用地形自编码器预测自感性皮质解剖学和神经编码
Max Grogan1, Kyle P Blum2, Yufei Wu1
1Department of Bioengineering, Imperial College London, London, United Kingdom.
PLoS computational biology
|December 4, 2024
概括
研究人员创建了一个新的计算模型来理解自身感知,身体位置的感觉. 地形变异自编码器 (topo-VAE) 成功预测了大脑如何绘制运动图,为感觉运动控制提供了洞察力.
科学领域:
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自身感知,肢体位置的感觉,对于运动控制至关重要,但人们对其了解甚少.
- 现有的模型对体感皮质如何在自然运动中代表肢体姿势缺乏清晰度.
研究的目的:
- 开发一个计算模型,从自然运动数据中生成自知觉的假定皮质地图.
- 为了研究肢体姿势在体感皮层中的表现.
主要方法:
- 开发具有横向连接 (topo-VAE) 的地形变异自动编码器.
- 使用大量的自然运动数据数据集进行模型计算.
- 将模型输出与子完成任务的现有神经生理学数据进行比较.
主要成果:
- 在没有先前的动力学知识的情况下,托波-VAE模型在以手为中心的坐标中准确地复制了自感受体场的取决于速度的特征.
- 该模型预测的神经元偏好方向 (PDs) 的分布与子大脑的经验记录保持一致.
- 该模型预测了PDs的blob-and-pinwheel几何形状,并表明很少有神经元编码单个关节.
结论:
- 托波-VAE为理解感觉运动表征和神经多元体提供了一个原则框架.
- 该模型的成功为恢复脑计算机接口的感官反和控制人形机器人的应用提供了基础.
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