Tutorials

Worked examples that take you from loading your first neuron all the way to plotting, morphological analysis, NBLAST and machine learning. Every tutorial is a self-contained script you can read here or download and run yourself.

New to NAVis?

Start with The Basics and work your way down.

General Tutorials

Get to grips with the core concepts: neuron types, lists of neurons and masking.

The BasicsGet a feel for NAVis: load a neuron, inspect it and run basic operations. Neuron TypesThe four neuron types — skeletons, meshes, dotprops and voxels — and when to use each. Lists of NeuronsWork with many neurons at once using NeuronLists: indexing, filtering and batch operations.

Import / Export

Load and save neurons across the many file formats and data sources NAVis supports.

SkeletonsLoad and save skeletons from SWC and other formats, or build them from scratch. MeshesLoad and save mesh neurons in OBJ, PLY, STL and other formats. DotpropsCreate dotprops — point-and-vector representations — from skeletons, meshes or raw points. PicklingQuickly cache and reload neurons with Python's pickle module. Skeletons from light-level dataExtract skeletons from confocal microscopy image stacks.

Plotting

Visualize neurons in 2D and 3D — and fine-tune every detail of how they look.

Plotting OverviewOverview of 2D and 3D plotting and how NAVis' rendering backends compare. ColoringControl neuron colors, palettes and colormaps in your plots. Neuron "Barcodes"Visualize a neuron's branching pattern as a topological "barcode". Neuron TopologyPlot neurons as abstract topology graphs using several layout algorithms. Fine-tuning SkeletonsFine-tune skeleton plots: colors, line width, radii and more. Depth-coloringAdd a sense of depth by coloring neurons along the viewing axis. Cortical NeuronsRecreate a published figure of cortical GABAergic neurons arranged by soma depth. XKCD StyleRender neurons in the hand-drawn XKCD sketch style, just for fun.

Morphology

Measure, analyze and manipulate the morphology of your neurons.

Manipulate MorphologyPrune, resample, smooth and reshape neuron morphology. Analyzing Neuron MorphologyMeasure morphometrics — segment lengths, geodesic distances and branch angles. Label PropagationSpread a sparse set of labels across a neuron using its topology. Axon-Dendrite SplitsSplit a neuron into axon and dendrite from synapse flow centrality.

Interfaces

Connect NAVis to external tools like the NEURON simulator and Blender.

NEURON simulatorRun compartment-model simulations of neurons and networks via the NEURON simulator. Visualize NEURON modelVisualize a NEURON compartment model in 3D. Blender 3DDrive Blender 3D from NAVis for high-quality neuron renders.

Remote Data Sources

Fetch neurons straight from remote sources such as neuPrint, the MICrONS datasets and the Insect Brain DB.

remote dataneuPrintQuery and fetch neurons and connectivity from a neuPrint server. remote dataNeuroglancer & CloudVolumePull neurons and meshes from Neuroglancer sources via CloudVolume. remote dataThe MICrONS DatasetsFetch and explore neurons from the MICrONS EM datasets. remote dataInsect Brain DBFetch neurons and brain meshes from the Insect Brain Database. remote dataH01 DatasetExplore the H01 human cortex EM dataset. remote dataBrain Image LibraryFetch neurons from the Brain Image Library (BIL).

NBLAST

Compare neuron morphology at scale with NBLAST.

NBLASTCompare neuron morphology with NBLAST — the concepts and a first run. Custom score matricesTrain a custom NBLAST scoring matrix from your own data. NBLAST against FlyCircuitMatch a query neuron against the entire FlyCircuit light-level dataset. NBLAST using light-level dataMatch light-level neurons against EM skeletons with NBLAST.

Misc

Assorted tutorials that don't fit neatly elsewhere — spatial transforms and multiprocessing.

MultiprocessingSpeed up batch workloads with built-in parallel processing. TransformationsMove neurons between brain templates and mirror them across the midline.

Machine Learning

Turn variable-sized neurons into fixed-size, canonically-posed model inputs — normalize, sample and augment them for machine-learning pipelines.

Normalizing NeuronsBring neurons into a canonical pose for machine-learning models. From Neurons to Model InputsTurn neurons into fixed-size model inputs: feature point clouds and batchable patches. Augmenting NeuronsAugment neuron training sets with realistic perturbations for robust models.