01. Scientific Curiosity & Motivation
Turn broad interest in brain mapping into concrete, testable connectomics questions with explicit scope and measurable outcomes.
Topic modules
Choose a skill or a workshop topic. Each module shows its development status.
For an ordered introduction, follow the nine-lesson connectomics course. For a group session, use the teaching materials.
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25 modules
02. Research Foundations & the Hidden Curriculum
Make implicit research expectations explicit: lab norms, communication scripts, dataset responsibilities, and building a personal support network.
03. Python and Jupyter for Neuroscience
Hands-on Python and Jupyter skills for reproducible connectomics data exploration, from environment setup through documented analysis workflows.
04. Neuroanatomy for Connectomics
Neuroanatomical fluency for interpreting EM structures across cortical layers and brain regions, with attention to uncertainty and misclassification risks.
05. Electron Microscopy and Image Basics
How EM produces the raw data of connectomics: acquisition principles, common artifacts, and image quality screening for segmentation readiness.
06. Segmentation 101
Core segmentation error taxonomy—merges, splits, boundary errors—and a practical correction workflow with documented quality impact.
07. Proofreading and Quality Control
Proofreading strategies that prioritize scientifically high-impact corrections and maintain reproducible, documented QC standards.
08. Hypothesis Testing in Connectomics
Designing testable connectomics hypotheses with measurable structural outcomes, appropriate null models, and explicit uncertainty limits.
09. Neuron Morphology & Skeletonization
Extracting and interpreting skeleton representations and morphology descriptors from segmented neurons for cell-type reasoning.
10. Network Science & Graph Representation
Representing connectomes as graphs, computing core network metrics, and interpreting results with biological and statistical caution.
11. Synapses and Circuit Logic
Interpreting synaptic organization and local circuit motifs from connectomics data, differentiating robust patterns from reconstruction artifacts.
12. Big Data in Connectomics
Scalable data architecture, query planning, and provenance tracking for petascale connectomics datasets like MICrONS and H01.
13. Machine Learning in Neuroscience
ML workflows for connectomics with controls for data leakage, spatial correlation bias, and biologically meaningful evaluation metrics.
14. Computer Vision for EM
Computer vision methods—from classical filters to deep learning—applied to EM imagery for segmentation support, morphology extraction, and quality diagnostics.
15. LLMs for Patch Analysis
LLM-assisted patch triage and annotation support with human-in-the-loop verification gates to prevent hallucination and unsupported scientific inference.
16. Scientific Visualization for Connectomics
Principled visualization of connectomics structures and analysis results: encoding uncertainty, avoiding misleading representations, and producing publication-ready figures.
17. Scientific Writing for Connectomics
Writing evidence-grounded connectomics manuscripts, clear figure legends, and effective reviewer responses for neuroscience audiences.
18. Data Cleaning and Preprocessing
Reproducible preprocessing workflows from raw connectomics data through analysis-ready releases with integrity checks, QC metrics, and full provenance.
19. Peer Review and Scientific Ethics
Applying peer-review criteria and research-ethics frameworks to connectomics manuscripts, workflows, and collaborative decisions.
20. Statistical Models and Inference
Defensible statistical inference for connectomics: choosing null models, controlling multiplicity in high-dimensional tests, and reporting with explicit assumptions.
21. Reproducibility and FAIR Principles
Operationalizing FAIR principles and reproducibility standards for connectomics datasets, analysis code, and public releases.
22. Scientific Writing & Presentation
Delivering clear scientific talks for technical and mixed audiences without oversimplifying structural evidence, with explicit question-handling norms.
23. Posters, Abstracts, and Conferences
Conference-ready abstracts and posters with explicit hidden-curriculum support for networking, Q&A, and navigating scientific meetings.
24. Career Pathways & Graduate School Prep
Evidence-based career strategy for connectomics: evaluating graduate programs, drafting targeted mentor outreach, and navigating admissions hidden curriculum.
25. Portfolio, Feedback, and Final Project
Capstone portfolio assembly demonstrating end-to-end connectomics competencies with curated artifacts, reflective commentary, and mentor feedback.
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