Open AccessSelf-healing codes: How stable neural populations can track continually reconfiguring neural representations
Author(s) -
Michael E Rule,
Timothy O’Leary
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2106692119
Subject(s) - hebbian theory , population , computer science , neuroscience , neural coding , biological neural network , sensory system , representation (politics) , redundancy (engineering) , artificial intelligence , artificial neural network , machine learning , biology , politics , political science , law , demography , sociology , operating system
Significance The brain is capable of adapting while maintaining stable long-term memories and learned skills. Recent experiments show that neural responses are highly plastic in some circuits, while other circuits maintain consistent responses over time, raising the question of how these circuits interact coherently. We show how simple, biologically motivated Hebbian and homeostatic mechanisms in single neurons can allow circuits with fixed responses to continuously track a plastic, changing representation without reference to an external learning signal.