Introduction

Although neuronal dendrites play a crucial role in shaping how individual neurons process synaptic information, their contribution to network-level functions has remained largely unexplored. Current spiking neural networks (SNNs) often oversimplify dendritic properties or overlook their essential functions. On the other hand, circuit models with morphologically detailed neuron representations are computationally intensive, making them impractical for simulating large networks.

In an effort to bridge this gap, we present Dendrify—a freely available, open-source Python package that seamlessly integrates 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. These models offer a well-rounded compromise between flexibility, performance, and biological accuracy, enabling us to investigate the impact of dendrites on 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

Tutorials

• Dendrify basics
• Running simulations

Examples

• Compartmental models
• Point-neuron models
• Validation tests

Reference documentation

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

Useful information

• Support
• Release notes
• Important literature
• Code of Conduct