Regime Mapping of Oscillatory States in Balanced Spiking Networks with Multiple Time Scales
This provides a reference for operating-point selection and synchrony modulation in biologically grounded spiking-network modeling, but it is incremental as it builds on existing balanced-network frameworks.
The researchers systematically mapped how synaptic decay, conduction delay, and plasticity rate jointly shape oscillatory states in balanced spiking networks, finding that increasing plasticity expands oscillatory regions toward shorter decay times and moderate-to-long delays, with identified parameter regions showing strongest rhythmic coherence.
Balanced spiking networks can transition between silent, asynchronous-irregular, and oscillatory states depending on interacting synaptic and temporal time scales, while their joint parameter structure remains incompletely characterized. In this work, we systematically map how postsynaptic decay (Ïs), conduction delay (d), and plasticity rate (λp) jointly shape oscillatory regimes in recurrent leaky integrate-and-fire networks. By combining Brian2 simulations across the (Ïs, d, λp) space with a coarse Hopf-reference boundary, we construct regime maps that directly visualize SIL-AI-OSC transitions and corresponding spectral prominence landscapes. The mapped results show that increasing λp expands oscillatory regions toward shorter Ïs and moderate-to-long delays, while prominence maps identify parameter regions with the strongest rhythmic coherence. Representative control experiments further connect this global landscape to local rhythm-forming mechanisms, showing that STDP freezing weakens rhythmic coherence whereas delay jitter enhances it with minimal change in mean firing rate. As a result, these findings provide a useful reference for operating-point selection, synchrony modulation studies, and future biologically grounded spiking-network modeling within similar balanced-network settings.