ArticleFrontiers in neuroscience2021
Learning Without Feedback: Fixed Random Learning Signals Allow for Feedforward Training of Deep Neural Networks.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Backpropagation-free spiking neural networks with the forward-forward algorithm.Scientific reports · 2026Article
- Closed-form feedback-free learning with forward projection.Nature communications · 2026Article
- Three-factor learning in spiking neural networks: An overview of methods and trends from a machine learning perspective.Patterns (New York, N.Y.) · 2025Review
- Self-Contrastive Forward-Forward algorithm.Nature communications · 2025Article
- A Learning Probabilistic Boolean Network Model of a Manufacturing Process with Applications in System Asset Maintenance.Entropy (Basel, Switzerland) · 2025Article
- TDE-3: an improved prior for optical flow computation in spiking neural networks.Frontiers in neuroscience · 2025Article
- Training an Ising machine with equilibrium propagation.Nature communications · 2024Article
- Information bottleneck-based Hebbian learning rule naturally ties working memory and synaptic updates.Frontiers in computational neuroscience · 2024Article
- Biologically plausible local synaptic learning rules robustly implement deep supervised learning.Frontiers in neuroscience · 2023Article
- Introducing principles of synaptic integration in the optimization of deep neural networks.Nature communications · 2022Article
- Neuromorphic artificial intelligence systems.Frontiers in neuroscience · 2022Review
- A Sparsity-Driven Backpropagation-Less Learning Framework Using Populations of Spiking Growth Transform Neurons.Frontiers in neuroscience · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While the backpropagation of error algorithm enables deep neural network training, it implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward and backward passes are completed. Not only do these constraints preclude biological plausibility, but they also hinder the development of low-cost adaptive smart sensors at the edge, as they severely constrain memory accesses and entail buffering overhead. In this work, we show that the one-hot-encoded labels provided in supervised classification problems, denoted as targets, can be viewed as a proxy for the error sign. Therefore, their fixed random projections enable a layerwise feedforward training of the hidden layers, thus solving the weight transport and update locking problems while relaxing the computational and memory requirements. Based on these observations, we propose the direct random target projection (DRTP) algorithm and demonstrate that it provides a tradeoff between accuracy and computational cost that is suitable for adaptive edge computing devices.
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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.