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

Neural representation of probabilistic information.

M J Barber1, J W Clark, C H Anderson

  • 1Institut für Theoretische Physik, Universität zu Köln, D-50937 Köln, Germany.

Neural Computation
|September 27, 2003
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

A crown-group cnidarian from the Ediacaran of Charnwood Forest, UK.

Nature ecology & evolution·2022
Same author

Crizotinib in ROS1-rearranged advanced non-small-cell lung cancer (NSCLC): updated results, including overall survival, from PROFILE 1001.

Annals of oncology : official journal of the European Society for Medical Oncology·2019
Same author

Phase II study of gemcitabine, oxaliplatin in combination with panitumumab in KRAS wild-type unresectable or metastatic biliary tract and gallbladder cancer.

British journal of cancer·2014
Same author

Merging of Landau levels in a strongly interacting two-dimensional electron system in silicon.

Physical review letters·2014
Same author

The activity of phosphonic and phosphinic acids on Treponema pallidum.

Antibiotics & chemotherapy (Northfield, Ill.)·2014
Same author

Clinical benefit of continuing ALK inhibition with crizotinib beyond initial disease progression in patients with advanced ALK-positive NSCLC.

Annals of oncology : official journal of the European Society for Medical Oncology·2014

Neural populations represent information using probability density functions (PDFs) of analog variables. This framework enables Bayesian computation through precisely defined neural encoding and decoding mechanisms.

Area of Science:

  • Computational Neuroscience
  • Neural Coding
  • Bayesian Inference

Background:

  • Neurons process analog variables, necessitating probabilistic descriptions.
  • Limited neuronal precision favors population-based information representation.
  • Existing models struggle to unify encoding, decoding, and Bayesian computation.

Purpose of the Study:

  • To demonstrate how neural populations encode and decode probability density functions (PDFs).
  • To establish a consistent Bayesian framework for neural computation.
  • To derive synaptic weights from encoding, decoding, and probability distributions.

Main Methods:

  • Decoding PDFs from neuronal firing rates using linear combinations of decoding functions.
  • Encoding PDFs by projecting onto complementary encoding functions and applying a nonlinear activation.

Related Experiment Videos

  • Minimizing cost functions to determine optimal encoding and decoding functions.
  • Formulating neural computations within a Bayesian framework with explicit synaptic weight generation.

    • Main Results:

    • A mathematical framework for representing time-dependent PDFs (rho(x; t)) using population activity (N firing rates a(i)(t)).
    • Demonstration of encoding via projection and rectification, and decoding via linear combination.
    • Derivation of encoders and decoders by minimizing representation inaccuracy.
    • A consistent Bayesian framework where computations are transformations of probabilities.

    Conclusions:

    • Neural populations can effectively represent and process information as probability density functions.
    • This probabilistic population code offers a unified approach to neural encoding, decoding, and Bayesian computation.
    • The model provides explicit methods for determining synaptic weights crucial for neural circuit function.