Singapore General Hospital

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bioRxiv Oct 29, 2026
A fixed fly connectome learns to remember: the cue hides in neuronal adaptation and is read only in the brain state of learning
A spiking model built from the complete central nervous system connectome of a male fly learns to plan moves and to remember a visual cue while its wiring stays fixed. The cue is not held by persistent firing but written into neuronal spike-frequency adaptation, kept silent through a blank delay and read out by the next input. Recall works only in the brain state of learning: the same trace is nearly reversed at an untrained delay, and a lesion mimicking the Kv4 loss caused by Aβ42 rewrites the trace and its reading, while learning under the lesion is spared.
A spiking model built from the complete central nervous system connectome of a male fly learns to plan moves and to remember a visual cue while its wiring stays fixed. The cue is not held by persistent firing but written into neuronal spike-frequency adaptation, kept silent through a blank delay and read out by the next input. Recall works only in the brain state of learning: the same trace is nearly reversed at an untrained delay, and a lesion mimicking the Kv4 loss caused by Aβ42 rewrites the trace and its reading, while learning under the lesion is spared.
Sheng Jing Hospital Maastricht University Singapore General Hospital Agency for Science, Technology and Research Shanghai Traditional Chinese Medicine Hospital Guangdong Medical College Affiliated Hospital of Guangdong Medical College Hospital
Han Wang（First author）Chunjie Tian（Corresponding author）
Spiking Neural Network Digital Twin Working Memory Drosophila Connectome Spike-Frequency Adaptation
Nature Medicine Oct 29, 2026
A medical vision-language model that scores 0.895 on diagnosis and points back to the lesion
Medical imaging foundation models have traded generality against pixel-level precision, and most return a label without evidence. EVLF-FM feeds both disease-level and pixel-level visual features into a language model, first with supervised fine-tuning and then with reinforcement learning on verifiable rewards, spanning six specialties and 10 imaging modalities. On internal diagnostic validation it reached the highest mean accuracy among evaluated models, 0.895, and averaged 0.842 Acc@0.5 for medical visual grounding across nine modalities.
Medical imaging foundation models have traded generality against pixel-level precision, and most return a label without evidence. EVLF-FM feeds both disease-level and pixel-level visual features into a language model, first with supervised fine-tuning and then with reinforcement learning on verifiable rewards, spanning six specialties and 10 imaging modalities. On internal diagnostic validation it reached the highest mean accuracy among evaluated models, 0.895, and averaged 0.842 Acc@0.5 for medical visual grounding across nine modalities.
SingHealth Community Hospitals Stanford University Catholic University of Korea National University of Singapore Singapore National Eye Center Hokkaido University Nanyang Technological University Aier Eye Hospital Group (China)
Yang Bai（First author）Darren Shu Jeng Ting（Corresponding author）Daniel Shu Wei Ting（Corresponding author）
EVLF-FM Visual Grounding Medical Visual Question Answering GRPO Explainable AI

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A fixed fly connectome learns to remember: the cue hides in neuronal adaptation and is read only in the brain state of learning

A medical vision-language model that scores 0.895 on diagnosis and points back to the lesion