Fluctuation-dissipation relations in the imbalanced Wilson-Cowan model

Area of Science:

• Neuroscience
• Computational Neuroscience
• Theoretical Neuroscience

Background:

• The relationship between spontaneous and stimulated brain activity is a fundamental neuroscience question.
• Previous work suggested evoked responses can be predicted from spontaneous activity correlations, particularly in balanced excitation-inhibition states.

Purpose of the Study:

• To extend theoretical understanding of brain activity to imbalanced conditions.
• To investigate how deviations from balanced excitation-inhibition affect neural dynamics and response functions.

Main Methods:

• Utilized the Wilson-Cowan neural network model.
• Performed analytical calculations around the balanced fixed point.
• Compared analytical predictions with numerical simulations of neural networks.

Main Results:

• In imbalanced conditions, time correlation and response functions exhibit diverse behaviors, including oscillations due to complex eigenvalues.
• Analytical predictions align with numerical simulation results, confirming the role of cross-correlations in response functions.
• Identified inhibitory neurons as critical in regulating system excitability and imbalance.

Conclusions:

• The study validates the predictive power of spontaneous activity correlations for evoked responses, even in imbalanced brain states.
• Neural network models can capture complex dynamics arising from imbalanced excitation-inhibition.
• Inhibitory neuron activity is crucial for maintaining and modulating brain state and responsiveness.