Common population codes produce extremely nonlinear neural manifolds.

ABSTRACT: Populations of neurons represent sensory, motor, and cognitive variables via patterns of activity distributed across the population. The size of the population used to encode a variable is typically much greater than the dimension of the variable itself, and thus, the corresponding neural population activity occupies lower-dimensional subsets of the full set of possible activity states. Given population activity data with such lower-dimensional structure, a fundamental question asks how close the low-dimensional data lie to a linear subspace. The linearity or nonlinearity of the low-dimensional structure reflects important computational features of the encoding, such as robustness and generalizability. Moreover, identifying such linear structure underlies common data analysis methods such

SUBMITTER: De A 

PROVIDER: S-EPMC10523500 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature