Cerebellum-inspired Kernel for Efficient Out-of-Distribution Detection

Abstract Detecting novel stimuli is a fundamental neural function, yet its machine learning counterpart—out-of-distribution (OOD) detection—remains challenging, with models often making overconfident predictions on unseen inputs. Inspired by the strong pattern-separation capabilities of cerebellum-like circuits, we introduce a cerebellum-inspired kernel with an efficient closed-form implementation. Combining random Gaussian projection with Top-k sparsification, the kernel reshapes similarities in high-dimensional space to enhance separability between in-distribution (ID) and OOD samples. On OpenOOD benchmarks, our kernel consistently improves multiple baseline methods, and pairing it with the energy score achieves performance comparable to or exceeding current state-of-the-art approaches. The closed-form design also avoids the high computational cost of large-expansion explicit mapping. These results demonstrate the generality and potential of cerebellar kernels for OOD detection and other tasks requiring efficient pattern separation under limited computational resources. Competing Interest Statement The authors have declared no competing interest. Footnotes zhangyaoduo{at}tju.edu.cn