How this library works
Every entry below is a standalone, richly detailed reference document. Each contains:
- Full instructor-ready narrative (not just bullet points)
- Real scientific references with context
- Worked examples with step-by-step reasoning
- Common misconceptions and how to address them
- Tags for cross-referencing across dimensions (using the taxonomy in
_data/content_tags.yml) - Reference images with alt text and captions (placeholders for teaching illustrations)
- Micro lesson IDs for combining entries into modular learning sequences
- Combines-with links identifying entries that pair well for combined micro lessons
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.
Tag dimensions
Content is tagged across 9 dimensions for flexible combination:
| Dimension | Color | Example tags |
|---|---|---|
| Neuroanatomy | #4A90D9 | soma, dendrite, axon, synapse, spine, organelle |
| Imaging | #7B68EE | electron-microscopy, SEM, TEM, FIB-SEM, SBEM, ATUM |
| Infrastructure | #E67E22 | pipeline, segmentation, alignment, CAVE, neuroglancer |
| Proofreading | #E74C3C | merge-error, split-error, QA-metrics, expected-run-length |
| Cell Types | #27AE60 | neuron-classification, glia, pyramidal-cell, interneuron |
| Connectomics | #F39C12 | graph-theory, motif, community-detection, hub, modularity |
| NeuroAI | #9B59B6 | structure-function, bio-inspired-architecture, deep-learning |
| Case Studies | #1ABC9C | FlyWire, MICrONS, H01, C-elegans, Drosophila |
| Methodology | #95A5A6 | experimental-design, reproducibility, benchmark, ground-truth |
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 |
Journal paper collection
~100 curated papers across 11 connectomics dimensions, each with summaries at beginner, intermediate, and advanced expertise levels. All papers are also available as structured data in _data/journal_papers.yml for programmatic filtering. See the full collection.
| Dimension | Papers | Focus |
|---|---|---|
| Neuroanatomy | 8 | Ultrastructure, synapses, spines, serial reconstruction |
| Imaging | 8 | SBEM, FIB-SEM, ATUM, acquisition pipelines |
| Computer Vision & ML | 10 | FFN, U-Net, affinity prediction, synapse detection |
| Data Storage & Pipelines | 8 | CAVE, neuPrint, CATMAID, OME-Zarr, pipeline engineering |
| Proofreading & QC | 8 | Error detection, crowd-sourced correction, QA metrics |
| Cell Types | 8 | Morphological, transcriptomic, connectivity-based classification |
| Graph Construction | 8 | Graph encoding, comparative connectomics |
| Network Analysis | 10 | Motifs, community detection, graph matching, NBS |
| MRI Connectomics | 12 | Diffusion tractography, functional connectivity, HCP |
| NeuroAI | 8 | Structure-function, bio-inspired AI, model taxonomy |
| Case Studies | 10 | C. elegans, FlyWire, MICrONS, H01, landmark datasets |