Attractor concretion as a mechanism for the formation of context representations.

NeuroImage
Mattia RigottiStefano Fusi

Abstract

Complex tasks often require the memory of recent events, the knowledge about the context in which they occur, and the goals we intend to reach. All this information is stored in our mental states. Given a set of mental states, reinforcement learning (RL) algorithms predict the optimal policy that maximizes future reward. RL algorithms assign a value to each already-known state so that discovering the optimal policy reduces to selecting the action leading to the state with the highest value. But how does the brain create representations of these mental states in the first place? We propose a mechanism for the creation of mental states that contain information about the temporal statistics of the events in a particular context. We suggest that the mental states are represented by stable patterns of reverberating activity, which are attractors of the neural dynamics. These representations are built from neurons that are selective to specific combinations of external events (e.g. sensory stimuli) and pre-existent mental states. Consistent with this notion, we find that neurons in the amygdala and in orbitofrontal cortex (OFC) often exhibit this form of mixed selectivity. We propose that activating different mixed selectivity neuron...Continue Reading

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Citations

Mar 25, 2010·Annual Review of Neuroscience·C Daniel Salzman, Stefano Fusi
Dec 29, 2010·Hepatitis Research and Treatment·Tatehiro Kagawa, Emmet B Keeffe
Feb 7, 2016·Current Opinion in Neurobiology·Stefano FusiMattia Rigotti
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Jun 19, 2016·Neuroscience·Junchol Park, Bita Moghaddam
Jun 11, 2016·PLoS Computational Biology·Pierre EnelPeter Ford Dominey
May 28, 2019·Journal of Computational Neuroscience·Benjamin BallintynPaul Miller
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Mar 10, 2020·ELife·Flora BouchacourtSrdjan Ostojic
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Feb 17, 2021·PloS One·Laura LazartiguesFrédéric Lavigne
Mar 5, 2021·Nature Communications·Stefano RecanatesiEric Shea-Brown
Apr 17, 2021·Learning & Memory·Ehsan KakaeiJochen Braun

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