Neural landscape diffusion resolves conflicts between needs across time

Ethan B. Richman, Nicole Ticea, William E. Allen, Karl Deisseroth () and Liqun Luo ()
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Ethan B. Richman: Stanford University
Nicole Ticea: Stanford University
William E. Allen: Stanford University
Karl Deisseroth: Stanford University
Liqun Luo: Stanford University

Nature, 2023, vol. 623, issue 7987, 571-579

Abstract: Abstract Animals perform flexible goal-directed behaviours to satisfy their basic physiological needs1–12. However, little is known about how unitary behaviours are chosen under conflicting needs. Here we reveal principles by which the brain resolves such conflicts between needs across time. We developed an experimental paradigm in which a hungry and thirsty mouse is given free choices between equidistant food and water. We found that mice collect need-appropriate rewards by structuring their choices into persistent bouts with stochastic transitions. High-density electrophysiological recordings during this behaviour revealed distributed single neuron and neuronal population correlates of a persistent internal goal state guiding future choices of the mouse. We captured these phenomena with a mathematical model describing a global need state that noisily diffuses across a shifting energy landscape. Model simulations successfully predicted behavioural and neural data, including population neural dynamics before choice transitions and in response to optogenetic thirst stimulation. These results provide a general framework for resolving conflicts between needs across time, rooted in the emergent properties of need-dependent state persistence and noise-driven shifts between behavioural goals.

Date: 2023
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DOI: 10.1038/s41586-023-06715-z

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