虚拟动物可以预测不同行为的神经活动结构
Diego Aldarondo1,2, Josh Merel3,4, Jesse D Marshall5,6
1Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA. diegoaldarondo@gmail.com.
Nature
|June 11, 2024
概括
研究人员使用人工智能和物理模拟创建了一个虚拟的动物来了解大脑对运动的控制. 虚拟模型
科学领域:
- 神经科学
- 人工智能
- 生物物理
背景情况:
- 了解大脑如何控制动物复杂的运动是一个重大挑战.
- 现有的模型往往难以弥合神经活动与可观察行为之间的差距.
研究的目的:
- 开发一种新的计算模型, 将神经原理与运动控制联系起来.
- 研究特定大脑区域的神经活动与运动执行的关系.
主要方法:
- 在物理模拟器中构建了一个"虚拟动物".
- 使用深度强化学习来训练人工神经网络来控制虚拟动物.
- 将神经网络活动与真正的,自由移动的老鼠的神经记录进行了比较.
主要成果:
- 虚拟动物的神经网络活动比单独的运动特征更能预测真正的老鼠大脑活动.
- 感应运动皮质中的神经活动与反向动力学原理相一致.
- 该模型的潜变性解释了神经的变性和稳定性,与最佳反控制一致.
结论:
- 虚拟动物的生物物理真实模拟为神经科学研究提供了强大的工具.
- 这种方法可以阐明运动控制的神经基础并验证理论框架.
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