considerations in designing a speech brain-computer interface.

Journal of physiology, Paris·2017
Same journal

Links between early child maltreatment, mental disorders, and cortisol secretion anomalies.

Journal of physiology, Paris·2017
See all related articles
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

Biological vision systems achieve selectivity despite invariance by using nonlinear dynamics. This allows for correlated neural activity, preserving pattern recognition accuracy and reducing false alarms in complex visual processing.

Area of Science:

  • Computational neuroscience
  • Computer vision
  • Neurobiology

Background:

  • Artificial pattern recognition systems struggle with selectivity, leading to high false alarm rates due to invariance to shape, pose, lighting, and texture.
  • Biological vision systems exhibit both invariance and selectivity, a capability not fully replicated in artificial systems.
  • Understanding how biological systems distinguish correct sub-pattern arrangements from chance arrangements is crucial for advancing pattern recognition.

Purpose of the Study:

  • To investigate the mechanisms underlying the selectivity of biological vision systems.
  • To explore the role of nonlinear dynamics in invariant cell types for achieving both invariance and selectivity.
  • To elucidate how biological systems maintain pattern recognition accuracy despite variations in input.

Related Experiment Videos

Main Methods:

  • Analysis of nonlinear dynamics in complex and other invariant cell types.
  • Investigation of functional connectivity and its impact on neural receptivity.
  • Examination of functional common input between cells with overlapping receptive fields.

Main Results:

  • Invariant cell types exhibit nonlinear dynamics, leading to temporarily modulated receptivity to inputs (functional connectivity).
  • Pairs of cells with overlapping receptive fields demonstrate functional common input, correlating activity when sub-patterns match.
  • These correlations, potentially manifesting as local synchrony, appear to preserve selectivity lost in artificial invariant systems.

Conclusions:

  • Nonlinear dynamics and functional common input are key mechanisms for biological vision systems to achieve selective pattern recognition.
  • The findings suggest a novel approach for developing more robust and selective artificial pattern recognition systems.
  • Further research into neural synchrony and its role in visual processing could yield significant advancements in AI.