进化的自组装神经网络：从自发活动到依赖经验的学习
Evolving Self-Assembling Neural Networks: From Spontaneous Activity to Experience-Dependent Learning
AI总结：
本文提出LNDP自组织神经网络，结合图Transformer与感觉神经元自发活动机制，实现依赖活动和奖励的突触及结构可塑性，可从随机或空网络中持续学习，并在快速适应与非平稳奖励环境中展现优势。
摘要：
生物神经网络的特征在于其高度的可塑性，这一核心属性使自然生物体具备显著的适应能力。重要的是，这种能力同时影响神经系统的突触强度和拓扑结构。相比之下，人工神经网络主要被设计为静态、全连接的结构，在面对不断变化的环境和新颖输入时，可能极其脆弱。基于此前关于神经发育程序（Neural Developmental Programs，NDPs）的工作，我们提出了一类能够以依赖活动和奖励的方式实现突触与结构可塑性的自组织神经网络，我们将其称为终身神经发育程序（Lifelong Neural Developmental Program，LNDP）。我们给出了这种网络的一个实例，该实例构建于图Transformer架构之上，并提出了一种基于感觉神经元自发活动的经验前可塑性机制。我们的结果证明，该模型能够从随机连接或空网络开始，在不同控制任务中从经验中学习。我们进一步表明，结构可塑性在需要快速适应或奖励非平稳的环境中具有优势。
英文摘要：
Biological neural networks are characterized by their high degree of plasticity, a core property that enables the remarkable adaptability of natural organisms. Importantly, this ability affects both the synaptic strength and the topology of the nervous systems. Artificial neural networks, on the other hand, have been mainly designed as static, fully connected structures that can be notoriously brittle in the face of changing environments and novel inputs. Building on previous works on Neural Developmental Programs (NDPs), we propose a class of self-organizing neural networks capable of synaptic and structural plasticity in an activity and reward-dependent manner which we call Lifelong Neural Developmental Program (LNDP). We present an instance of such a network built on the graph transformer architecture and propose a mechanism for pre-experience plasticity based on the spontaneous activity of sensory neurons. Our results demonstrate the ability of the model to learn from experiences in different control tasks starting from randomly connected or empty networks. We further show that structural plasticity is advantageous in environments necessitating fast adaptation or with non-stationary rewards.
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。
