Connectome analysis of a cerebellum-like circuit for sensory prediction

TL;DR - A Nature study combines connectomics, electrophysiology, and modelling to examine how a cerebellum-like circuit in electric fish learns sensory predictions. It finds that synaptic plasticity distributed across multiple network layers supports rapid, accurate, and noise-robust learning.

• Maps neural connectivity in an electric fish sensory-prediction circuit.
• Integrates structural data with electrophysiological measurements and computational modelling.
• Shows that plasticity across multiple layers works cooperatively rather than being confined to a single site.
• Links distributed plasticity to learning speed, predictive accuracy, and resilience to noise.