While artificial neural networks (ANNs) have been widely adopted in machine learning, researchers are increasingly obsessed by the gaps between ANNs and biological neural networks (BNNs). In this paper, we propose a framework named as Evolutionary Plastic Recurrent Neural Networks} (EPRNN). Inspired by BNN, EPRNN composes Evolution Strategies, Plasticity Rules, and Recursion-based Learning all in one meta learning framework for generalization to different tasks. More specifically, EPRNN incorporates with nested loops for meta learning -- an outer loop searches for optimal initial parameters of the neural network and learning rules; an inner loop adapts to specific tasks. In the inner loop of EPRNN, we effectively attain both long term memory and short term memory by forging plasticity with recursion-based learning mechanisms, both of which are believed to be responsible for memristance in BNNs. The inner-loop setting closely simulate that of BNNs, which neither query from any gradient oracle for optimization nor require the exact forms of learning objectives. To evaluate the performance of EPRNN, we carry out extensive experiments in two groups of tasks: Sequence Predicting, and Wheeled Robot Navigating. The experiment results demonstrate the unique advantage of EPRNN compared to state-of-the-arts based on plasticity and recursion while yielding comparably good performance against deep learning based approaches in the tasks. The experiment results suggest the potential of EPRNN to generalize to variety of tasks and encourage more efforts in plasticity and recursion based learning mechanisms.
翻译:虽然在机器学习中广泛采用人工神经网络(ANNS),但研究人员日益对ANNS和生物神经网络(BNNS)之间的差距着迷。在本文中,我们提议了一个称为进化塑料常态神经网络(EPRNN)的框架。受到BNN、ENPRNN的启发,它组成了进化战略、可塑性规则以及基于回溯的学习,全部在一个元学习框架中,以便概括到不同的任务。更具体地说,ERNNNNE结合了嵌入的元学习循环 -- -- 一个外环搜索神经网络和生物神经网络(BNNNS)的最佳初始参数;一个内环适应具体的任务。在EPRNNNNE的内环环环中,我们有效地实现长期的记忆和短期记忆,方法是利用以回溯性学习为基础的学习机制(两者都被认为对BNNNS的回溯性具有共性)。 内嵌式模拟了BNNNP的密切模拟,既不从任何梯度的梯度或触觉变现,也不要求确切的学习目标形式。要评价EPRNNNNER的绩效,同时将进行广泛的实验,同时将展示基于E的周期性工作的成果与基于S-NBNBR的周期性工作。