Dynamical modeling of multi-scale variability in neuronal competition.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Perception

Background:

• Variability exists across multiple scales in the brain and perception.
• The origins of perceptual variability, unlike spiking variability (explained by the balanced state theory), remain unclear.
• Perceptual rivalry, a form of neuronal competition, offers insights into neural processing due to correlated brain activity with alternating perceptions.

Purpose of the Study:

• To investigate the source of perceptual variability during neuronal competition.
• To develop and validate a biophysical circuit model that explains both spiking and perceptual variability.
• To determine if a single model can account for variability across multiple scales.

Main Methods:

• Developed a single biophysical circuit model incorporating specific mutual inhibition architectures.
• Ensured the model satisfied a wide range of experimental constraints at multiple scales.
• Analyzed the model's ability to replicate observed spiking and perceptual variability during rivalry.

Main Results:

• The proposed biophysical circuit model successfully explains both spiking variability and perceptual variability during rivalry.
• The model adheres to strict experimental constraints across multiple scales.
• The model demonstrates predictive power regarding how stimulus conditions influence spiking and perceptual variability.

Conclusions:

• A unified biophysical model, based on mutual inhibition, can elucidate the mechanisms underlying both neural and perceptual variability.
• This work bridges the gap between understanding spiking variability and perceptual variability.
• The findings provide a framework for predicting how stimulus changes affect neural and perceptual dynamics.