Deciphering neuronal variability across states reveals dynamic sensory encoding.

ABSTRACT: Influenced by non-stationary factors such as brain states and behavior, neurons exhibit substantial response variability even to identical stimuli. However, it remains unclear how their relative impact on neuronal variability evolves over time. To address this question, we designed an encoding model conditioned on latent states to partition variability in the mouse visual cortex across internal brain dynamics, behavior, and external visual stimulus. Applying a hidden Markov model to local field potentials, we consistently identified three distinct oscillation states, each with a unique variability profile. Regression models within each state revealed a dynamic composition of factors influencing spiking variability, with the dominant factor switching within seconds. The state-conditioned re

SUBMITTER: Akella S 

PROVIDER: S-EPMC11839951 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature