ArticleFrontiers in neuroscience2019
Neuromorphic Hardware Learns to Learn.
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 14 papers.
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Who cites it
14 citing papers in PubMed.
- Rapid learning with phase-change memory-based in-memory computing through learning-to-learn.Nature communications · 2025Article
- Neuromorphic neuromodulation: Towards the next generation of closed-loop neurostimulation.PNAS nexus · 2024Review
- Realizing Molecular Machine Learning through Communications for Biological AI: Future Directions and Challenges.IEEE nanotechnology magazine · 2023Article
- A survey and perspective on neuromorphic continual learning systems.Frontiers in neuroscience · 2023Review
- Meta-SpikePropamine: learning to learn with synaptic plasticity in spiking neural networks.Frontiers in neuroscience · 2023Article
- Weak self-supervised learning for seizure forecasting: a feasibility study.Royal Society open science · 2022Article
- Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing.Nano-micro letters · 2022Review
- Highly efficient neuromorphic learning system of spiking neural network with multi-compartment leaky integrate-and-fire neurons.Frontiers in neuroscience · 2022Article
- The BrainScaleS-2 Accelerated Neuromorphic System With Hybrid Plasticity.Frontiers in neuroscience · 2022Article
- A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.Frontiers in neuroscience · 2022Article
- Article
- ROA: A Rapid Learning Scheme forFrontiers in artificial intelligence · 2021Article
- Adaptive Extreme Edge Computing for Wearable Devices.Frontiers in neuroscience · 2021Review
- Bayesian Multi-objective Hyperparameter Optimization for Accurate, Fast, and Efficient Neural Network Accelerator Design.Frontiers in neuroscience · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hyperparameters and learning algorithms for neuromorphic hardware are usually chosen by hand to suit a particular task. In contrast, networks of neurons in the brain were optimized through extensive evolutionary and developmental processes to work well on a range of computing and learning tasks. Occasionally this process has been emulated through genetic algorithms, but these require themselves hand-design of their details and tend to provide a limited range of improvements. We employ instead other powerful gradient-free optimization tools, such as cross-entropy methods and evolutionary strategies, in order to port the function of biological optimization processes to neuromorphic hardware. As an example, we show these optimization algorithms enable neuromorphic agents to learn very efficiently from rewards. In particular, meta-plasticity, i.e., the optimization of the learning rule which they use, substantially enhances reward-based learning capability of the hardware. In addition, we demonstrate for the first time Learning-to-Learn benefits from such hardware, in particular, the capability to extract abstract knowledge from prior learning experiences that speeds up the learning of new but related tasks. Learning-to-Learn is especially suited for accelerated neuromorphic hardware, since it makes it feasible to carry out the required very large number of network computations.
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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.