How do synchronized bursts emerge in neural networks? This research investigates the emergence of synchronized burst activity in networks of neurons exhibiting spike adaptation. It demonstrates that networks of tonically firing adapting excitatory neurons can evolve to a state of synchronized bursting. The study analyzes the underlying mechanism in a network of integrate-and-fire neurons, examining the dependence of this state on network parameters. The findings reveal that this mechanism is robust against inhomogeneities, connectivity sparseness, and noise. Decreasing inhibitory feedback can trigger a switch from asynchronous to synchronized bursting. The research highlights a key mechanism driving synchronized activity in neural networks, with implications for understanding brain dynamics and function.
Published in Neural Computation, this paper aligns with the journal's focus on computational approaches to understanding neural systems. It uses mathematical modeling to investigate the dynamics of neural networks, specifically exploring the emergence of synchronized activity. The research contributes to the understanding of how network properties influence neuronal behavior and brain function.