Modularity-dependent storage of dynamic spiking patterns: Bridging micro- and mesoscopic representations

Biological systems rely on asynchronous and temporally overlapping dynamics, allowing for the concurrent activation of multiple processes. This principle is particularly evident in brain function, where cognitive tasks engage distributed, interacting regions rather than sequentially isolated ones. To investigate the mechanisms enabling such coordination, we study a modular spiking neural network composed of leaky integrate-and-fire neurons and governed by spike-timing-dependent plasticity. Our model stores modular spatiotemporal patterns both at the mesoscopic level (sequences of modules) and at the microscopic level (precise spike timings) and includes a parameter, η, which regulates the degree of temporal overlap between modules' activations. By tuning η, the network transitions from sequential to overlapping regimes, ranging from synfire chainlike dynamics to fully co-activated modules. We investigate how the temporal structure influences the network's capacity to encode and selectively retrieve multiple dynamical patterns while considering biological constraints such as the cost of long-range connectivity. Our results offer insight into how spatiotemporal coding and network organization support robust, large-scale memory storage and replay.