Introduction

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.

Tip

If you use Dendrify for your published research, we kindly ask you to cite our article:
Introducing the Dendrify framework for incorporating dendrites to spiking neural networks
M Pagkalos, S Chavlis, P Poirazi
DOI: https://doi.org/10.1038/s41467-022-35747-8

CONTENTS:

Getting started

• Introduction
• Installation
  • Dependencies
  • GPU support

Using Dendrify

• Tutorial
• Examples

Reference documentation

• Classes
  • Compartment
  • Soma
  • Dendrite
  • NeuronModel
  • EphysProperties
• Model library
  • Somatic models
  • Dendritic models
  • Synaptic models
• Index

Useful information

• Important literature
• Release notes
• Code of Conduct