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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Shared neural variability is common in cortical populations, but its link to local circuit architecture is not fully understood.
  • Existing theories often presume overlapping synaptic input as the primary driver of this variability.

Purpose of the Study:

  • To investigate the relationship between the spatial structure of neural connectivity and correlated variability in neural circuits.
  • To extend balanced network theory by incorporating distance-dependent connectivity patterns.

Main Methods:

  • Utilized computational models of neural networks.
  • Performed in vivo recordings from the macaque primary visual cortex.
  • Analyzed the spatial correlation structure of neural activity and connectivity.

Main Results:

  • Spatially localized lateral projections lead to weakly correlated spiking.
  • Broader lateral projections result in a non-monotonic distance-dependent correlation structure: positive for nearby pairs, negative for intermediate distances, and weak for distant pairs.
  • This distance-dependent pattern was observed in recordings from the macaque visual cortex.

Conclusions:

  • Distance-dependent connectivity is crucial for explaining correlated neural variability.
  • Balanced network theory is enhanced by incorporating spatial connectivity structures.
  • The findings provide insights into the local circuit architecture underlying neural variability.