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Activation Learning by Local Competitions
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Activation Learning by Local Competitions
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Despite its great success, backpropagation has certain limitations that necessitate the investigation of new learning methods. In this study, we present a biologically plausible local learning rule that improves upon Hebb's well-known proposal and discovers unsupervised features by local competitions among neurons. This simple learning rule enables the creation of a forward learning paradigm called activation learning, in which the output activation (sum of the squared output) of the neural network estimates the likelihood of the input patterns, or "learn more, activate more" in simpler terms. For classification on a few small classical datasets, activation learning performs comparably to backpropagation using a fully connected network, and outperforms backpropagation when there are fewer training samples or unpredictable disturbances. Additionally, the same trained network can be used for a variety of tasks, including image generation and completion. Activation learning also achieves state-of-the-art performance on several real-world datasets for anomaly detection. This new learning paradigm, which has the potential to unify supervised, unsupervised, and semi-supervised learning and is reasonably more resistant to adversarial attacks, deserves in-depth investigation.
Forward citations
Cited by 5 Pith papers
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What Does Goodness Measure? A Likelihood-Ratio Account of Forward-Forward Learning
Squared Forward-Forward goodness is the likelihood-ratio statistic for zero-mean populations differing in scale; anisotropic and heavy-tailed cases yield Mahalanobis and saturating (divisive-normalization) forms.
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Supervised Hebbian learning in Deep Counterstream Associative Networks
Supervised counterstream Hebbian learning in deep associative networks reaches high accuracy on binarized MNIST by propagating opposing activity waves linked via local rules.
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Constrained Hebbian Learning Supports Efficient Representational Allocation under Structural Constraints
A constrained Hebbian rule produces audiovisual representations with lower task-information cost (retained input information per unit of task-relevant information) than sparse backpropagation and DDTP at comparable ac...
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Energy-Efficient Information Representation in MNIST Classification Using Biologically Inspired Learning
On a 3-class MNIST subset, a competitive-Hebbian + weight-perturbation rule achieves higher estimated mutual information per nonsilent synapse than BP, but with much lower accuracy and no error bars.
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