Patterns of ongoing activity and the functional architecture of the primary visual cortex

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Spontaneous neural activity in the cerebral cortex is complex and occurs without external stimuli.
• Understanding the underlying dynamics of this ongoing activity is crucial for deciphering brain function.

Purpose of the Study:

• To theoretically investigate two distinct models of cortical dynamics: a single background state versus multiple attractor states.
• To compare the predictions of these models with experimental optical-imaging data from the primary visual cortex (V1).

Main Methods:

• Development of simplified network rate models for the primary visual cortex (V1).
• Analysis of fluctuation characteristics (dimensionality, Gaussianity, speed) for each model.
• Utilizing a more realistic V1 model with input correlations and spatial interactions.

Main Results:

• The single state model predicts fast, high-dimensional, Gaussian-like fluctuations.
• The multiple state model predicts slow, low-dimensional, non-Gaussian fluctuations.
• Experimental V1 optical-imaging data are consistent with both the single and multiple attractor state models.

Conclusions:

• Ongoing cortical activity can be flexibly explained by either a stable background state or dynamic attractor states.
• These findings provide insights into the representational capacity of spontaneous neural activity in V1.
• The study highlights the importance of theoretical modeling in interpreting complex neural data.