Merge pull request #13 from arbor-sim/blog/scaling

add new paper

Author: thorstenhater

_posts/2026-02-19-plastic-arbor.md

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+---
+layout: post
+title: "Plastic Arbor: A modern simulation framework for synaptic plasticity—From single synapses to networks of morphological neurons"
+---
+
+# Plastic Arbor: A modern simulation framework for synaptic plasticity—From single synapses to networks of morphological neurons
+
+[Article](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013926)
+
+From the 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.