Bayesian reaction optimization with fixed MaleCNS features: performance, wiring controls, and reward–aversion diagnostics
Year: 2026
Type: preprint
Abstract: Connectome-derived features provide one possible representation for reaction optimization, but their utility need not depend on measured neuronal pairing. We examine a Bayesian predictor using fixed mushroom-body features derived from MaleCNS connectivity, with observed prediction errors separated into engineered reward and aversion signals. An offline evaluator returns only selected recorded outcomes. We distinguish this predictor from earlier models that modify KC–MBON efficacy incrementally or reconstruct it from observed response ranks. The rank comparison changes both the learning head and acquisition rule. In the exploratory predictive-model evaluation, the mean best response over post-initial observations exceeded rank reconstruction by 0.486 percentage points on C–N coupling and 3.346 on Suzuki–Miyaura coupling. Differences from development-tuned Gaussian-process optimization were smaller, and their cluster-bootstrap intervals included zero. Native-versusrewired intervals also included zero. C–N responses were analytical yields; Suzuki responses were UV-area percentages. High/low response diagnostics separate input-dependent KC activity from outcome-dependent prediction updates: circuit activity remains fixed under an identical-cue outcome intervention. Reused datasets and input ensembles, and one rewired graph restrict inference. These results support evaluating anatomical representation, learning rule, search performance, and prediction accuracy separately; they establish neither statistical equivalence nor a unique advantage of native connectivity.
Source: ChemRxiv
Author: Yuuya Nagata
Institution: National Institute for Materials Science
Language: English
Subfield: Cellular and Molecular Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7213460673