Deciphering neuronal variability across states reveals dynamic sensory encoding

Shailaja Akella (), Peter Ledochowitsch, Joshua H. Siegle, Hannah Belski, Daniel D. Denman, Michael A. Buice, Severine Durand, Christof Koch, Shawn R. Olsen and Xiaoxuan Jia ()
Additional contact information
Shailaja Akella: Allen Institute
Peter Ledochowitsch: Allen Institute
Joshua H. Siegle: Allen Institute
Hannah Belski: Allen Institute
Daniel D. Denman: Allen Institute
Michael A. Buice: Allen Institute
Severine Durand: Allen Institute
Christof Koch: Allen Institute
Shawn R. Olsen: Allen Institute
Xiaoxuan Jia: Tsinghua University

Nature Communications, 2025, vol. 16, issue 1, 1-22

Abstract: 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 regression model uncovered extensive diversity in source contributions across units, varying in accordance with anatomical hierarchy and internal state. This heterogeneity in encoding underscores the importance of partitioning variability over time, particularly when considering the influence of non-stationary factors on sensory processing.

Date: 2025
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DOI: 10.1038/s41467-025-56733-w

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