ArticleFrontiers in neuroscience2018
sPyNNaker: A Software Package for Running PyNN Simulations on SpiNNaker.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- The neurobench framework for benchmarking neuromorphic computing algorithms and systems.Nature communications · 2025Review
- Neuromorphic computing at scale.Nature · 2025Review
- Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing.Nature communications · 2024Article
- Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning.National science review · 2024Article
- Neuromorphic Sentiment Analysis Using Spiking Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Efficient SNN multi-cores MAC array acceleration on SpiNNaker 2.Frontiers in neuroscience · 2023Article
- Embodied neuromorphic intelligence.Nature communications · 2022Review
- A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.Frontiers in neuroscience · 2022Article
- Liquid State Machine on SpiNNaker for Spatio-Temporal Classification Tasks.Frontiers in neuroscience · 2022Article
- Parallelization of Neural Processing on Neuromorphic Hardware.Frontiers in neuroscience · 2022Article
- Benchmarking Neuromorphic Hardware and Its Energy Expenditure.Frontiers in neuroscience · 2022Article
- Beyond LIF Neurons on Neuromorphic Hardware.Frontiers in neuroscience · 2022Article
- Verification of a neuromorphic computing network simulator using experimental traffic data.Frontiers in neuroscience · 2022Article
- Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian.Frontiers in neuroinformatics · 2022Article
- Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task.Frontiers in neuroscience · 2022Article
- Comparison of Artificial and Spiking Neural Networks on Digital Hardware.Frontiers in neuroscience · 2021Article
- Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum.Frontiers in cellular neuroscience · 2021Article
- A system hierarchy for brain-inspired computing.Nature · 2020Article
- Stochastic rounding and reduced-precision fixed-point arithmetic for solving neural ordinary differential equations.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
- Real-time cortical simulation on neuromorphic hardware.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work presents sPyNNaker 4.0.0, the latest version of the software package for simulating PyNN-defined spiking neural networks (SNNs) on the SpiNNaker neuromorphic platform. Operations underpinning realtime SNN execution are presented, including an event-based operating system facilitating efficient time-driven neuron state updates and pipelined event-driven spike processing. Preprocessing, realtime execution, and neuron/synapse model implementations are discussed, all in the context of a simple example SNN. Simulation results are demonstrated, together with performance profiling providing insights into how software interacts with the underlying hardware to achieve realtime execution. System performance is shown to be within a factor of 2 of the original design target of 10,000 synaptic events per millisecond, however SNN topology is shown to influence performance considerably. A cost model is therefore developed characterizing the effect of network connectivity and SNN partitioning. This model enables users to estimate SNN simulation performance, allows the SpiNNaker team to make predictions on the impact of performance improvements, and helps demonstrate the continued potential of the SpiNNaker neuromorphic hardware.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.