Plastic Arbor: a modern simulation framework for synaptic plasticity $unicode{x2013}$ from single synapses to networks of morphological neurons

2024-11-25

AI Summary

Simulating synaptic plasticity in large-scale morphologically detailed neuronal networks suffers from low computational efficiency and difficulty integrating diverse plasticity mechanisms. Method: We developed the first full-scale simulation platform within the Arbor framework that systematically integrates multiple spike-driven plasticity rules—including STDP and Ca²⁺-dependent mechanisms—via multi-compartmental modeling, CPU/GPU heterogeneous parallelization, and a plugin-based, cross-platform plasticity mechanism architecture. This design significantly reduces computational overhead, enabling morphological network simulations to approach the speed of point-neuron models. Contribution/Results: We quantitatively established, for the first time, a power-law relationship between dendritic structural length and network information storage efficiency. Furthermore, hour-scale simulations demonstrated that increased dendritic complexity enhances long-term memory capacity. Our work provides a scalable, biologically grounded simulation paradigm for neuromorphic computing and synaptic plasticity theory.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Deep Neural Architectures and Foundation Models
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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 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.

Problem

• Simulating large-scale networks with detailed neuronal morphology and plasticity
• Extending Arbor library to support diverse spike-driven synaptic plasticity paradigms
• Investigating dendritic structure impact on network dynamics and information storage

Innovation

• Extends Arbor for spike-driven plasticity simulations
• Supports multi-compartment neuron networks efficiently
• Enables plasticity modeling with dendritic structure analysis