Journal Paper Collection
A curated library of ~100 essential connectomics papers organized across 11 dimensions — from ultrastructure to MRI — and tagged for cross-referencing with the content library. Each paper includes:
- Three-level summaries — beginner (no prerequisites), intermediate (familiar with basics), advanced (active researcher)
- Tags — linking papers to the content library tag taxonomy for combinable micro lessons
- Key figures — which figures to focus on and what they show
- Discussion prompts — ready-to-use journal club questions
- Related content — links to content library entries for deeper context
All papers are also available as structured data in _data/journal_papers.yml for programmatic filtering by dimension, tag, or expertise level.
By Dimension
EM-Scale Connectomics
| Dimension | Papers | Focus |
|---|---|---|
| Neuroanatomy | 8 | Ultrastructure, synapses, spines, organelles, serial reconstruction |
| Imaging & Sample Preparation | 8 | SBEM, FIB-SEM, ATUM, tissue preparation, acquisition pipelines |
| Computer Vision & ML | 10 | Segmentation (FFN, U-Net, affinity), synapse detection, error correction |
| Data Storage & Pipelines | 8 | CAVE, neuPrint, CATMAID, OME-Zarr, cloud storage, pipeline engineering |
| Proofreading & QC | 8 | Crowd-sourced proofreading, error detection, agglomeration, QA metrics |
| Cell Types & Classification | 8 | Morphological, transcriptomic, connectivity-based classification |
Graph Analysis & Network Science
| Dimension | Papers | Focus |
|---|---|---|
| Graph Construction & Representation | 8 | Graph encoding, comparative connectomics, structure-function |
| Network Analysis & Statistics | 10 | Motifs, community detection, graph matching, null models, NBS |
MRI & Macro-Scale
| Dimension | Papers | Focus |
|---|---|---|
| MRI Connectomics | 12 | Diffusion tractography, functional connectivity, HCP, parcellation |
Cross-Cutting
| Dimension | Papers | Focus |
|---|---|---|
| NeuroAI & Modeling | 8 | Structure-function, bio-inspired AI, connectome-constrained models |
| Datasets & Case Studies | 10 | C. elegans, FlyWire, MICrONS, H01, landmark projects |
Total: ~98 papers across 11 dimensions.
How to Use This Collection
For self-study
Start with the beginner summary to orient yourself, then read the paper, then compare your understanding with the intermediate and advanced summaries. Use the key figures list to focus your reading.
For journal club
Use the discussion prompts to structure group discussion. The three-level summaries help facilitators calibrate discussion depth for mixed-expertise groups. See the Technical Track Journal Club for scheduling guidance.
For micro lesson design
Use tags to find papers that align with specific content library entries. The combines_with field on content library entries and the Related content links on papers create a cross-referenced web for assembling multi-resource micro lessons.
For course design
Papers are organized to follow the technical training sequence. Each dimension aligns with specific technical training units:
| Dimension | Primary units |
|---|---|
| Neuroanatomy | 05, 06 |
| Imaging | 03 |
| Computer Vision & ML | 04, 08 |
| Data Storage & Pipelines | 04, 08 |
| Proofreading | 08 |
| Cell Types | 05, 06, 07 |
| Graph Construction | 09 |
| Network Analysis | 09 |
| MRI Connectomics | 01, 02 |
| NeuroAI | 09 |
| Case Studies | 01, 02, 08, 09 |
Expertise Level Guide
| Level | Assumes | Best for |
|---|---|---|
| Beginner | No neuroscience or connectomics background | New trainees, interdisciplinary collaborators, public engagement |
| Intermediate | Familiar with EM, basic neuroscience, and computational concepts | Graduate students, postdocs entering the field |
| Advanced | Active researcher or advanced trainee | Methodological deep dives, experimental design, peer review |
Structured Data Access
All papers are stored as structured YAML records in _data/journal_papers.yml. Each record contains:
- id: paper-id
title: "Paper title"
authors: "Author list"
year: 2024
journal: "Journal name"
doi: "10.xxxx/xxxxx"
dimension: dimension-name
tags: [dimension:tag1, dimension:tag2]
key_figures: ["Fig. 1: description"]
discussion_prompts: ["Prompt 1", "Prompt 2"]
related_content: [/content-library/path/]
summaries:
beginner: "Plain-language summary"
intermediate: "Technical summary"
advanced: "Expert summary with methodological detail"
This enables programmatic filtering, e.g., “show all papers tagged case-studies:FlyWire with beginner summaries” or “find papers related to /content-library/proofreading/error-taxonomy/”.