ArticleFrontiers in neuroinformatics2021
PyGeNN: A Python Library for GPU-Enhanced Neural Networks.
Article in Frontiers in neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Efficient event-based delay learning in spiking neural networks.Nature communications · 2025Article
- Building on models-a perspective for computational neuroscience.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Metadata practices for simulation workflows.Scientific data · 2025Article
- Temporal pavlovian conditioning of a model spiking neural network for discrimination sequences of short time intervals.Journal of computational neuroscience · 2025Article
- Estimating orientation in natural scenes: A spiking neural network model of the insect central complex.PLoS computational biology · 2024Article
- Investigating visual navigation using spiking neural network models of the insect mushroom bodies.Frontiers in physiology · 2024Article
- From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?Brain sciences · 2023Review
- A multi-layer mean-field model of the cerebellum embedding microstructure and population-specific dynamics.PLoS computational biology · 2023Article
- Geosmin suppresses defensive behaviour and elicits unusual neural responses in honey bees.Scientific reports · 2023Article
- Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.Frontiers in neuroinformatics · 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
- EXODUS: Stable and efficient training of spiking neural networks.Frontiers in neuroscience · 2023Article
- Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons.eLife · 2022Article
- Connectivity concepts in neuronal network modeling.PLoS computational biology · 2022Review
- A numerical population density technique for N-dimensional neuron models.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
- Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task.Frontiers in neuroscience · 2022Article
- 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
- Parallelization of Neural Processing on Neuromorphic Hardware.Frontiers in neuroscience · 2022Article
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3 authors.
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Abstract
More than half of the Top 10 supercomputing sites worldwide use GPU accelerators and they are becoming ubiquitous in workstations and edge computing devices. GeNN is a C++ library for generating efficient spiking neural network simulation code for GPUs. However, until now, the full flexibility of GeNN could only be harnessed by writing model descriptions and simulation code in C++. Here we present PyGeNN, a Python package which exposes all of GeNN's functionality to Python with minimal overhead. This provides an alternative, arguably more user-friendly, way of using GeNN and allows modelers to use GeNN within the growing Python-based machine learning and computational neuroscience ecosystems. In addition, we demonstrate that, in both Python and C++ GeNN simulations, the overheads of recording spiking data can strongly affect runtimes and show how a new spike recording system can reduce these overheads by up to 10×. Using the new recording system, we demonstrate that by using PyGeNN on a modern GPU, we can simulate a full-scale model of a cortical column faster even than real-time neuromorphic systems. Finally, we show that long simulations of a smaller model with complex stimuli and a custom three-factor learning rule defined in PyGeNN can be simulated almost two orders of magnitude faster than real-time.
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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.