ArticleFrontiers in neuroinformatics2024
Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch.
Article in Frontiers in neuroinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Computational Model of the Respiratory CPG for the Artificial Control of Breathing.Bioengineering (Basel, Switzerland) · 2025Article
- Competition and cooperation of assembly sequences in recurrent neural networks.PLoS computational biology · 2025Article
- Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.Journal of clinical medicine · 2025Review
- The road toward a physiological control of artificial respiration: the role of bio-inspired neuronal networks.Frontiers in neuroscience · 2025Article
- Commentary: Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch.Frontiers in neuroinformatics · 2024Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Spiking neural network simulations are a central tool in Computational Neuroscience, Artificial Intelligence, and Neuromorphic Engineering research. A broad range of simulators and software frameworks for such simulations exist with different target application areas. Among these, PymoNNto is a recent Python-based toolbox for spiking neural network simulations that emphasizes the embedding of custom code in a modular and flexible way. While PymoNNto already supports GPU implementations, its backend relies on NumPy operations. Here we introduce PymoNNtorch, which is natively implemented with PyTorch while retaining PymoNNto's modular design. Furthermore, we demonstrate how changes to the implementations of common network operations in combination with PymoNNtorch's native GPU support can offer speed-up over conventional simulators like NEST, ANNarchy, and Brian 2 in certain situations. Overall, we show how PymoNNto's modular and flexible design in combination with PymoNNtorch's GPU acceleration and optimized indexing operations facilitate research and development of spiking neural networks in the Python programming language.
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Registered trials
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