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Local neuronal correlations, not visual stimuli or running speed, best predict neural activity in mouse visual cortex. Functional groups of co-varying neurons improve sensory decoding, defining Hebbian assemblies.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Understanding sensory encoding requires explaining trial-to-trial variability in cortical neuron activity.
  • Neuronal responses vary significantly even with identical stimuli, necessitating models that capture this dynamic.

Purpose of the Study:

  • To determine the relative importance of visual stimulus, running speed, and neuronal correlations in explaining short-term dynamics of L2/3 murine visual cortical neurons.
  • To evaluate if incorporating local neuronal correlations improves predictions of neuronal variability within single trials.

Main Methods:

  • A linear model was used to analyze the short-term dynamics of L2/3 murine visual cortical neurons.
  • The model incorporated terms for visual stimulus, mouse running speed, and experimentally measured neuronal correlations.
  • The predictive power of each factor on neuronal variability was assessed.

Main Results:

  • Predictions of neuronal activity on single trials improved most when conditioning on experimentally measured local neuronal correlations.
  • Accurate single-trial predictions were driven by positively co-varying and synchronously active functional groups of neurons.
  • Including functional groups in the model significantly enhanced the decoding accuracy of sensory information compared to models assuming neuronal independence.

Conclusions:

  • Local neuronal correlations are a critical factor in explaining neuronal variability within single trials.
  • Functional groups of neurons, defined by local correlations, provide an operational definition of Hebbian assemblies.
  • These findings advance our understanding of sensory encoding and neural computation by highlighting the role of correlated activity.