将模型单元映射到视觉神经元中, 揭示了社会行为的代码
Benjamin R Cowley1,2, Adam J Calhoun3, Nivedita Rangarajan3
1Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA. cowley@cshl.edu.
Nature
|May 22, 2024
概括
研究人员开发了一种新的AI模型, 将神经活动映射到动物行为中. 通过模拟训练中的神经元干扰, 他们发现了导致果社会相互作用的复杂种群代码.
科学领域:
- 神经科学
- 计算生物学
- 动物的行为
背景情况:
- 动物的行为来自于感官处理和运动控制.
- 模拟神经对行为贡献对于理解感觉运动转换至关重要.
研究的目的:
- 开发一种用于将内部神经网络单元映射到真实神经元的新型建模方法.
- 预测来自神经系统乱的行为变化.
- 在视觉引导的社会行为中模拟Drosophila melanogaster男性的感觉运动转变.
主要方法:
- 引入了'淘汰训练',在训练过程中扰乱深层神经网络以模仿真正的神经干扰.
- 将模型应用于进行复杂社会行为的Drosophila男性.
- 确定了特定的神经细胞类型及其在行为中的作用.
主要成果:
- 该模型确定了内部网络单元和真实神经元之间的一对一映射.
- 与之前的工作相反,视觉投影神经元的组合, 而不是离散的道, 驱动男性的社会互动.
- 一个丰富的人口代码, 涉及神经元在非社会行为, 发现驱动女性的互动.
结论:
- 这个框架将神经乱的行为效应整合到一个统一的模型中.
- 提供刺激到神经元类型到行为地图.
- 能够在未来将脑电线图整合到模型中.
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