AI 中文总结
研究利用未标记数据进行可泛化神经群体解码的问题，提出MOJO训练框架，联合自监督学习与监督学习目标，在多数据集上评估，结果显示该框架能提升性能、产生更具可解释性表示且可推广到其他神经模态。
AI 中文摘要
强大而准确的神经解码器对脑机接口和闭环实验等神经技术至关重要。近期工作表明，在尖峰水平对神经数据进行分词有助于多会话预训练并带来最优解码性能。但当前基于尖峰的模型限于监督学习，只能用有配对行为标签的数据集训练。为解决此局限，我们引入MOJO（基于掩码自动编码器的联合训练），这是一个通过掩码自动编码和监督学习目标联合利用自监督学习的尖峰分词模型训练框架。我们在三个尖峰数据集上评估MOJO，涵盖猴子伸手任务时的运动皮层以及小鼠视觉和决策任务时的多区域记录，结果显示其性能优于纯监督学习训练的模型。当使用有限标记数据训练时，这种改进尤其明显，特别是在少样本微调中。纳入自监督学习还产生了更具可解释性的神经元表示，在未针对脑区分类和尖峰统计预测进行明确优化的情况下提高了性能。我们进一步表明，MOJO可推广到语音时的人类皮层脑电图，其性能继续优于纯监督学习训练的模型，且与专门为连续信号设计的神经基础模型相当。总体而言，用自监督学习增强尖峰分词模型可在标签匮乏的情况下提高性能，能在各种任务和物种中使用未标记数据，并推广到其他神经模态。这些结果为训练神经基础模型时更灵活和可扩展的数据使用指明了方向。
英文摘要
Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments. Recent work has shown that tokenizing neural data at the spike level facilitates multi-session pretraining and delivers state-of-the-art decoding performance. However, current spike-based models are restricted to supervised learning (SL), limiting training to datasets with paired behavioural labels. To address this limitation, we introduce MOJO (Masked autOencoder-based JOint training), a training framework for spike-tokenizing models that jointly leverages self-supervised learning (SSL) via masked autoencoding and SL objectives. We evaluate MOJO on three spiking datasets spanning monkey motor cortex during reaching tasks and multi-regional mouse recordings during vision and decision making tasks, demonstrating superior performance over purely SL-trained models. This improvement is especially pronounced when training with limited labelled data, particularly in few-shot finetuning, where only a small amount of labelled data from a new session is available. Incorporating SSL also yields more interpretable neuronal representations, improving performance on brain region classification and spike-statistics prediction without explicit optimization for these tasks. We further show that MOJO generalizes beyond spiking data to human electrocorticography during speech, where it continues to outperform purely SL-trained models and achieves performance comparable to neuro-foundation models (NFMs) designed specifically for continuous signals. Overall, augmenting spike-tokenizing models with SSL improves performance in label-impoverished settings and enables the use of unlabelled data across various tasks and species, while generalizing to other neural modalities. These results suggest a path towards more flexible and scalable data usage when training NFMs.
