积极的推断和故意的行为
Karl J Friston1,2, Tommaso Salvatori3, Takuya Isomura4
1Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London WC1N 3AR, U.K.
Neural computation
|March 3, 2025
概括
基础认知和感知行为可以从神经网络中出现,如自我组织原理所示. 这项研究通过积极推断和模拟来正式化反应性,感知性和故意行为.
科学领域:
- 理论生物学 理论生物学
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 基础认知和感知行为可能来自体外细胞培养和神经网络.
- 神经网络在与信息景观交互时表现出结构化的行为.
- 自由能源原则为理解自我组织作为自我证明提供了一个框架.
研究的目的:
- 通过自由能量原理的镜头来描述神经网络中的自我组织.
- 在主动推理中,定义和区分反应性,感知性和故意行为.
- 在实验室实验和机器学习基准的模拟中调查新出现的行为.
主要方法:
- 利用主动推理对预测行动后果的模型代理进行推理.
- 介绍了由潜在状态中首选的终点驱动的故意行为的正式解释.
- 在实验室中模拟的神经元培养,玩一个简化的Pong游戏,观察自我组织.
- 应用机器学习基准 (世界导航网格,河内塔) 来研究适应性行为.
主要成果:
- 神经元文化调节了活动以改善游戏玩法,展示了嵌套的,最小化自由能量的过程.
- 模拟区分了反应性,有意识和故意的行为.
- 使用诱导推理将故意行为正式化.
- 适应性行为在诱导性主动推理下迅速有效地出现.
结论:
- 神经网络的新兴特性,如基础认知和感知,可以通过自我证明和积极推断来理解.
- 这项研究提供了反应性,感知性和故意性行为之间的正式区别.
- 主动推断,特别是感应形式,有助于人工智能中高效的适应性行为出现.
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