ArticleFrontiers in neuroinformatics2011
An efficient simulation environment for modeling large-scale cortical processing.
Article in Frontiers in neuroinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Brian2CUDA: Flexible and Efficient Simulation of Spiking Neural Network Models on GPUs.Frontiers in neuroinformatics · 2022Article
- A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations.Frontiers in neuroinformatics · 2022Article
- Pattern Recognition of Spiking Neural Networks Based on Visual Mechanism and Supervised Synaptic Learning.Neural plasticity · 2020Article
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- Mapping the Information Trace in Local Field Potentials by a Computational Method of Two-Dimensional Time-Shifting Synchronization Likelihood Based on Graphic Processing Unit Acceleration.Neuroscience bulletin · 2017Article
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- Improper activation of D1 and D2 receptors leads to excess noise in prefrontal cortex.Frontiers in computational neuroscience · 2015Article
- Learning touch preferences with a tactile robot using dopamine modulated STDP in a model of insular cortex.Frontiers in neurorobotics · 2015Article
- NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors.Frontiers in neuroscience · 2015Article
- Efficient spiking neural network model of pattern motion selectivity in visual cortex.Neuroinformatics · 2014Article
- N2A: a computational tool for modeling from neurons to algorithms.Frontiers in neural circuits · 2014Article
- An efficient automated parameter tuning framework for spiking neural networks.Frontiers in neuroscience · 2014Article
- Limits to high-speed simulations of spiking neural networks using general-purpose computers.Frontiers in neuroinformatics · 2014Article
- Three tools for the real-time simulation of embodied spiking neural networks using GPUs.Neuroinformatics · 2013Article
- A large-scale neural network model of the influence of neuromodulatory levels on working memory and behavior.Frontiers in computational neuroscience · 2013Article
- Cognit activation: a mechanism enabling temporal integration in working memory.Trends in cognitive sciences · 2012Article
- A novel phase portrait for neuronal excitability.PloS one · 2012Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. It uses a standard network construction interface. The simulator allows for execution on either GPUs or CPUs. The simulator, which is written in C/C++, allows for both fine grain and coarse grain specificity of a host of parameters. We demonstrate the ease of use and computational efficiency of this model by implementing a large-scale model of cortical areas V1, V4, and area MT. The complete model, which has 138,240 neurons and approximately 30 million synapses, runs in real-time on an off-the-shelf GPU. The simulator source code, as well as the source code for the cortical model examples is publicly available.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.