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Bayesian inference in ring attractor networks.

Anna Kutschireiter¹, Melanie A Basnak¹, Rachel I Wilson¹

¹Department of Neurobiology, Harvard Medical School, Boston, MA 02115.

Proceedings of the National Academy of Sciences of the United States of America

|February 22, 2023

Summary

This summary is machine-generated.

This study introduces a "Bayesian ring attractor" that accurately tracks head direction by incorporating uncertainty. This novel attractor network demonstrates near-optimal performance in path integration and evidence accumulation, offering a biologically plausible model for neural computation.

Keywords:

Bayesian inference Kalman filter head direction neurons ring attractor networks working memory

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Area of Science:

Computational neuroscience
Neural networks
Bayesian inference

Background:

Attractor networks are hypothesized to store working memories.
Conventional attractors lack mechanisms to represent memory uncertainty.
Accurate memory representation requires weighing evidence against uncertainty.

Purpose of the Study:

To demonstrate how uncertainty can be incorporated into attractor networks.
To develop a biologically plausible model for dynamic Bayesian inference in neural systems.
To investigate the performance of a novel

Main Methods:

Developed a normative framework (circular Kalman filter) for benchmarking.
Retuned recurrent connections in a conventional ring attractor.
Incorporated large-scale connectome data and biological constraints.

Main Results:

The proposed

Conclusions:

Attractor networks can implement dynamic Bayesian inference.
The Bayesian ring attractor offers a biologically plausible model for neural computation.
This model has implications for understanding head direction systems and other neural systems tracking periodic rhythms.