模拟复杂的动物行为与潜状态的反向增强学习学习
Aditi Jha1,2, Victor Geadah3,2, Jonathan W Pillow2
1Department of Electrical and Computer Engineering, Princeton University.
bioRxiv : the preprint server for biology
|November 28, 2024
概括
我们开发了离散的动态反向增强学习 (dDIRL) 来模拟复杂的动物导航. 这种方法揭示了内部状态驱动行为,并确定了小鼠的独特探索模式.
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
- 动物行为 动物行为
背景情况:
- 了解复杂的动物行为是将大脑活动与行为联系起来的关键.
- 目前的模型在与长期的,自然主义的行为,如导航,斗争.
- 需要动态建模动物延长的运动.
研究的目的:
- 引入离散的动态反向增强学习 (dDIRL),这是一个用于建模长期动物行为的新范式.
- 应用dDIRL来理解水不足小鼠的导航.
- 推断内部状态和奖励功能驱动观察到的行为.
主要方法:
- 开发了dDIRL,这是一个潜在的状态依赖的行为建模框架.
- 利用期望最大化来从观察到的数据中推断奖励函数和状态转换.
- 将模型应用于个别缺水的小鼠在迷宫中进行导航.
主要成果:
- 确定了三种不同的内部状态,控制了小鼠的行为.
- 发现了一种一致的寻找水的状态,虽然并不总是占主导地位.
- 根据迷宫探索模式发现了两种不同的动物群.
结论:
- dDIRL提供了对内部状态和奖励如何塑造复杂行为的细微了解.
- 该模型提供了对自然导航神经基础的洞察.
- 这一框架推动了在复杂的环境中研究动态动物行为的研究.
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