ArticleFrontiers in neuroscience2013
Six networks on a universal neuromorphic computing substrate.
Article in Frontiers in neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Spike-based alignment learning solves the weight transport problem.Nature communications · 2026Article
- Sliding-window training on readout layer enhances liquid state machines for temporal prediction.Npj unconventional computing · 2026Article
- Ultrafast neural sampling with spiking nanolasers.Nature communications · 2025Article
- Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity.Nature communications · 2025Article
- Low-latency hierarchical routing of reconfigurable neuromorphic systems.Frontiers in neuroscience · 2025Article
- Online dynamical learning and sequence memory with neuromorphic nanowire networks.Nature communications · 2023Article
- Efficient SNN multi-cores MAC array acceleration on SpiNNaker 2.Frontiers in neuroscience · 2023Article
- Monitoring time domain characteristics of Parkinson's disease using 3D memristive neuromorphic system.Frontiers in computational neuroscience · 2023Article
- Precise Spiking Motifs in Neurobiological and Neuromorphic Data.Brain sciences · 2022Review
- Article
- Benchmarking Neuromorphic Hardware and Its Energy Expenditure.Frontiers in neuroscience · 2022Article
- A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations.Frontiers in neuroinformatics · 2022Article
- Information dynamics in neuromorphic nanowire networks.Scientific reports · 2021Article
- Optoelectronic intelligence.Applied physics letters · 2021Article
- Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware.Frontiers in neuroscience · 2021Article
- SyNC, a Computationally Extensive and Realistic Neural Net to Identify Relative Impacts of Synaptopathy Mechanisms on Glutamatergic Neurons and Their Networks in Autism and Complex Neurological Disorders.Frontiers in cellular neuroscience · 2021Article
- Event-Based Update of Synapses in Voltage-Based Learning Rules.Frontiers in neuroinformatics · 2021Article
- Bio-Inspired Strategies for Improving the Selectivity and Sensitivity of Artificial Noses: A Review.Sensors (Basel, Switzerland) · 2020Review
- Periodicity Pitch Perception.Frontiers in neuroscience · 2020Article
- Extending the Functional Subnetwork Approach to a Generalized Linear Integrate-and-Fire Neuron Model.Frontiers in neurorobotics · 2020Article
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and digital transmission of action potentials. Major advantages of this emulation device, which has been explicitly designed as a universal neural network emulator, are its inherent parallelism and high acceleration factor compared to conventional computers. Its configurability allows the realization of almost arbitrary network topologies and the use of widely varied neuronal and synaptic parameters. Fixed-pattern noise inherent to analog circuitry is reduced by calibration routines. An integrated development environment allows neuroscientists to operate the device without any prior knowledge of neuromorphic circuit design. As a showcase for the capabilities of the system, we describe the successful emulation of six different neural networks which cover a broad spectrum of both structure and functionality.
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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.