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

While the implementation of learning and memory in the brain is governed in large part by subcellular mechanims in the dendrites of neurons, large-scale network simulations featuring such processes remain challenging to achieve. This can be attributed to a lack of appropriate software tools, as neuroscientific simulation software focuses 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. To achieve this, 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 already existing model descriptions language NESTML, we generate C++ code implementing user-configurable subcellular dynamics. Through benchmarking and profiling, we show that the generated models run efficiently, leading to scalable NEST network simulations. We demonstrate relevant functionalities by showing 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 learning, and in particular of how brain-wide communication through long-range, layer 1-targetting connections steers perisomatic plasticity.