TY - JOUR
T1 - The dynamics and geometry of choice in the premotor cortex
AU - Genkin, Mikhail
AU - Shenoy, Krishna V.
AU - Chandrasekaran, Chandramouli
AU - Engel, Tatiana A.
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/9/4
Y1 - 2025/9/4
N2 - The brain represents sensory variables in the coordinated activity of neural populations, in which tuning curves of single neurons define the geometry of the population code1,2. Whether the same coding principle holds for dynamic cognitive variables remains unknown because internal cognitive processes unfold with a unique time course on single trials observed only in the irregular spiking of heterogeneous neural populations3, 4, 5, 6, 7–8. Here we show the existence of such a population code for the dynamics of choice formation in the primate premotor cortex. We developed an approach to simultaneously infer population dynamics and tuning functions of single neurons to the population state. Applied to spike data recorded during decision-making, our model revealed that populations of neurons encoded the same dynamic variable predicting choices, and heterogeneous firing rates resulted from the diverse tuning of single neurons to this decision variable. The inferred dynamics indicated an attractor mechanism for decision computation. Our results reveal a unifying geometric principle for neural encoding of sensory and dynamic cognitive variables.
AB - The brain represents sensory variables in the coordinated activity of neural populations, in which tuning curves of single neurons define the geometry of the population code1,2. Whether the same coding principle holds for dynamic cognitive variables remains unknown because internal cognitive processes unfold with a unique time course on single trials observed only in the irregular spiking of heterogeneous neural populations3, 4, 5, 6, 7–8. Here we show the existence of such a population code for the dynamics of choice formation in the primate premotor cortex. We developed an approach to simultaneously infer population dynamics and tuning functions of single neurons to the population state. Applied to spike data recorded during decision-making, our model revealed that populations of neurons encoded the same dynamic variable predicting choices, and heterogeneous firing rates resulted from the diverse tuning of single neurons to this decision variable. The inferred dynamics indicated an attractor mechanism for decision computation. Our results reveal a unifying geometric principle for neural encoding of sensory and dynamic cognitive variables.
UR - https://www.scopus.com/pages/publications/105009007828
UR - https://www.scopus.com/pages/publications/105009007828#tab=citedBy
U2 - 10.1038/s41586-025-09199-1
DO - 10.1038/s41586-025-09199-1
M3 - Article
C2 - 40562938
AN - SCOPUS:105009007828
SN - 0028-0836
VL - 645
SP - 168
EP - 176
JO - Nature
JF - Nature
IS - 8079
ER -