Technical Training Course Hub
Canonical connectomics sequence from imaging to NeuroAI.
Core Concepts & Methods
Build scientific and technical fluency in nanoscale connectomics from motivation through analysis methods.
Fadel alignment: Knowledge, Skills
Technical Slides and Lecture Drafts
Lecture-ready sequence with slide-source links.
Journal Club Reading List
Required and optional readings aligned to the technical sequence.
Connectomics Dictionary
Shared vocabulary for methods, quality, and analysis.
Datasets
Data resources for training and research.
Concepts in This Track
Filter concepts by immediate need to find the most relevant next resources.
Hypothesis Framing
Track: core-concepts-methods
User needs: starting a research question, avoiding overclaiming
Translate broad brain questions into testable structural hypotheses with clear evidence boundaries.
How to learn it: Start with one biological question, define measurable structural outputs, then state explicit non-claims.
Teaching set:
- Lesson: Unit 01 Lesson
- Activity: Unit 01 Quick Activity
- Slides: Unit 01 Slides
- References: Journal Club Reading List
Scale Selection
Track: core-concepts-methods
User needs: matching method to question, planning compute and storage
Choose imaging and analysis scale that can resolve required features at manageable cost.
How to learn it: Match your hypothesis to the smallest sufficient resolution and volume, then budget compute before data acquisition.
Teaching set:
- Lesson: Unit 02 Lesson
- Activity: Unit 02 Quick Activity
- Slides: Unit 02 Slides
- References: Workflow Overview
EM Artifacts and QA
Track: core-concepts-methods
User needs: improving data quality, debugging acquisition issues
Identify acquisition artifacts and define practical QA gates before reconstruction.
How to learn it: Use a shared artifact taxonomy and pass/fail thresholds so acquisition issues are caught before reconstruction.
Teaching set:
- Lesson: Unit 03 Lesson
- Activity: Unit 03 Quick Activity
- Slides: Unit 03 Slides
- References: Connectome Quality Tool
Reconstruction Architecture
Track: core-concepts-methods
User needs: building robust pipelines, reproducible processing
Design scalable ingest-to-serving systems with lineage, release, and rollback discipline.
How to learn it: Treat reconstruction as production infrastructure: lineage, observability, and rollback are core scientific requirements.
Teaching set:
- Lesson: Unit 04 Lesson
- Activity: Unit 04 Quick Activity
- Slides: Unit 04 Slides
- References: Framework Overview
Ultrastructure Annotation
Track: core-concepts-methods
User needs: reading EM confidently, improving annotation consistency
Use compartment, organelle, and synaptic cues to make reproducible interpretation decisions.
How to learn it: Combine multiple ultrastructural cues across slices and label uncertainty explicitly instead of forcing hard calls.
Teaching set:
- Lesson: Unit 05 Lesson
- Activity: Unit 05 Studio Activity
- Slides: Unit 05 Slides
- References: Connectomics Dictionary
Axon vs Dendrite Classification
Track: core-concepts-methods
User needs: reducing identity confusion, handling ambiguity
Apply multi-cue decision rules and confidence tags for process-type calls.
How to learn it: Use morphology plus context cues to classify neurites and escalate ambiguous cases consistently.
Teaching set:
- Lesson: Unit 06 Lesson
- Activity: Unit 06 Quick Activity
- Slides: Unit 06 Slides
- References: Unit 08 QC Context
Glia Identification
Track: core-concepts-methods
User needs: reducing glia-neuron boundary errors, interpreting myelin context
Distinguish major glia classes and integrate glia decisions into high-value QC workflows.
How to learn it: Focus on glial ultrastructure signatures and boundary integrity to reduce high-impact proofreading errors.
Teaching set:
- Lesson: Unit 07 Lesson
- Activity: Unit 07 Quick Activity
- Slides: Unit 07 Slides
- References: Connectome Quality Tool