Asynchronous and Coherent Dynamics in Balanced Excitatory-Inhibitory Spiking Networks

Area of Science:

• Computational Neuroscience
• Neural Dynamics
• Network Science

Background:

• The dynamic excitatory-inhibitory (E-I) balance is a key concept explaining irregular, low-firing activity in the cortex.
• Existing models primarily focus on E-I balance as a generator of sparse neural activity.
• The potential for E-I balance to produce other complex neural dynamics remains less explored.

Purpose of the Study:

• To investigate diverse neural activity regimes beyond irregular firing, arising from E-I balance in sparse networks.
• To classify asynchronous and coherent behaviors in heterogeneous E-I networks using computational and analytical methods.
• To explore the mechanisms underlying collective oscillations and coherent chaos in balanced neural systems.

Main Methods:

• Extensive simulations of sparse excitatory-inhibitory (E-I) networks composed of N spiking neurons.
• Analytical investigations using low-dimensional neural mass models and bifurcation analysis.
• Mean-field (MF) analysis to characterize asynchronous regimes in the limit N >> K >> 1.

Main Results:

• Identified supra- and sub-threshold asynchronous regimes characterized by neuron splitting into silent, fluctuation-driven, and mean-driven groups.
• Observed coherent rhythms including periodic/quasi-periodic collective oscillations (COs) and coherent chaos.
• Discovered two mechanisms for COs: Pyramidal-Interneuron Gamma (PING)-like oscillations and fluctuation-driven COs, including novel frequency locking.

Conclusions:

• E-I balance can generate a broader spectrum of neural activity, including complex coherent rhythms, not just irregular firing.
• Network heterogeneity and intrinsic fluctuations play crucial roles in shaping neural dynamics and enabling novel phenomena like fluctuation-driven frequency locking.
• The findings offer insights into the generation of complex spatio-temporal patterns observed in heterogeneous neural circuits.