Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism

• URL: http://arxiv.org/abs/2504.11558v1
• Date: Tue, 15 Apr 2025 19:00:53 GMT
• Title: Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism
• Authors: Mete Erdogan, Cengiz Pehlevan, Alper T. Erdogan,
• Abstract summary: We introduce the Error Broadcast and Decorrelation (EBD) algorithm, a novel learning framework that addresses the credit assignment problem in neural networks by directly broadcasting output error to individual layers.<n>EBD defines layerwise loss functions to penalize correlations between layer activations and output errors, offering a principled approach to error broadcasting without the need for weight transport.<n> Numerical experiments demonstrate that EBD achieves performance comparable to or better than known error-broadcast methods on benchmark datasets.
• Score: 34.75158394131716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the Error Broadcast and Decorrelation (EBD) algorithm, a novel learning framework that addresses the credit assignment problem in neural networks by directly broadcasting output error to individual layers. Leveraging the stochastic orthogonality property of the optimal minimum mean square error (MMSE) estimator, EBD defines layerwise loss functions to penalize correlations between layer activations and output errors, offering a principled approach to error broadcasting without the need for weight transport. The optimization framework naturally leads to the experimentally observed three-factor learning rule and integrates with biologically plausible frameworks to enhance performance and plausibility. Numerical experiments demonstrate that EBD achieves performance comparable to or better than known error-broadcast methods on benchmark datasets. While the scalability of EBD to very large or complex datasets remains to be further explored, our findings suggest it provides a biologically plausible, efficient, and adaptable alternative for neural network training. This approach could inform future advancements in artificial and natural learning paradigms.

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