循环神经网络的理想反馈控制
Ideomotor feedback control in a recurrent neural network
摘要：
提出了一种控制未知环境的神经网络结构。它基于一个随机连接的循环神经网络，感知和动作同时从该网络中读取并反馈。有两种并行学习规则实现了一种认知控制：（i）感知是沿着网络应该可靠地预测其传入刺激的原则学习的;（ii）动作是沿着网络的预测应该匹配目标时间序列的原则学习的。神经网络在其环境中的一致性行为是这两个原则之间相互作用的结果。数值模拟显示了有前途的性能的方法，它可以变成一个本地的，因此“生物合理”，算法。
英文摘要：
The architecture of a neural network controlling an unknown environment is presented. It is based on a randomly connected recurrent neural network from which both perception and action are simultaneously read and fed back. There are two concurrent learning rules implementing a sort of ideomotor control: (i) perception is learned along the principle that the network should predict reliably its incoming stimuli; (ii) action is learned along the principle that the prediction of the network should match a target time series. The coherent behavior of the neural network in its environment is a consequence of the interaction between the two principles. Numerical simulations show the promising performance of the approach, which can be turned into a local, and thus "biologically plausible", algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
