Recap Local Hebbian Prototype Learning As A Selforganizing Readout For Reservoir Dynamics

# RECAP: Local Hebbian Prototype Learning as a Self-Organizing Readout for Reservoir Dynamics

**arXiv ID:** 2603.06639
**Authors:** Heng Zhang
**Published:** 2026-02-25T08:28:35Z
**Abstract:**
Robust perception in brains is often attributed to high-dimensional population activity together with local plasticity mechanisms that reinforce recurring structure. In contrast, most modern image recognition systems are trained by error backpropagation and end-to-end gradient optimization, which are not naturally aligned with local computation and local plasticity. We introduce RECAP (Reservoir Computing with Hebbian Co-Activation Prototypes), a bio-inspired learning strategy for robust image classification that couples untrained reservoir dynamics with a self-organizing Hebbian prototype readout. RECAP discretizes time-averaged reservoir responses into activation levels, constructs a co-activation mask over reservoir unit pairs, and incrementally updates class-wise prototype matrices via a Hebbian-like potentiation-decay rule. Inference is performed by overlap-based prototype matching. The method avoids error backpropagation and is naturally compatible with online prototype updates. We illustrate the resulting robustness behavior on MNIST-C, where RECAP remains robust under diverse corruptions without exposure to corrupted training samples.

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This skill is generated from the arXiv paper: RECAP: Local Hebbian Prototype Learning as a Self-Organizing Readout for Reservoir Dynamics (2603.06639).

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## References

- [arXiv:2603.06639](http://arxiv.org/abs/2603.06639v1)