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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Neurons show significant response variability to identical stimuli due to non-stationary factors like brain states and behavior.
  • Understanding the temporal dynamics of these factors' influence on neuronal variability is crucial but remains unclear.

Purpose of the Study:

  • To investigate how internal brain dynamics, behavior, and external visual stimuli dynamically influence neuronal response variability over time.
  • To partition the sources of variability in the mouse visual cortex using a state-conditioned encoding model.

Main Methods:

  • Applied a hidden Markov model to local field potentials to identify distinct brain oscillation states.
  • Developed a state-conditioned encoding model to analyze spiking variability.
  • Utilized regression models within identified states to determine the contribution of different factors.

Main Results:

  • Consistently identified three distinct oscillation states, each with a unique neuronal variability profile.
  • Revealed that the dominant factor influencing spiking variability dynamically switches within seconds.
  • Uncovered significant diversity in the contribution of internal states and anatomical hierarchy to neuronal variability across units.

Conclusions:

  • Neuronal variability is highly dynamic and influenced by a changing composition of factors over short timescales.
  • Partitioning variability based on distinct brain states is essential for accurately understanding sensory processing.
  • The findings highlight the importance of considering non-stationary influences on neural encoding.