Neuronal Stochastic Attention Circuit (NSAC) for Probabilistic Representation Learning
Reliable uncertainty quantification in continuous-time (CT) representation learning remains nascent, particularly within CT attention literature. We introduce the Neuronal Stochastic Attention Circuit (NSAC), a novel biologically-inspired CT attention architecture that reformulates attention logit computation as the solution of an Ornstein-Uhlenbeck stochastic differential equation modulated by input-dependent, nonlinear interlinked gates derived from repurposed C. elegans Neuronal Circuit Policies (NCPs) wiring mechanism. It induces a Gaussian distribution over logits that propagates principled stochasticity through a logistic-normal distribution over attention weights to yield probabili...
| Authors | Waleed Razzaq, Yun-Bo Zhao |
|---|---|
| Primary category | cs.LG |
| Published | 25 May 2026 |
| Last updated at source | 19 June 2026 |
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