Abstract
Local field potentials (LFPs) can be routinely recorded alongside spiking activity in intracortical neural experiments, measure a larger complementary spatiotemporal scale of brain activity for scientific inquiry, and can offer practical advantages over spikes, including greater long-term stability, robustness to electrode degradation, and lower power requirements. Despite these advantages, recent neural modeling frameworks have largely focused on spiking activity since LFP signals pose inherent modeling challenges due to their aggregate, population-level nature, often leading to lower predictive power for downstream task variables such as motor behavior. To address this challenge, we introduce a cross-modal knowledge distillation framework that transfers high-fidelity representational knowledge from pretrained multi-session spike transformer models to LFP transformer models. Specifically, we first train a teacher spike model across multiple recording sessions using a masked autoencoding objective with a session-specific neural tokenization strategy. We then align the latent representations of the student LFP model to those of the teacher spike model. Our results show that the Distilled LFP models consistently outperform single- and multi-session LFP baselines in both fully unsupervised and supervised settings, and can generalize to other sessions without additional distillation while maintaining superior performance. These findings demonstrate that cross-modal knowledge distillation is a powerful and scalable approach for leveraging high-performing spike models to develop more accurate LFP models.
Abstract (translated)
局部场电位（LFPs）可以在皮层神经实验中与脉冲活动常规记录在一起，能够测量更大范围的空间和时间尺度的大脑活动，为科学研究提供补充。相比脉冲活动，LFP信号具有更高的长期稳定性、对电极退化的鲁棒性以及更低的功耗等实用优势。尽管有这些优点，最近的神经建模框架大多集中在脉冲活动上，因为LFP信号因其群体水平和聚合性质而给建模带来固有的挑战，这通常会导致对未来任务变量（如运动行为）预测能力较低。 为了解决这一问题，我们引入了一种跨模式知识蒸馏框架，该框架将预训练的多会话脉冲变换模型中的高保真表示知识转移到LFP变换模型中。具体来说，首先使用带有特定于每个会话的神经标记策略的掩码自动编码目标，在多个记录会话上对教师脉冲模型进行训练。然后使学生LFP模型的潜在表征与教师脉冲模型的相一致。 我们的结果显示，蒸馏后的LFP模型在完全无监督和有监督设置中都优于单个会话和多会话LFP基线，并且能够推广到其他会话，而无需进一步的蒸馏同时保持优越性能。这些发现表明跨模式知识蒸馏是一种强大且可扩展的方法，用于利用高性能脉冲模型来开发更准确的LFP模型。
URL
https://arxiv.org/abs/2512.12461
