Artificial neural networks (ANNs) are typically confined to accomplishing pre-defined tasks by learning a set of static parameters. In contrast, biological neural networks (BNNs) can adapt to various new tasks by continually updating their connection weights based on their observations, which is aligned with the paradigm of learning effective learning rules in addition to static parameters, e.g., meta-learning. Among broad classes of biologically inspired learning rules, Hebbian plasticity updates the neural network weights using local signals without the guide of an explicit target function, closely simulating the learning of BNNs. However, typical plastic ANNs using large-scale meta-parameters violate the nature of the genomics bottleneck and deteriorate the generalization capacity. This work proposes a new learning paradigm decomposing those connection-dependent plasticity rules into neuron-dependent rules thus accommodating $O(n^2)$ learnable parameters with only $O(n)$ meta-parameters. The decomposed plasticity, along with different types of neural modulation, are applied to a recursive neural network starting from scratch to adapt to different tasks. Our algorithms are tested in challenging random 2D maze environments, where the agents have to use their past experiences to improve their performance without any explicit objective function and human intervention, namely learning by interacting. The results show that rules satisfying the genomics bottleneck adapt to out-of-distribution tasks better than previous model-based and plasticity-based meta-learning with verbose meta-parameters.
翻译:人工神经网络(ANNS)通常局限于通过学习一套静态参数完成预先确定的任务。相比之下,生物神经网络(BNNS)可以不断更新基于观测的连接权重,从而适应各种新任务,这种连接权重与学习有效学习规则的范式相一致,而这种模式与静态参数(例如元学习)是相一致的。在生物启发学习规则的广泛类别中,Hebbian可塑性利用当地信号更新神经网络重量,而没有明确的目标功能指南,密切模拟BNS的学习。然而,生物神经网络(BNNS)可以不断更新其基于观察的连接权重,从而根据观察权重,不断更新连接权重。Hebbian可学习的参数中只有$O(n)元模型元参数。 与不同种类的神经调和不同类型神经调制的模型一起,将典型的软性软性内分解型自动自动自动调整,适用于一个反复的内向型网络性质的性质,即从反复的内分级的内分级操作到任何直径的内向式的演的动作。