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Introducing the Dendrify framework for incorporating dendrites to spiking neural networks

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  • Michalis Pagkalos

    (Foundation for Research and Technology Hellas (FORTH)
    University of Crete)

  • Spyridon Chavlis

    (Foundation for Research and Technology Hellas (FORTH))

  • Panayiota Poirazi

    (Foundation for Research and Technology Hellas (FORTH))

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.

Suggested Citation

  • Michalis Pagkalos & Spyridon Chavlis & Panayiota Poirazi, 2023. "Introducing the Dendrify framework for incorporating dendrites to spiking neural networks," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-022-35747-8
    DOI: 10.1038/s41467-022-35747-8
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    References listed on IDEAS

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    1. Balázs Ujfalussy & Tamás Kiss & Péter Érdi, 2009. "Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields," PLOS Computational Biology, Public Library of Science, vol. 5(9), pages 1-16, September.
    2. Guillaume Bellec & Franz Scherr & Anand Subramoney & Elias Hajek & Darjan Salaj & Robert Legenstein & Wolfgang Maass, 2020. "A solution to the learning dilemma for recurrent networks of spiking neurons," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    3. Jacopo Bono & Claudia Clopath, 2017. "Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level," Nature Communications, Nature, vol. 8(1), pages 1-17, December.
    4. Nicolas Perez-Nieves & Vincent C. H. Leung & Pier Luigi Dragotti & Dan F. M. Goodman, 2021. "Neural heterogeneity promotes robust learning," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    5. Rosanna Migliore & Carmen A Lupascu & Luca L Bologna & Armando Romani & Jean-Denis Courcol & Stefano Antonel & Werner A H Van Geit & Alex M Thomson & Audrey Mercer & Sigrun Lange & Joanne Falck & Chri, 2018. "The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-25, September.
    6. Alexandra Tzilivaki & George Kastellakis & Panayiota Poirazi, 2019. "Challenging the point neuron dogma: FS basket cells as 2-stage nonlinear integrators," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
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