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Inspired by offline-to-online distillation strategies in speech recognition, REALM tr","title":"REALM: Retrospective Encoder Alignment for LFP Modeling","url":"https://arxiv.org/abs/2605.14867","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.14867v1 Announce Type: cross \nAbstract: Spike activity has been the dominant neural signal for behavior decoding due to its high spatial and temporal resolution. However, as brain-computer interfaces (BCIs) move toward high channel counts and wireless operation, the high sampling frequency of spike signals becomes a bottleneck due to high power and bandwidth requirements. Local field potentials (LFPs) represent a different spatial-temporal scale of brain activity compared to spikes, offering key advantages including improved long-term stability, reduced energy consumption, and lower bandwidth requirement. 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