How this library works

Every entry below is a standalone, richly detailed reference document. Each contains:

Unit pages, slide decks, and modules link to these entries rather than duplicating content. This keeps the curriculum DRY (Don’t Repeat Yourself) and ensures a single source of truth for each topic.


Neuroanatomy

Ultrastructural biology of neurons as seen in electron microscopy.

Entry Scope Primary units
Soma ultrastructure Nuclear envelope, Nissl substance, Golgi, lipofuscin; EM identification 05
Dendrite biology Spine types, PSDs, microtubule organization, local translation 05, 06
Axon biology AIS, myelinated segments, boutons, vesicle pools, active zones 05, 06
Synapse classification Gray Type I/II, asymmetric vs symmetric, cleft structure 05, 08
Organelle annotation cues Mitochondria, ER, MVBs, lysosomes as compartment indicators 05, 06
Myelin and nodes of Ranvier Compact myelin, paranodal loops, Schmidt-Lanterman incisures 05, 06

Proofreading

Quality control of automated segmentation at connectome scale.

Entry Scope Primary units
Error taxonomy Merge, split, boundary, and identity errors with examples 08
Proofreading strategies Exhaustive, targeted, priority-ranked, crowd-sourced approaches 08
Proofreading tools CAVE, Neuroglancer, FlyWire, NeuTu; editing operations 08
Metrics and QA VI, ERL, edge F1, synapse-centric F1 with formulas 08
Worked examples Step-by-step correction scenarios for merge, split, synapse errors 08

Connectomics

Graph analysis, motif search, and the bridge to NeuroAI.

Entry Scope Primary units
Connectome history C. elegans through FlyWire and MICrONS; milestones and lessons 01, 09
Graph representations Nodes, edges, weights, adjacency matrices, multigraphs 09
Network analysis methods Degree, clustering, path length, community detection, spectral 09
Motif analysis DotMotif, null models, subgraph isomorphism, statistics 09
NeuroAI bridge Structure-function, bio-inspired architectures, connectome-constrained models 09

Imaging

EM acquisition, image formation, and artifact management.

Entry Scope Primary units
EM principles Beam physics, contrast mechanisms, SEM vs TEM, resolution limits 03
Artifact taxonomy Knife chatter, charging, folds, tears, drift; downstream impact 03, 05
Tissue preparation Fixation, heavy-metal staining, embedding, sectioning strategies 03
Acquisition QA Per-tile QC, pilot reconstructions, metadata requirements 03

Infrastructure

Reconstruction pipelines, data formats, and reproducibility.

Entry Scope Primary units
Reconstruction pipeline Ingest, alignment, segmentation, agglomeration, serving 04
Data formats and representations Volumes, meshes, skeletons, graphs; when to use each 02, 04
Provenance and versioning Lineage metadata, CAVE materialization, reproducible reprocessing 04, 08

Cell types

Identification and classification of neuronal and glial cell types in EM.

Entry Scope Primary units
Axon-dendrite classification Multi-cue discrimination, edge cases, confidence scoring 06
Glia recognition Astrocytes, microglia, oligodendrocytes; boundary ambiguities 07
Neuron type identification Morphological and connectivity-based classification 05, 06, 09

Case studies

Deep dives into landmark connectomics projects.

Entry Scope Primary units
FlyWire whole-brain connectome 140K neurons, collaborative proofreading, brain-wide circuit analysis 08, 09
MICrONS visual cortex mm³ mouse cortex, functional connectomics, structure-function linking 01, 03, 08, 09
H01 human cortex Petavoxel human fragment, unique challenges, pathological features 05, 08
C. elegans revisited The first connectome, re-analysis, developmental connectomics 01, 09
MouseConnects HI-MC NIH CONNECTS flagship, 10 mm³ hippocampus, ongoing project 01, 04