Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling
跨模态表征知识蒸馏用于增强基于尖峰的LFP建模
机构 * Ming Hsieh Department of Electrical and Computer Engineering(明希斯电气与计算机工程系) ; Thomas Lord Department of Computer Science(托马斯·劳德计算机科学系) ; Alfred E. Mann Department of Biomedical Engineering(阿尔弗雷德·E·曼生物医学工程系) ; Neuroscience Graduate Program University of Southern California(神经科学研究生项目美国南加州大学)
专题命中 其他多模态 :cross-modal(title,abstract);分类 cs.AI
AI总结 本文提出跨模态知识蒸馏框架,通过将预训练的尖峰模型知识转移至LFP模型,提升LFP建模的准确性和泛化能力。
Comments Published at the 39th Annual Conference on Neural Information Processing Systems 2025. Code is available at https://github.com/ShanechiLab/CrossModalDistillation
Journal ref NeurIPS 2025