Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs
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, tha...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-02-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2021.627620/full |
| _version_ | 1818656501772845056 |
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| author | Bruno Golosio Bruno Golosio Gianmarco Tiddia Gianmarco Tiddia Chiara De Luca Chiara De Luca Elena Pastorelli Elena Pastorelli Francesco Simula Pier Stanislao Paolucci |
| author_facet | Bruno Golosio Bruno Golosio Gianmarco Tiddia Gianmarco Tiddia Chiara De Luca Chiara De Luca Elena Pastorelli Elena Pastorelli Francesco Simula Pier Stanislao Paolucci |
| author_sort | Bruno Golosio |
| collection | DOAJ |
| description | 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 · 108 connections, can be simulated at a speed very close to real time, while the simulation time of a balanced network of 1,000,000 AdEx neurons with 1,000 connections per neuron was about 70 s per second of biological activity. |
| first_indexed | 2024-12-17T03:26:36Z |
| format | Article |
| id | doaj.art-cd03c19b9c094dbfac4ae22de66bec43 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-17T03:26:36Z |
| publishDate | 2021-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-cd03c19b9c094dbfac4ae22de66bec432022-12-21T22:05:23ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882021-02-011510.3389/fncom.2021.627620627620Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUsBruno Golosio0Bruno Golosio1Gianmarco Tiddia2Gianmarco Tiddia3Chiara De Luca4Chiara De Luca5Elena Pastorelli6Elena Pastorelli7Francesco Simula8Pier Stanislao Paolucci9Department of Physics, University of Cagliari, Cagliari, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Cagliari, Cagliari, ItalyDepartment of Physics, University of Cagliari, Cagliari, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Cagliari, Cagliari, ItalyPh.D. Program in Behavioral Neuroscience, “Sapienza” University of Rome, Rome, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, ItalyPh.D. Program in Behavioral Neuroscience, “Sapienza” University of Rome, Rome, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, ItalyOver 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 · 108 connections, can be simulated at a speed very close to real time, while the simulation time of a balanced network of 1,000,000 AdEx neurons with 1,000 connections per neuron was about 70 s per second of biological activity.https://www.frontiersin.org/articles/10.3389/fncom.2021.627620/fullspiking neural network simulatorcortical microcircuitsadaptive exponential integrate-and-fire neuron modelconductance-based synapsesGPU |
| spellingShingle | Bruno Golosio Bruno Golosio Gianmarco Tiddia Gianmarco Tiddia Chiara De Luca Chiara De Luca Elena Pastorelli Elena Pastorelli Francesco Simula Pier Stanislao Paolucci Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs Frontiers in Computational Neuroscience spiking neural network simulator cortical microcircuits adaptive exponential integrate-and-fire neuron model conductance-based synapses GPU |
| title | Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs |
| title_full | Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs |
| title_fullStr | Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs |
| title_full_unstemmed | Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs |
| title_short | Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs |
| title_sort | fast simulations of highly connected spiking cortical models using gpus |
| topic | spiking neural network simulator cortical microcircuits adaptive exponential integrate-and-fire neuron model conductance-based synapses GPU |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2021.627620/full |
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