By Changsong Zhou (Hong Kong, PRC).

The brain is highly energy consuming, therefore is under strong selective pressure to achieve cost- efficiency in both cortical connectivity and activity. Cortical neural circuits display highly irregular spiking in individual neurons but variably sized collective firing, oscillations and critical avalanches at the population level, all of which have functional importance. It is not clear how cost-efficiency is related to ubiquitously observed multi-level properties of irregular firing, oscillations and neuronal avalanches. In this talk, I will introduce our work demonstrating that prominent multilevel neural dynamics properties can be simultaneously reconciled in a generic, biologically plausible neural circuit model that captures excitation-inhibition balance and realistic dynamics of synaptic conductance. Their co-emergence achieves minimal energy cost as well as maximal energy efficiency on information capacity, when neuronal firings are maintained in the form of critical neuronal avalanches. We propose a semi-analytical mean-field theory to derive the field equations governing the network macroscopic dynamics. It reveals that the critical state E-I balanced state of the network manifesting irregular individual spiking is characterized by a macroscopic stable state, which can be either a fixed point or a periodic motion and the transition is predicted by a Hopf bifurcation in the macroscopic field. An analysis of the impact of network topology from random to modular networks shows that local dense connectivity under E-I balanced dynamics appears to be the key “less-is-more” solutions to achieve cost-efficiency organization in neural systems. In the presence of external stimuli, the model at criticality can simultaneously account for various reliable neural response features observed in experiments.

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