ArticleFrontiers in neuroscience2019
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Backpropagation through space, time and the brain.Nature communications · 2025Article
- SpiKon-E: Hybrid Soft Artificial Muscle Control Using Hardware Spiking Neural Network.Biomimetics (Basel, Switzerland) · 2025Article
- Recent Advancements in 2D Material-Based Memristor Technology Toward Neuromorphic Computing.Micromachines · 2024Review
- High-performance deep spiking neural networks with 0.3 spikes per neuron.Nature communications · 2024Article
- Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey.Biomimetics (Basel, Switzerland) · 2024Review
- Neuromorphic Sensor Based on Force-Sensing Resistors.Biomimetics (Basel, Switzerland) · 2024Article
- Hybrid Deep Learning Techniques for Securing Bioluminescent Interfaces in Internet of Bio Nano Things.Sensors (Basel, Switzerland) · 2023Article
- Monitoring time domain characteristics of Parkinson's disease using 3D memristive neuromorphic system.Frontiers in computational neuroscience · 2023Article
- Bio-Inspired Control System for Fingers Actuated by Multiple SMA Actuators.Biomimetics (Basel, Switzerland) · 2022Article
- Neuromorphic computing for content-based image retrieval.PloS one · 2022Article
- Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges.Frontiers in neuroscience · 2022Article
- A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.Frontiers in neuroscience · 2022Article
- Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster.Frontiers in neuroinformatics · 2022Article
- Neuromorphic artificial intelligence systems.Frontiers in neuroscience · 2022Review
- The BrainScaleS-2 Accelerated Neuromorphic System With Hybrid Plasticity.Frontiers in neuroscience · 2022Article
- Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors.Scientific reports · 2021Article
- Event-based backpropagation can compute exact gradients for spiking neural networks.Scientific reports · 2021Article
- Adaptive behaviour and learning in slime moulds: the role of oscillations.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight Resolution.Frontiers in neuroscience · 2021Article
- Optical Axons for Electro-Optical Neural Networks.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
17 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play a simplified version of the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57 mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.
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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.