ArticleFrontiers in neuroscience2017
Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
- Neural vs Neuromorphic Interfaces: Where Are We Standing?Chemical reviews · 2025Review
- Self-Contrastive Forward-Forward algorithm.Nature communications · 2025Article
- SpikeAtConv: an integrated spiking-convolutional attention architecture for energy-efficient neuromorphic vision processing.Frontiers in neuroscience · 2025Article
- Probabilistic metaplasticity for continual learning with memristors in spiking networks.Scientific reports · 2024Article
- Mapless mobile robot navigation at the edge using self-supervised cognitive map learners.Frontiers in robotics and AI · 2024Article
- Sparse-firing regularization methods for spiking neural networks with time-to-first-spike coding.Scientific reports · 2023Article
- Structural plasticity for neuromorphic networks with electropolymerized dendritic PEDOT connections.Nature communications · 2023Article
- Neural spiking for causal inference and learning.PLoS computational biology · 2023Article
- Supervised Learning Algorithm Based on Spike Train Inner Product for Deep Spiking Neural Networks.Brain sciences · 2023Article
- Remembrance of things perceived: Adding thalamocortical function to artificial neural networks.Frontiers in integrative neuroscience · 2023Review
- Training multi-layer spiking neural networks with plastic synaptic weights and delays.Frontiers in neuroscience · 2023Article
- Prediction of SMILE surgical cutting formula based on back propagation neural network.International journal of ophthalmology · 2023Article
- Spiking CMOS-NVM mixed-signal neuromorphic ConvNet with circuit- and training-optimized temporal subsampling.Frontiers in neuroscience · 2023Article
- SAM: A Unified Self-Adaptive Multicompartmental Spiking Neuron Model for Learning With Working Memory.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
- Article
- Event-Based Update of Synapses in Voltage-Based Learning Rules.Frontiers in neuroinformatics · 2021Article
- Gradient Decomposition Methods for Training Neural Networks With Non-ideal Synaptic Devices.Frontiers in neuroscience · 2021Article
- Adaptive Extreme Edge Computing for Wearable Devices.Frontiers in neuroscience · 2021Review
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4 authors.
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Abstract
An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.
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