Self-healing codes: How stable neural populations can track continually reconfiguring neural representations.

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As an adaptive system, the brain must retain a faithful representation of the world while continuously integrating new information. Recent experiments have measured population activity in cortical and hippocampal circuits over many days and found that patterns of neural activity associated with fixed behavioral variables and percepts change dramatically over time. Such "representational drift" raises the question of how malleable population codes can interact coherently with stable long-term representations that are found in other circuits and with relatively rigid topographic mappings of peripheral sensory and motor signals. We explore how known plasticity mechanisms can allow single neurons to reliably read out an evolving population code without external error feedback. We find that interactions between Hebbian learning and single-cell homeostasis can exploit redundancy in a distributed population code to compensate for gradual changes in tuning. Recurrent feedback of partially stabilized readouts could allow a pool of readout cells to further correct inconsistencies introduced by representational drift. This shows how relatively simple, known mechanisms can stabilize neural tuning in the short term and provides a plausible explanation for how plastic neural codes remain integrated with consolidated, long-term representations.

Hebbian plasticity, homeostasis, lifelong learning, representational drift, Animals, Homeostasis, Models, Neurological, Nerve Net, Neuronal Plasticity, Neurons
Journal Title
Proc Natl Acad Sci U S A
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Journal ISSN
Volume Title
Proceedings of the National Academy of Sciences
European Research Council (716643)