Artificial Kuramoto Oscillatory Neurons

• URL: http://arxiv.org/abs/2410.13821v2
• Date: Fri, 14 Feb 2025 05:29:53 GMT
• Title: Artificial Kuramoto Oscillatory Neurons
• Authors: Takeru Miyato, Sindy Löwe, Andreas Geiger, Max Welling,
• Abstract summary: It has long been known in both neuroscience and AI that ''binding'' between neurons leads to a form of competitive learning. We introduce Artificial rethinking together with arbitrary connectivity designs such as fully connected convolutional, or attentive mechanisms. We show that this idea provides performance improvements across a wide spectrum of tasks such as unsupervised object discovery, adversarial robustness, uncertainty, and reasoning.
• Score: 65.16453738828672
• License:
• Abstract: It has long been known in both neuroscience and AI that ``binding'' between neurons leads to a form of competitive learning where representations are compressed in order to represent more abstract concepts in deeper layers of the network. More recently, it was also hypothesized that dynamic (spatiotemporal) representations play an important role in both neuroscience and AI. Building on these ideas, we introduce Artificial Kuramoto Oscillatory Neurons (AKOrN) as a dynamical alternative to threshold units, which can be combined with arbitrary connectivity designs such as fully connected, convolutional, or attentive mechanisms. Our generalized Kuramoto updates bind neurons together through their synchronization dynamics. We show that this idea provides performance improvements across a wide spectrum of tasks such as unsupervised object discovery, adversarial robustness, calibrated uncertainty quantification, and reasoning. We believe that these empirical results show the importance of rethinking our assumptions at the most basic neuronal level of neural representation, and in particular show the importance of dynamical representations. Code: https://github.com/autonomousvision/akorn Project page: https://github.com/takerum/akorn_project_page

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