NetFormer: An interpretable model for recovering dynamical connectivity in neural populations
Wuwei Zhang, Ziyu Lu, Trung Le, Hao Wang, Uygar Sumbul, Eric Shea-Brown, Lu Mi · COSYNE 2025
Inferring the underlying connectivity of a network from observations of its activity is a long-standing challenge. In the brain, this challenge is exacerbated by dynamic reconfiguration of effective connectivity, mediated by both synaptic plasticity and neuromodulation. Traditional methods for extracting connectivity from activity focus on modeling static connectivity. On the other hand, recent transformer-based models are able to capture nonstationary interactions, but their deep layered structures and nonlinear attention mechanisms make them challenging to interpret, particularly in terms of
ePoster · Neuroscience