A NEST-based framework unlocks massively parallel simulation of networks of multi-compartment neurons with customizable subcellular dynamics
Abstract
While subcellular mechanisms in the dendrites of neurons govern the implementation of learning and memory in the brain, large-scale network simulations featuring such processes remain challenging to achieve. This can be attributed to a lack of appropriate software tools, as neuroscientific simulators focus on the one hand on highly detailed models, and on the other hand on massive networks featuring point neurons. Here, we fill this gap by implementing a framework for the massively parallel simulation of simplified dendrite models with customizable subcellular dynamics. We leverage the NEural Simulation Tool (NEST), the neuroscientific reference with respect to efficient massively parallel simulations of point-neuron networks. By co-opting the model description language NESTML, we generate C++ code implementing user-configurable subcellular dynamics. We show that the generated models run efficiently, leading to scalable NEST network simulations. To showcase relevant functionalities, we provide a proof-of-concept demonstration that a key sensory computation – the association of top-down context arriving at distal dendrites in layer 1 and feedforward sensory input arriving perisomatically – can be achieved in a single-shot fashion through apical calcium dynamics. Our work thus unlocks the study of how dendritic processes shape network function and learning at scale.
Related
- Testing immune priming failure in laryngeal squamous cell carcinoma: direct evidence, unresolved links, and a compartment-resolved framework
- A compartment-resolved framework for microbial sulfur–reactive sulfur signaling in diabetic cardiomyopathy: from gut metabolism to cardiac sulfur responses
- Ocular fluid biomarkers and anti-retinal autoantibodies in neovascular age-related macular degeneration: compartment-specific evidence and a provisional multidomain framework
- SPLATCHE2: a spatially explicit simulation framework for complex demography, genetic admixture and recombination
- A MATLAB-based AI-IoT simulation framework for LSTM-driven smart solar irrigation and renewable energy management
BibTeX
@article{wybo2026nestbased,
title = {{A NEST-based framework unlocks massively parallel simulation of networks of multi-compartment neurons with customizable subcellular dynamics}},
author = {Willem A. M. Wybo and Leander Ewert and Charl Linssen and Pooja Babu and Elena Pastorelli and Pier Stanislao Paolucci and Abigail Morrison},
journal = {Communications Biology},
year = {2026},
month = oct,
doi = {10.1038/s42003-026-11157-0},
url = {https://www.nature.com/articles/s42003-026-11157-0},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A NEST-based framework unlocks massively parallel simulation of networks of multi-compartment neurons with customizable subcellular dynamics
AU - Willem A. M. Wybo
AU - Leander Ewert
AU - Charl Linssen
AU - Pooja Babu
AU - Elena Pastorelli
AU - Pier Stanislao Paolucci
AU - Abigail Morrison
PY - 2026
DA - 2026/10/27
JO - Communications Biology
DO - 10.1038/s42003-026-11157-0
UR - https://www.nature.com/articles/s42003-026-11157-0
AB - While subcellular mechanisms in the dendrites of neurons govern the implementation of learning and memory in the brain, large-scale network simulations featuring such processes remain challenging to achieve. This can be attributed to a lack of appropriate software tools, as neuroscientific simulators focus on the one hand on highly detailed models, and on the other hand on massive networks featuring point neurons. Here, we fill this gap by implementing a framework for the massively parallel simulation of simplified dendrite models with customizable subcellular dynamics. We leverage the NEural Simulation Tool (NEST), the neuroscientific reference with respect to efficient massively parallel simulations of point-neuron networks. By co-opting the model description language NESTML, we generate C++ code implementing user-configurable subcellular dynamics. We show that the generated models run efficiently, leading to scalable NEST network simulations. To showcase relevant functionalities, we provide a proof-of-concept demonstration that a key sensory computation – the association of top-down context arriving at distal dendrites in layer 1 and feedforward sensory input arriving perisomatically – can be achieved in a single-shot fashion through apical calcium dynamics. Our work thus unlocks the study of how dendritic processes shape network function and learning at scale.
SN - 2399-3642
ER -