Data availabilityOriginal data and/or segmentations for the EM dataset are available in browsable and API-accessible (precomputed) format (https://github.com/google/neuroglancer). The dedicated web page for this project (http://efish-public.storage.googleapis.com/index.html) provides the public Google Storage addresses for each component of the EM dataset. Processed data and supporting files needed to reproduce the published results are deposited in a public Zenodo repository (https://doi.org/10.5281/zenodo.19892261)65. This repository contains any processed connectomics data, generated modelling data, electrophysiology data and supporting files that have not been previously published. Source data are provided with this paper.Code availabilityThe dedicated web page for this project (http://efish-public.storage.googleapis.com/index.html) provides links to all code (written in Python, v.3.8 and higher) used to analyse the segmented, agglomerated and proofread EM datasets and all code (written in Matlab 2024b, MathWorks) necessary for implementing the models used in this study. All code not previously published (along with documentation) is available from GitHub (https://github.com/neurologic/efish_em_ELL) and is additionally archived in Zenodo (https://doi.org/10.5281/zenodo.19892261)65. This Zenodo archive includes custom Python code used for dataset processing and analysis and custom MATLAB code used for modelling. All previously published code used is cited in the text.ReferencesMarblestone, A. H., Wayne, G. & Kording, K. P. Toward anintegration of deep learning and neuroscience. Front Comput. Neurosci. 10, 94 (2016).Article