Correlated states in balanced neuronal networks

Area of Science:

• Computational neuroscience
• Neuronal network dynamics
• Synaptic connectivity

Background:

• Neuronal network models with balanced excitation and inhibition typically exhibit weak spike train correlations (the asynchronous state).
• Previous research indicated that specific connectivity structures could induce larger correlations in balanced networks, even with small average correlations.
• Existing models often assume uncorrelated feedforward synaptic input, limiting understanding of correlated input effects.

Purpose of the Study:

• To investigate the impact of correlated feedforward inputs on interneuronal correlations within balanced neuronal networks.
• To analyze the emergent properties of a recurrent network receiving correlated spike train inputs.
• To compare the characteristics of this 'correlated state' with the established 'asynchronous state' and in vivo cortical recordings.

Main Methods:

• Development and analysis of computational models of balanced neuronal networks.
• Simulations incorporating correlated spike trains as feedforward input.
• Mathematical analysis of network dynamics and interneuronal correlation structure.
• Comparison of model outputs with experimental data from in vivo cortical recordings.

Main Results:

• Correlated feedforward inputs significantly increase interneuronal correlations in balanced networks, creating a distinct 'correlated state'.
• This correlated state exhibits a tight excitatory-inhibitory balance, consistent with physiological recordings from the cortex.
• The structure of correlations differs markedly from the asynchronous state, even when average correlations are low.

Conclusions:

• Correlated feedforward input is a crucial factor in generating significant interneuronal correlations in balanced networks.
• The 'correlated state' provides a more biologically plausible model for cortical network activity than the traditional asynchronous state.
• Understanding input correlations is essential for deciphering neuronal communication and network function in vivo.