ArticleiScience2021
EqSpike: spike-driven equilibrium propagation for neuromorphic implementations.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Quantum equilibrium propagation for efficient training of quantum systems based on Onsager reciprocity.Nature communications · 2025Article
- Temporal Contrastive Learning through implicit non-equilibrium memory.Nature communications · 2025Article
- Defect-Tolerant Memristor Crossbar Circuits for Local Learning Neural Networks.Nanomaterials (Basel, Switzerland) · 2025Article
- Training an Ising machine with equilibrium propagation.Nature communications · 2024Article
- Structural plasticity for neuromorphic networks with electropolymerized dendritic PEDOT connections.Nature communications · 2023Article
- Memristor Crossbar Circuits Implementing Equilibrium Propagation for On-Device Learning.Micromachines · 2023Article
- Energy-based analog neural network framework.Frontiers in computational neuroscience · 2023Article
- Periodicity Pitch Perception Part III: Sensibility and Pachinko Volatility.Frontiers in neuroscience · 2022Article
- Article
- Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing Its Gradient Estimator Bias.Frontiers in neuroscience · 2021Article
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Authors and funding
7 authors.
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Abstract
Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge. Equilibrium propagation is a promising alternative to backpropagation as it only involves local computations, but hardware-oriented studies have so far focused on rate-based networks. In this work, we develop a spiking neural network algorithm called EqSpike, compatible with neuromorphic systems, which learns by equilibrium propagation. Through simulations, we obtain a test recognition accuracy of 97.6% on the MNIST handwritten digits dataset (Mixed National Institute of Standards and Technology), similar to rate-based equilibrium propagation, and comparing favorably to alternative learning techniques for spiking neural networks. We show that EqSpike implemented in silicon neuromorphic technology could reduce the energy consumption of inference and training, respectively, by three orders and two orders of magnitude compared to graphics processing units. Finally, we also show that during learning, EqSpike weight updates exhibit a form of spike-timing-dependent plasticity, highlighting a possible connection with biology.
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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.