Introducing the Dendrify framework for incorporating dendrites to spiking neural networks

Michalis Pagkalos, Spyridon Chavlis and Panayiota Poirazi ()
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Michalis Pagkalos: Foundation for Research and Technology Hellas (FORTH)
Spyridon Chavlis: Foundation for Research and Technology Hellas (FORTH)
Panayiota Poirazi: Foundation for Research and Technology Hellas (FORTH)

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Computational modeling has been indispensable for understanding how subcellular neuronal features influence circuit processing. However, the role of dendritic computations in network-level operations remains largely unexplored. This is partly because existing tools do not allow the development of realistic and efficient network models that account for dendrites. Current spiking neural networks, although efficient, 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 extremes and facilitate the adoption of dendritic features in spiking neural networks, we introduce Dendrify, an open-source Python package based on Brian 2. 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 while paving the way for developing more powerful neuromorphic systems.

Date: 2023
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DOI: 10.1038/s41467-022-35747-8

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