Neural landscape diffusion resolves conflicts between needs across time

Ethan B Richman; Nicole Ticea; William E Allen; Karl Deisseroth; Liqun Luo

doi:10.1038/s41586-023-06715-z

Neural landscape diffusion resolves conflicts between needs across time

Nature. 2023 Nov;623(7987):571-579. doi: 10.1038/s41586-023-06715-z. Epub 2023 Nov 8.

Authors

Ethan B Richman¹, Nicole Ticea^{2

3

4}, William E Allen^{1

5}, Karl Deisseroth^{6

7

8}, Liqun Luo^{9

10}

Affiliations

¹ Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
² Department of Bioengineering, Stanford University, Stanford, CA, USA.
³ Department of Biology, Stanford University, Stanford, CA, USA.
⁴ Department of Applied Physics, Stanford University, Stanford, CA, USA.
⁵ Society of Fellows, Harvard University, Cambridge, MA, USA.
⁶ Department of Bioengineering, Stanford University, Stanford, CA, USA. deissero@stanford.edu.
⁷ Department of Psychiatry and Behavioral Sciences, Stanford, CA, USA. deissero@stanford.edu.
⁸ Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA. deissero@stanford.edu.
⁹ Department of Biology, Stanford University, Stanford, CA, USA. lluo@stanford.edu.
¹⁰ Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA. lluo@stanford.edu.

Abstract

Animals perform flexible goal-directed behaviours to satisfy their basic physiological needs^1-12. 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.

MeSH terms

Animals
Brain* / cytology
Brain* / physiology
Choice Behavior* / physiology
Food
Goals
Hunger* / physiology
Mice
Models, Neurological
Neurons* / physiology
Optogenetics
Reward
Stochastic Processes
Thirst* / physiology
Time Factors
Water

Substances

Water

Abstract

MeSH terms

Substances

Grants and funding