Enhancing deep neural networks through complex-valued representations and Kuramoto synchronization dynamics

• URL: http://arxiv.org/abs/2502.21077v1
• Date: Fri, 28 Feb 2025 14:10:42 GMT
• Title: Enhancing deep neural networks through complex-valued representations and Kuramoto synchronization dynamics
• Authors: Sabine Muzellec, Andrea Alamia, Thomas Serre, Rufin VanRullen,
• Abstract summary: We investigate whether synchrony-based mechanisms can enhance object encoding in artificial models trained for visual categorization.<n>We combine complex-valued representations with Kuramoto dynamics to promote phase alignment, facilitating the grouping of features belonging to the same object.<n>Our findings highlight the potential of synchrony-driven mechanisms to enhance deep learning models, improving their performance, robustness, and generalization.
• Score: 13.611995923070426
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural synchrony is hypothesized to play a crucial role in how the brain organizes visual scenes into structured representations, enabling the robust encoding of multiple objects within a scene. However, current deep learning models often struggle with object binding, limiting their ability to represent multiple objects effectively. Inspired by neuroscience, we investigate whether synchrony-based mechanisms can enhance object encoding in artificial models trained for visual categorization. Specifically, we combine complex-valued representations with Kuramoto dynamics to promote phase alignment, facilitating the grouping of features belonging to the same object. We evaluate two architectures employing synchrony: a feedforward model and a recurrent model with feedback connections to refine phase synchronization using top-down information. Both models outperform their real-valued counterparts and complex-valued models without Kuramoto synchronization on tasks involving multi-object images, such as overlapping handwritten digits, noisy inputs, and out-of-distribution transformations. Our findings highlight the potential of synchrony-driven mechanisms to enhance deep learning models, improving their performance, robustness, and generalization in complex visual categorization tasks.

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