一个通用的皮层内电机解码器
Joel Ye1, Fabio Rizzoglio2, Adam Smoulder1
1Carnegie Mellon University.
bioRxiv : the preprint server for biology
|February 20, 2025
概括
这项研究探讨了使用神经活动数据进行运动解码的基础模型. 虽然这些模型对于许多任务是有效的,但它们面临着传感器变化和输出刻板印象的局限性.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 生物工程是生物工程.
背景情况:
- 了解运动控制的神经基础对于推进神经技术至关重要.
- 传统方法经常简化神经数据,限制了广泛的适用性.
- 基金会模型通过整合庞大的数据集提供了一种新的方法.
研究的目的:
- 评估基础模型的有效性,特别是自回归变压器,用于使用大规模神经群体尖端活动进行动力解码.
- 评估预训练模型在各种运动解码任务和神经数据变异中的通用性.
主要方法:
- 在从子和人类获得的2000小时内皮层微电极数据上预先训练了一种自回归的变压器模型.
- 与广泛的运动共变量配对的神经激增活动.
- 在8个下游电机解码任务和各种神经分布转移中评估模型性能.
主要成果:
- 预训练的基础模型展示了广泛的实用性,改善了多个下游解码任务的性能.
- 该模型在不同的神经数据分布中展示了概括能力.
- 确定了与神经数据集固有的传感器可变性和输出刻板印象有关的局限性.
结论:
- 基金会的模型显示,通过利用大型,多样化的神经数据集来实现动力解码具有前途.
- 虽然强大,但当前的自回归变压器缩放可能无法完全克服固有的数据限制.
- 需要进一步的研究来解决传感器变化和输出刻板印象,以增强神经解码.
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