ArticleFrontiers in neuroscience2018
Deep Supervised Learning Using Local Errors.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Contrastive signal-dependent plasticity: Self-supervised learning in spiking neural circuits.Science advances · 2024Article
- Learnable Leakage and Onset-Spiking Self-Attention in SNNs with Local Error Signals.Sensors (Basel, Switzerland) · 2023Article
- Efficient training of spiking neural networks with temporally-truncated local backpropagation through time.Frontiers in neuroscience · 2023Article
- Article
- Neuromorphic artificial intelligence systems.Frontiers in neuroscience · 2022Review
- Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits.Nature neuroscience · 2021Article
- Learning Without Feedback: Fixed Random Learning Signals Allow for Feedforward Training of Deep Neural Networks.Frontiers in neuroscience · 2021Article
- BlocTrain: Block-Wise Conditional Training and Inference for Efficient Spike-Based Deep Learning.Frontiers in neuroscience · 2021Article
- Can the Brain Do Backpropagation? -Exact Implementation of Backpropagation in Predictive Coding Networks.Advances in neural information processing systems · 2020Article
- Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE).Frontiers in neuroscience · 2020Article
- Learning to Approximate Functions Using Nb-Doped SrTiOFrontiers in neuroscience · 2020Article
- Review
- Theories of Error Back-Propagation in the Brain.Trends in cognitive sciences · 2019Review
- DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance.Frontiers in bioengineering and biotechnology · 2019Article
- Direct Feedback Alignment With Sparse Connections for Local Learning.Frontiers in neuroscience · 2019Article
- Review
- Deep Learning With Spiking Neurons: Opportunities and Challenges.Frontiers in neuroscience · 2018Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Error backpropagation is a highly effective mechanism for learning high-quality hierarchical features in deep networks. Updating the features or weights in one layer, however, requires waiting for the propagation of error signals from higher layers. Learning using delayed and non-local errors makes it hard to reconcile backpropagation with the learning mechanisms observed in biological neural networks as it requires the neurons to maintain a memory of the input long enough until the higher-layer errors arrive. In this paper, we propose an alternative learning mechanism where errors are generated locally in each layer using fixed, random auxiliary classifiers. Lower layers could thus be trained independently of higher layers and training could either proceed layer by layer, or simultaneously in all layers using local error information. We address biological plausibility concerns such as weight symmetry requirements and show that the proposed learning mechanism based on fixed, broad, and random tuning of each neuron to the classification categories outperforms the biologically-motivated feedback alignment learning technique on the CIFAR10 dataset, approaching the performance of standard backpropagation. Our approach highlights a potential biological mechanism for the supervised, or task-dependent, learning of feature hierarchies. In addition, we show that it is well suited for learning deep networks in custom hardware where it can drastically reduce memory traffic and data communication overheads. Code used to run all learning experiments is available under https://gitlab.com/hesham-mostafa/learning-using-local-erros.git.
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Registered trials
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.