ArticleFrontiers in computational neuroscience2021
Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs.
Article in Frontiers in computational neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Plastic Arbor: A modern simulation framework for synaptic plasticity-From single synapses to networks of morphological neurons.PLoS computational biology · 2026Article
- Building on models-a perspective for computational neuroscience.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Metadata practices for simulation workflows.Scientific data · 2025Article
- NESTML: a generic modeling language and code generation tool for the simulation of spiking neural networks with advanced plasticity rules.Frontiers in neuroinformatics · 2025Article
- Commentary: Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch.Frontiers in neuroinformatics · 2024Article
- Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch.Frontiers in neuroinformatics · 2024Article
- Article
- Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.Frontiers in neuroinformatics · 2023Article
- BIDL: a brain-inspired deep learning framework for spatiotemporal processing.Frontiers in neuroscience · 2023Article
- neuroAIx-Framework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20× faster than biological real-time.Frontiers in computational neuroscience · 2023Article
- Towards the Simulation of a Realistic Large-Scale Spiking Network on a Desktop Multi-GPU System.Bioengineering (Basel, Switzerland) · 2022Article
- Connectivity concepts in neuronal network modeling.PLoS computational biology · 2022Review
- Benchmarking Neuromorphic Hardware and Its Energy Expenditure.Frontiers in neuroscience · 2022Article
- NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models.Frontiers in neuroinformatics · 2022Article
- A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations.Frontiers in neuroinformatics · 2022Article
- Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster.Frontiers in neuroinformatics · 2022Article
- Brian2CUDA: Flexible and Efficient Simulation of Spiking Neural Network Models on GPUs.Frontiers in neuroinformatics · 2022Article
- PyGeNN: A Python Library for GPU-Enhanced Neural Networks.Frontiers in neuroinformatics · 2021Article
- Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight Resolution.Frontiers in neuroscience · 2021Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Over the past decade there has been a growing interest in the development of parallel hardware systems for simulating large-scale networks of spiking neurons. Compared to other highly-parallel systems, GPU-accelerated solutions have the advantage of a relatively low cost and a great versatility, thanks also to the possibility of using the CUDA-C/C++ programming languages. NeuronGPU is a GPU library for large-scale simulations of spiking neural network models, written in the C++ and CUDA-C++ programming languages, based on a novel spike-delivery algorithm. This library includes simple LIF (leaky-integrate-and-fire) neuron models as well as several multisynapse AdEx (adaptive-exponential-integrate-and-fire) neuron models with current or conductance based synapses, different types of spike generators, tools for recording spikes, state variables and parameters, and it supports user-definable models. The numerical solution of the differential equations of the dynamics of the AdEx models is performed through a parallel implementation, written in CUDA-C++, of the fifth-order Runge-Kutta method with adaptive step-size control. In this work we evaluate the performance of this library on the simulation of a cortical microcircuit model, based on LIF neurons and current-based synapses, and on balanced networks of excitatory and inhibitory neurons, using AdEx or Izhikevich neuron models and conductance-based or current-based synapses. On these models, we will show that the proposed library achieves state-of-the-art performance in terms of simulation time per second of biological activity. In particular, using a single NVIDIA GeForce RTX 2080 Ti GPU board, the full-scale cortical-microcircuit model, which includes about 77,000 neurons and 3 · 10
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