Speaker
Description
To investigate both the storage of phase-coded oscillatory patterns, and the scale-free behaviour near the transition between the replay and not-replay regimes, 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) 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. The scale-free avalanches near the edge of instability are studied. Our results offer insight into how spatiotemporal coding and network organization support robust, large-scale memory storage and replay, and characterize the regime close to the transition. Empirical MEG data are compared with prediction of the model informed with tractography.
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