Dynamical models of cortical circuits

- Inhibition-stabilized networks describe the visual cortical operating point.
- Dynamic synapses cause nonlinearity and multi-stability in balanced networks.
- Random networks in the balanced state can support sharp stimulus tuning.
- Emerging mathematical tools facilitate the analysis of circuit generated activity correlations.
- Microstate chaos in balanced networks is highly sensitive to single neuron dynamics.

Cortical neurons operate within recurrent neuronal circuits. Dissecting their operation is key to understanding information processing in the cortex and requires transparent and adequate dynamical models of circuit function. Convergent evidence from experimental and theoretical studies indicates that strong feedback inhibition shapes the operating regime of cortical circuits. For circuits operating in inhibition-dominated regimes, mathematical and computational studies over the past several years achieved substantial advances in understanding response modulation and heterogeneity, emergent stimulus selectivity, inter-neuron correlations, and microstate dynamics. The latter indicate a surprisingly strong dependence of the collective circuit dynamics on the features of single neuron action potential generation. New approaches are needed to definitely characterize the cortical operating regime.

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