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What this is

Three complete graduate lectures on nanoscale connectomics, released for anyone to teach. Each is roughly 150 minutes of material in three parts of about 50 minutes, with speaker notes, in-class discussion prompts, and a graded artifact.

They were written for EN.585.781 Frontiers in Neuroengineering at Johns Hopkins, where they run as modules 7–9. Nothing in them depends on that course: the sequence is self-contained and assumes only introductory neuroscience.

This is a different kind of page from the technical training slides, which are build plans for an instructor assembling their own lecture. These are the lectures themselves — written to be projected.

The three lectures

1 · Introduction to Connectomics

59 slides. Why synapse-resolution structure needs electron microscopy, what a wiring diagram can and cannot establish, the three scales, and the state of the field through 2025.

2 · Tools and Methods

56 slides. How tissue becomes a queryable petascale dataset: preparation and its artifacts, storage and infrastructure, and the versioning that makes a result reproducible.

3 · Algorithms and Applications

58 slides. Segmentation and its error taxonomy, proofreading triage, graph construction and null models, and an honest account of connectomics and machine learning.

What holds the three together

One discovery pipeline. Question → specimen → image → reconstruction → graph → claim. The same diagram opens every lecture, marked to show which columns that lecture owns. Students learn to read it left to right as how a dataset is built, and right to left as how you debug a number you do not believe.

Eight progression streams. Scale; throughput and automation; segmentation quality; modality integration; organism and lifespan coverage; structure → function; openness and community; translation and people. Introduced in lecture 1, tagged into the milestone table, and revisited as a scorecard at the end of lecture 3. The point is that progress in this field is not one curve, and that reading a paper by asking which stream does this advance is more useful than ranking it.

One claim discipline. Every connectivity claim sorts into evidence supported by structure alone, structure plus a declared assumption, or not supportable by structure at all. It is introduced in lecture 1 and is what the lecture 3 lab is graded against.

A note on scope. Diffusion MRI and X-ray microtomography appear once, as the example that different questions need different instruments. These lectures are about what only synapse-resolution structure can establish.

Teaching from them

Timing. Three parts of roughly 50 minutes, with natural breaks at the part dividers. A 90-minute slot takes Parts A and B; Part C works as a standalone seminar. A single 75-minute survey can be built from lecture 1 Part A plus lecture 3 Part C.

What students produce. One artifact per lecture, each building on the last:

Lecture Artifact What it must contain
1 Study brief A measurable endpoint, a null model, an explicit non-claim
2 Reproducible query A pinned materialization version, stated inclusion criteria, one stated limitation
3 Analysis card Hypothesis, estimand, null model, success criterion, error band, non-claim, provenance

Prerequisites. Introductory neuroscience helps and is not required. Lecture 2’s assignment assumes basic Python; the lecture itself does not.

No data or accounts needed to teach. The lectures reference public platforms — neuPrint, FlyWire Codex, BossDB, CAVE — but nothing in the slides requires a login. The lecture 2 assignment does.

Formats

Each lecture page links its rendered HTML deck, which presents directly in a browser. The Marp markdown source is the version to take if you want to work with the text — speaker notes are in the source as HTML comments and export to PowerPoint notes.

For Google Slides, render to PowerPoint and use File → Import slides. Be aware that Marp’s PowerPoint export renders each slide as an image, so text is not editable in Slides. If you want to change wording, edit the markdown and re-render.

git clone https://github.com/wrgr/neurotrailblazers
cd neurotrailblazers
npm install --no-save @marp-team/marp-cli
./scripts/render_marp.sh --pptx

Exports land in course/decks/marp/out/en585781/.

Licence

Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0). https://creativecommons.org/licenses/by-sa/4.0/

You may teach from these lectures in any setting, including commercially; copy and redistribute them in any medium; and re-cut, shorten, translate, restyle, or merge them into your own material — and distribute the result. No permission needed.

Two conditions. Attribution — credit the original, link the licence, and indicate whether you changed anything. ShareAlike — if you adapt the material, distribute your version under this same licence, so adaptations stay as open as what they were built from.

How to credit:

Gray Roncal, W. (2026). Introduction to Connectomics (EN.585.781 Frontiers in Neuroengineering, Module 7). NeuroTrailblazers. CC BY-SA 4.0. https://neurotrailblazers.org/teaching/lectures/

For an adaptation, prefix with “Adapted from” and note what you changed. A credit line on a title slide or in a syllabus is sufficient.

If your adaptation improves the teaching, the project would like to hear about it — open an issue. Re-cut versions for different course lengths are exactly the thing worth sharing back.

On the contents. These decks contain no third-party figures — they are text, tables, and ASCII diagrams — so redistributing or adapting them raises no image-licensing questions, and nothing in them is encumbered by a licence incompatible with ShareAlike. They cite published work extensively; citation is not reproduction, and the cited papers carry their own licences. If you add figures to an adaptation, check they are compatible with CC BY-SA 4.0 before distributing it.

Corrections

The field moves quickly, and “state of the field” claims age. If you find something wrong or out of date, please open an issue — corrections from people teaching this material are the most useful kind.