Snudda
Leverage your neuron morphologies and experimental data to create microcircuits with realistic connectivity
Snudda allows the user to set up and generate microcircuits where the connectivity between neurons is based on reconstructed neuron morphologies. The touch detection algorithm looks for overlaps of axons and dendrites, and places putative synapses where they touch. The putative synapses are then pruned, removing a fraction of the synapses to match statistics from pairwise connectivity experiments. In cases where complete morphologies are not available, Snudda can use probability functions in addition to touch detection on morphologies, to create realistic microcircuits.
The Snudda software is written in Python and includes support for supercomputers. It uses ipyparallel to parallelise network creation, and NEURON as the backend for simulations. Install using pip or by directly downloading from GitHub or the EBRAINS gitlab mirror.
- Create microcircuits in silico using touch detection and rule based synaptic pruning
- Add connectivity between different brain structures
- Powerful modelling of neuromodulators like dopamine and acetylcholine
- Add individual parameter sets for each synapse
- Compatible with NEURON models optimised by BluePyOpt
Get started
Use Snudda to create microcircuits with realistic connectivities
Documentation and examples with executable Jupyter notebooks are available. The examples are based on the striatum, but are easily adaptable to other brain structures. Snudda is open source and its workflow includes Treem, BluePyOpt and NEURON. You can explore Snudda either by installing it on your local computer or on the EBRAINS platform.
Related publications
Hjorth, J.J.J., Hellgren Kotaleski, J. & Kozlov, A. Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda. Neuroinform 19, 685–701 (2021). https://doi.org/10.1007/s12021-021-09531-w