Neuroscience-Inspired Encoding and Learning: A Path to Robust Representation Learning

Mohammed-Yassine Habibi, Klea Ziu, Makoto Yamada

Keywords: Neuroscience-inspired models, Self-supervised learning, Non-contrastive learning, Representation learning

Abstract:

We present a neuroscience-inspired strategy to improve the robustness of encoder models by combining Artificial Kuramoto Oscillatory Neurons (AKOrN) with PhiNet, a non-contrastive self-supervised learning approach. AKOrN models neurons as interacting oscillators using the Kuramoto framework, while PhiNet draws inspiration from the hippocampus and temporal prediction theory to build stable, generalizable representations without relying on negative pairs.

Our motivation stems from the challenge of learning robust representations in domains such as brain imaging, where datasets are small, noisy, and highly variable. Conventional vision models tend to overfit and are vulnerable to adversarial perturbations. By integrating the oscillatory dynamics of AKOrN with the predictive structure of PhiNet, we aim to obtain models that are inherently more robust without the need for adversarial training, synthetic augmentation, or heavy post-training procedures.

Submission Number: 71