{"title":"Attractor and integrator networks in the brain","authors":"Mikail Khona, Ila R. Fiete","doi":"10.1038/s41583-022-00642-0","DOIUrl":null,"url":null,"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. Attractor network dynamics can support several computations performed by the brain. In their Review, Khona and Fiete introduce different attractor dynamics and their computational utility, describe evidence of attractor networks across the brain and explain how such networks could be recombined to increase their flexibility and versatility.","PeriodicalId":49142,"journal":{"name":"Nature Reviews Neuroscience","volume":null,"pages":null},"PeriodicalIF":28.7000,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"61","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://www.nature.com/articles/s41583-022-00642-0","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 61
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. Attractor network dynamics can support several computations performed by the brain. In their Review, Khona and Fiete introduce different attractor dynamics and their computational utility, describe evidence of attractor networks across the brain and explain how such networks could be recombined to increase their flexibility and versatility.
期刊介绍:
Nature Reviews Neuroscience is a multidisciplinary journal that covers various fields within neuroscience, aiming to offer a comprehensive understanding of the structure and function of the central nervous system. Advances in molecular, developmental, and cognitive neuroscience, facilitated by powerful experimental techniques and theoretical approaches, have made enduring neurobiological questions more accessible. Nature Reviews Neuroscience serves as a reliable and accessible resource, addressing the breadth and depth of modern neuroscience. It acts as an authoritative and engaging reference for scientists interested in all aspects of neuroscience.