Work

Representational drift: Emerging theories for continual learning and experimental future directions

Year: 2022

Type: review

Abstract: Recent work has revealed that the neural activity patterns correlated with sensation, cognition, and action often are not stable and instead undergo large scale changes over days and weeks-a phenomenon called representational drift. Here, we highlight recent observations of drift, how drift is unlikely to be explained by experimental confounds, and how the brain can likely compensate for drift to allow stable computation. We propose that drift might have important roles in neural computation to allow continual learning, both for separating and relating memories that occur at distinct times. Finally, we present an outlook on future experimental directions that are needed to further characterize drift and to test emerging theories for drift's role in computation.

Source: Current Opinion in Neurobiology

DOI: 10.1016/j.conb.2022.102609

Authors: Laura N. Driscoll, Lea Duncker, Christopher D. Harvey

Institutions: Stanford University, Howard Hughes Medical Institute, Harvard University

Language: English

Topic: Memory and Neural Mechanisms

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $4,350

Funders: Human Frontier Science Program, National Institutes of Health, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke