dendrify 1.0.9

Introducing dendrites to spiking neural networks

Although neuronal dendrites greatly influence how single neurons process incoming information, their role in network-level functions remain largely unexplored. Current SNNs are usually quite simplistic, overlooking essential dendritic properties. Conversely, circuit models with morphologically detailed neuron models are computationally costly, thus impractical for large-network simulations.

To bridge the gap between these two, we introduce Dendrify, a free, open-source Python package compatible with the Brian 2 simulator. Dendrify, through simple commands, automatically generates reduced compartmental neuron models with simplified yet biologically relevant dendritic and synaptic integrative properties. Such models strike a good balance between flexibility, performance, and biological accuracy, allowing us to explore dendritic contributions to network-level functions.

If you use Dendrify for your published research, we kindly ask you to cite our article:

Pagkalos, M., Chavlis, S., & Poirazi, P. (2023). Introducing the Dendrify framework for incorporating dendrites to spiking neural networks. Nature Communications, 14(1), 131. https://doi.org/10.1038/s41467-022-35747-8

Documentation for Dendrify can be found at https://dendrify.readthedocs.io/en/latest/

The project presentation for the INCF/OCNS Software Working Group is available on google drive and an interactive notebook with a short demo on google colab.