Computational Neuroanatomy
Computational Neuroanatomy: A Hands-on Curriculum in Python is a free, self-paced series of runnable notebooks that teaches neuroanatomy in Python using real data. The series is ordered by increasing complexity, from a single reconstruction to atlas-scale analysis and ML-ready ground truth.
Start the curriculum (hosted on Read the Docs)
At a glance
- Audience: neuroscientists and image analysts comfortable with basic Python. No prior SNT or Java experience is needed
- Format: 9 self-contained notebooks, each stating its learning objectives, estimated time, and requirements. About 4 to 5 hours in total
- Data: real reconstructions and volumes (e.g., MouseLight, NeuroMorpho.org, remote OME-Zarr), analyzed with SNT through PySNT alongside NumPy, pandas, NetworkX, napari, and scikit-learn
- Access: open and free, no registration. Notebooks can be read online or downloaded and run locally
Tutorials
T01Single Cell Analysis
T02Visualizing Reconstructions in napari
T03Hemisphere Analysis
T04Volumetric Quantifications
T05Spatial Intersection Between Trees
T06Unsupervised Classification of Projection Neurons
T07Curvature Optimization with Automated Path Tracing
T08Crossover Detection
T09Handling Large-Scale Neuronal Data
Source, license, and feedback
The source of every notebook is on GitHub. Text and figures are licensed under CC BY 4.0, code under the PySNT license, and datasets are subject to their original terms. Corrections, new tutorials, and feedback are welcome on the forum or as issues.
Citing
Ferreira TA, PySNT and SNT contributors. Computational Neuroanatomy: A Hands-on Curriculum in Python. morphonets.org/computational-neuroanatomy, doi:10.5281/zenodo.24090107
See also the SNT citation guidelines.