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NetFormer: An interpretable model for recovering dynamical connectivity in neuronal population dynamics

ICLR 2025 · Probabilistic and scientific modeling › Dynamical systems · approach: Low-rank and diffusion neural dynamics

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Introduces NetFormer, an interpretable transformer-style model that recovers time-varying (dynamical) connectivity from neuronal population activity recordings.

Problem

Existing methods for inferring neuronal network structure from activity recordings model only static connectivity and ignore nonstationary aspects such as ongoing synaptic plasticity and neuromodulation.

Approach

An interpretable model that recovers dynamical (time-varying) connectivity from neuronal population dynamics, explicitly accounting for nonstationary synaptic plasticity and neuronal modulation rather than a single static connectivity matrix.

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