Cost-Efficient Neural Dynamics: Reconciling Multilevel Spontaneous and Evoked Activity in E-I Balanced Neural Networks at Criticality

Explore the intricate relationship between cost-efficiency and multi-level neural dynamics in this 47-minute conference talk by Changsong Zhou from PCS Institute for Basic Science. Delve into a comprehensive analysis of how irregular spiking, oscillations, and critical avalanches in cortical neural circuits can be reconciled within a biologically plausible neural network model. Discover how excitation-inhibition balance and realistic synaptic conductance dynamics contribute to achieving minimal energy cost and maximal information capacity efficiency. Examine the proposed semi-analytical mean-field theory that governs network macroscopic dynamics and learn about the critical state characterized by irregular individual spiking. Investigate the impact of network topology on cost-efficiency and explore how the model accounts for various reliable neural response features observed in experiments. Gain insights into complex neural dynamics in information processing and potential applications in brain-inspired artificial intelligence.