Plastic Arbor: a modern simulation framework for synaptic plasticity -- from single synapses to networks of morphological neurons

TL;DR

This paper introduces an extended version of the Arbor simulation framework that efficiently models large-scale networks of morphologically detailed neurons with diverse synaptic plasticity mechanisms, enabling advanced studies of neural dynamics.

Contribution

The authors extend Arbor to support diverse spike-driven plasticity paradigms in large-scale, morphologically detailed neuron networks, combining high performance with detailed biological modeling.

Findings

Arbor can simulate plastic networks with multi-compartment neurons at minimal additional runtime.

The framework demonstrates high efficiency in runtime and memory compared to other simulators.

Dendritic structure length influences network's ability to store information effectively.

Abstract

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 support simulations of a large variety of spike-driven plasticity paradigms. To showcase the features of the extended 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. In addition, we demonstrate that Arbor is highly efficient in terms of runtime and memory use as compared to other simulators. Using the extended framework, as an example, we investigate the impact of dendritic structures on network dynamics across a timescale of several hours, finding 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.