Arbor is a software library designed for efficient simulation of large-scale networks of biological neurons with detailed morphological structures. It combines customizable neuronal and synaptic mechanisms with high-performance computing, supporting multi-core CPU and GPU systems. In humans and other animals, synaptic plasticity processes play a vital role in cognitive functions, including learning and memory. Recent studies have shown that intracellular molecular processes in dendrites significantly influence single-neuron dynamics. However, for understanding how the complex interplay between dendrites and synaptic processes influences network dynamics, computational modeling is required. To enable the modeling of large-scale networks of morphologically detailed neurons with diverse plasticity processes, we have extended the Arbor library to the Plastic Arbor framework, supporting simulations of a large variety of spike-driven plasticity paradigms. To showcase the features of the new framework, we present examples of computational models, beginning with single-synapse dynamics, progressing to multi-synapse rules, and finally scaling up to large recurrent networks. 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. By our extension of Arbor, we aim to provide a valuable tool that will support future studies on the impact of synaptic plasticity, especially, in conjunction with neuronal morphology, in large networks.

翻译：Arbor是一个专为高效仿真具有精细形态结构的大规模生物神经元网络而设计的软件库。它将可定制的神经元与突触机制与高性能计算相结合，支持多核CPU和GPU系统。在人类及其他动物中，突触可塑性过程在包括学习与记忆在内的认知功能中起着至关重要的作用。近期研究表明，树突内的细胞内分子过程显著影响单个神经元的动力学特性。然而，要理解树突与突触过程之间复杂的相互作用如何影响网络动力学，必须借助计算建模手段。为实现具有多样化可塑性过程的形态精细神经元的大规模网络建模，我们将Arbor库扩展为Plastic Arbor框架，支持多种脉冲驱动可塑性范式的仿真。为展示新框架的特性，我们呈现了从单突触动力学起步，逐步扩展到多突触规则，最终实现大规模循环网络的计算模型示例。在通过与其他仿真器对比进行交叉验证的同时，我们证明Arbor能够以近乎零额外运行时成本（相较于点神经元仿真）实现多室神经元可塑性网络的仿真。利用新框架，我们已经能够研究数小时时间尺度上树突结构对网络动力学的影响，揭示了树突分支长度与网络高效存储信息能力之间的关联。通过对Arbor的扩展，我们旨在提供一个有价值的工具，以支持未来关于大规模网络中突触可塑性影响——特别是与神经元形态结构相结合的研究。