The brain is a nonlinear and highly Recurrent Neural Network (RNN). This RNN is surprisingly plastic and supports our astonishing ability to learn and execute complex tasks. However, learning is incredibly complicated due to the brain's nonlinear nature and the obscurity of mechanisms for determining the contribution of each synapse to the output error. This issue is known as the Credit Assignment Problem (CAP) and is a fundamental challenge in neuroscience and Artificial Intelligence (AI). Nevertheless, in the current understanding of cognitive neuroscience, it is widely accepted that a feedback loop systems play an essential role in synaptic plasticity. With this as inspiration, we propose a computational model by combining Neural Networks (NN) and nonlinear optimal control theory. The proposed framework involves a new NN-based actor-critic method which is used to simulate the error feedback loop systems and projections on the NN's synaptic plasticity so as to ensure that the output error is minimized.

翻译：大脑是一个非线性且高度循环的神经网络。这一循环神经网络具有惊人的可塑性，支撑着我们学习和执行复杂任务的超凡能力。然而，由于大脑的非线性特性以及确定每个突触对输出误差贡献的机制尚不明确，学习过程极其复杂。这一问题被称为信用分配问题，是神经科学与人工智能领域的根本挑战。尽管如此，在当前的认知神经科学理解中，反馈回路系统被广泛认为在突触可塑性中发挥着关键作用。受此启发，我们通过结合神经网络与非线性最优控制理论提出了一种计算模型。该框架涉及一种基于神经网络的新型Actor-Critic方法，用于模拟误差反馈回路系统及其对神经网络突触可塑性的影响，从而确保输出误差最小化。