A NEST-based framework unlocks massively parallel simulation of networks of multi-compartment neurons with customizable subcellular dynamics

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.