In biological evolution complex neural structures grow from a handful of
cellular ingredients. As genomes in nature are bounded in size, this complexity
is achieved by a growth process where cells communicate locally to decide
whether to differentiate, proliferate and connect with other cells. This
self-organisation is hypothesized to play an important part in the
generalisation, and robustness of biological neural networks. Artificial neural
networks (ANNs), on the other hand, are traditionally optimized in the space of
weights. Thus, the benefits and challenges of growing artificial neural
networks remain understudied. Building on the previously introduced Neural
Developmental Programs (NDP), in this work we present an algorithm for growing
ANNs that solve reinforcement learning tasks. We identify a key challenge:
ensuring phenotypic complexity requires maintaining neuronal diversity, but
this diversity comes at the cost of optimization stability. To address this, we
introduce two mechanisms: (a) equipping neurons with an intrinsic state
inherited upon neurogenesis; (b) lateral inhibition, a mechanism inspired by
biological growth, which controlls the pace of growth, helping diversity
persist. We show that both mechanisms contribute to neuronal diversity and
that, equipped with them, NDPs achieve comparable results to existing direct
and developmental encodings in complex locomotion tasks

该研究探讨了通过细胞间的局部通信和自组织来实现神经网络的生长过程，引入了两种机制以确保保持神经元多样性，同时解决了优化稳定性和神经元多样性之间的矛盾。实验结果表明，通过这两种机制，神经发育程序在复杂运动任务中取得了与现有编码方式相当的结果。

成长中的人工神经网络控制：神经元多样性的作用

Growing Artificial Neural Networks for Control: the Role of Neuronal  Diversity

The informational synthesis of neural structures, processes, parameters and
characteristics that allow a unified description and modeling as neural
machines of natural and artificial neural systems is presented. The general
informational parameters as the global quantitative measure of the neural
systems computing potential as absolute and relative neural power were
proposed. Neural information organizing and processing follows the way in which
nature manages neural information by developing functions, functionalities and
circuits related to different internal or peripheral components and also to the
whole system through a non-deterministic memorization, fragmentation and
aggregation of afferent and efferent information, deep neural information
processing representing multiple alternations of fragmentation and aggregation
stages. The relevant neural characteristics were integrated into a neural
machine type model that incorporates unitary also peripheral or interface
components as the central ones. The proposed approach allows overcoming the
technical constraints in artificial computational implementations of neural
information processes and also provides a more relevant description of natural
ones.

自然和人工神经系统的神经结构、过程、参数和特征的信息综合表述和建模为神经机器，提出了神经系统计算潜力的全球定量度量作为绝对和相对神经能力的一般信息参数，通过非确定性的感入和感出信息的记忆、分段和聚合，深度神经信息处理代表了分段和聚合阶段的多重替代，将相关神经特征集成到一个包括单元、外围或接口组件的神经机器型模型中，该方法能够克服人工计算实现神经信息过程的技术约束，并提供更加准确的自然描述。