Learning to integrate parts for whole through correlated neural variability.

ABSTRACT: Neural activity in the cortex exhibits a wide range of firing variability and rich correlation structures. Studies on neural coding indicate that correlated neural variability can influence the quality of neural codes, either beneficially or adversely. However, the mechanisms by which correlated neural variability is transformed and processed across neural populations to achieve meaningful computation remain largely unclear. Here we propose a theory of covariance computation with spiking neurons which offers a unifying perspective on neural representation and computation with correlated noise. We employ a recently proposed computational framework known as the moment neural network to resolve the nonlinear coupling of correlated neural variability with a task-driven approach to constructing

SUBMITTER: Zhu Z 

PROVIDER: S-EPMC11398653 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature