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.

基于神经发育程序的一类自组织神经网络模型，具有可塑性，并能从经历中学习和适应不同的控制任务。结果表明，在需要快速适应或具有非稳态奖励的环境中，结构可塑性是有益的。