P176 Plastic Arbor: a modern simulation framework for synaptic plasticity – from single synapses to networks of morphological neurons
Jannik Luboeinski*1,2,3, Sebastian Schmitt1,2, Shirin Shafiee1,2, Thorsten Hater4, Fabian Bösch5, Christian Tetzlaff1,2,3
1III. Institute of Physics – Biophysics, University of Göttingen, Germany
2Department for Neuro- and Sensory Physiology, University Medical Center Göttingen, Germany
3Campus Institute Data Science (CIDAS), Göttingen, Germany
4Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany
5Swiss National Supercomputing Centre, ETH Zürich, Switzerland
*Email: jannik.luboeinski@med.uni-goettingen.de
Introduction
Arbor is a software library designed for the efficient simulation of large-scale networks of biological neurons with detailed morphological structures. It combines customizable neuronal and synaptic mechanisms with high-performance computing, enabling to use diverse backend architectures such as multi-core CPU and GPU systems [1] (also see Fig. 1a).
Synaptic plasticity processes play a vital role in cognitive functions, including learning and memory [2,3]. Recent studies have shown that intracellular molecular processes in dendrites significantly influence single-neuron dynamics [4,5]. However, for understanding how the complex interplay between dendrites and synaptic processes influences network dynamics, computational modeling is required.
Methods
To enable the modeling of large-scale networks of morphologically detailed neurons with diverse plasticity processes, we have extended the Arbor library to yield the Plastic Arbor framework, supporting simulations of a large variety of spike-driven plasticity paradigms (cf. Fig. 1b). To showcase the features of the new framework, we present examples of computational models, beginning with single-synapse dynamics [6,7], progressing to multi-synapse rules [8,9], and finally scaling up to large recurrent networks [10].
Results
While cross-validating our implementations by comparison with other simulators, we show that Arbor allows simulating plastic networks of multi-compartment neurons at nearly no additional cost in runtime compared to point-neuron simulations. Using the new framework, we have already been able to investigate the impact of dendritic structures on network dynamics across a timescale of several hours, showing a relation between the length of dendritic trees and the ability of the network to efficiently store information.
Discussion
Due to its modern computing architecture and inherent support of multi-compartment neurons, the Arbor simulator constitutes an important tool for the computational modeling of neuronal networks. By our extension of Arbor, we provide a valuable tool that will support future studies on the impact of synaptic plasticity, especially, in conjunction with neuronal morphology, in large networks. In our recent work, we also demonstrate new insights into the functional impact of morphological neuronal structure at the network level. In the future, the Plastic Arbor framework may power a great variety of studies considering synaptic mechanisms and their interactions with neuronal dynamics and morphologies, from single synapses to large networks.
Figure 1. Overview of the extended Arbor framework with support for synaptic plasticity simulations.
Acknowledgements
This work was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through grants SFB1286 (C01, Z01) and TE 1172/7-1, as well as by the European Commission H2020 grants no. 899265 (ADOPD) and 945539 (HBP SGA3).
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