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

TL;DR

This work introduces 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 and illustrates the resulting robustness behavior on MNIST-C, where RECAP remains robust under diverse corruption without exposure to corrupted training samples.

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.

Figures (6)

• Figure 1: RECAP for robust image classification. An input image drives an untrained echo-state reservoir, producing temporal population activity that is time-averaged and discretized into activation levels. The discretized population code is converted into a binary co-activation mask, which updates a class-wise continuous prototype state via a Hebbian-like potentiation–decay rule. After thresholding, inference is performed by matching the test mask against all class prototypes and selecting the highest-overlap class.
• Figure 2: Corrupted examples of the MNIST-C dataset used in the experiments. The method consists of 15 types of algorithmically generated corruptions from noise (top-left), blur (bottom-left), weather (top-right), and digital categories (bottom-right). Each type has 5 levels of severity (i.e., 75 distinct corruptions).
• Figure 3: Motivation and intuition for the RECAP readout. (a) Standard reservoir computing relies on a typically linear readout, which can be sensitive to feature distortions under input degradations. (b) Local Hebbian-style plasticity provides an inspiration for strengthening repeatedly co-activated relations li2014activity. (c) Repeated inputs can induce structured co-activation patterns in reservoir population responses. (d) RECAP leverages these ideas by learning class-wise relational prototypes from co-activation masks using a Hebbian-like potentiation–decay update and performing inference via prototype matching. Figures modified from li2014activitysuarez2024connectomefusi2016neurons.
• Figure 4: Workflow for prototype formation. An input image is processed by the reservoir for $T$ steps, yielding a time-averaged state vector that is quantized into $K$ discrete levels. This induces a binary co-activation mask over unit pairs. During training, each sample updates its class prototype via Hebbian potentiation–decay; prototypes are then thresholded into binary templates for inference.
• Figure 5: Radar plot of per-corruption Relative CE on MNIST-C across 15 types of corruption, each represented as a point on the circular chart. The concentric contour lines indicate Relative mCE scores. For visualization, the radial axis is inverted so that lower Relative CE values (better robustness) appear farther from the center. AlexNet, used as the standard, is represented by a perfect gray circle reflecting an mCE of 100% across all categories. The larger the area enclosed by a model’s line, the better the robustness. The RECAP model, highlighted in the chart in yellow, shows the largest area, indicating superior robustness compared to baseline.