Chaotic balanced state in a model of cortical circuits

Area of Science:

• Computational neuroscience
• Theoretical neuroscience
• Neural network modeling

Background:

• The origins of temporal irregularity in cortical neuron electrical activity remain unclear.
• Existing models often struggle to replicate the dynamic range and responsiveness observed in vivo.

Purpose of the Study:

• To investigate the hypothesis that balanced excitatory and inhibitory currents explain neuronal temporal irregularity.
• To analyze a network model that captures sparse, strong synaptic connections and external drives.

Main Methods:

• Development and analytical solution of a mean-field theory for a network of excitatory and inhibitory binary units.
• Modeling sparse, random connectivity with strong internal feedback and regular external input.
• Analysis of network dynamics, including stationary states, temporal statistics, and sensitivity to initial conditions.

Main Results:

• Identification of a novel 'balanced state' in large networks where excitatory and inhibitory inputs dynamically balance.
• Demonstration that this balanced state linearizes population responses and enables rapid tracking of time-dependent external inputs.
• Characterization of the balanced state as asynchronous chaotic, with near-Poissonian statistics and broadly distributed, power-law-tailed single-cell activity.

Conclusions:

• Balanced excitation and inhibition are crucial for generating realistic temporal dynamics in cortical networks.
• The balanced state provides a theoretical framework for understanding neuronal responsiveness and information processing.
• This model offers insights into neural computation, contrasting with synchronized states found in other network architectures.