arXiv:2603.16884v2 Announce Type: replace
Abstract: This work introduces a statistical procedure to infer the interaction graph of neuronal networks modeled by Galves-L”ocherbach dynamics. The methodology performs bivariate inference, identifying synaptic links from the spike trains of pairs of neurons without observing the rest of the network.
We propose a Macro-Micro Extrapolation algorithm to address data sparsity by inferring interactions in the limit $Delta to 0^+$. The core component is a Spike-Triggered Estimator that leverages the local reset property to decouple synaptic jumps from background noise. By employing an adaptive logic that switches between sample averaging and Pyramid Extrapolation, the framework categorizes connections as excitatory, inhibitory, or null.
Numerical simulations demonstrate that the classifier identifies synapses without error across varying noise regimes and complex network topologies, even for observation windows broader than those predicted by the current theoretical bounds.
Rationale and methods of the MOVI-HIIT! cluster-randomized controlled trial: an avatar-guided virtual platform for classroom activity breaks and its impact on cognition, adiposity, and fitness in preschoolers
IntroductionClassroom-based active breaks (ABs) have been shown to reduce sedentary time and increase physical activity in primary school children; however, evidence regarding their effects on