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arXiv Machine Learning By Daniel Levine, David van Dijk, Kenneth Kay

AutoHebb: Self-Modifying Neural Networks that Zero-Shot Infer and Continually Learn

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AutoHebb introduces autonomous Hebbian networks that combine external feedback with internally generated neuromodulatory signals to gate a biologically grounded 3‑factor Hebbian learning rule. These networks, optimized via meta‑learning for fixed weights, demonstrate strong inductive biases and autonomous learning control, enabling zero‑shot inference and continual learning without external optimization. They outperform contemporary models on these tasks and can be reverse‑engineered into explicit algorithms, highlighting the importance of meta‑learning, Hebbian plasticity, and autonomous learning control.

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