Attractor and integrator networks in the brain
Name
2112.03978.pdf
Description
Submitted version
Size
19.16 MB
Format
Adobe PDF
Checksum (MD5)
23b08a0359bb8cb57d1598417000699a
Author(s)
Khona, Mikail
Fiete, Ila R
Date Issued
December 2022
Journal
Nature Reviews Neuroscience
Publisher
Springer Science and Business Media LLC
Citation
Khona, Mikail and Fiete, Ila R. 2022. "Attractor and integrator networks in the brain." Nature Reviews Neuroscience, 23 (12).
Version
Original manuscript
Abstract
In this Review, we describe the singular success of attractor neural network models in describing how the brain maintains persistent activity states for working memory, corrects errors and integrates noisy cues. We consider the mechanisms by which simple and forgetful units can organize to collectively generate dynamics on the long timescales required for such computations. We discuss the myriad potential uses of attractor dynamics for computation in the brain, and showcase notable examples of brain systems in which inherently low-dimensional continuous-attractor dynamics have been concretely and rigorously identified. Thus, it is now possible to conclusively state that the brain constructs and uses such systems for computation. Finally, we highlight recent theoretical advances in understanding how the fundamental trade-offs between robustness and capacity and between structure and flexibility can be overcome by reusing and recombining the same set of modular attractors for multiple functions, so they together produce representations that are structurally constrained and robust but exhibit high capacity and are flexible.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41583-022-00642-0
