Brain and Cognition unit, Faculty of psychology and educational sciences, KU Leuven, Leuven, Belgium.
Cognitive and developmental psychology unit, Faculty of social science, University of Kaiserslautern, Kaiserslautern, Germany.
PLoS Comput Biol. 2023 Aug 11;19(8):e1011325. doi: 10.1371/journal.pcbi.1011325. eCollection 2023 Aug.
Adaptive rewiring provides a basic principle of self-organizing connectivity in evolving neural network topology. By selectively adding connections to regions with intense signal flow and deleting underutilized connections, adaptive rewiring generates optimized brain-like, i.e. modular, small-world, and rich club connectivity structures. Besides topology, neural self-organization also follows spatial optimization principles, such as minimizing the neural wiring distance and topographic alignment of neural pathways. We simulated the interplay of these spatial principles and adaptive rewiring in evolving neural networks with weighted and directed connections. The neural traffic flow within the network is represented by the equivalent of diffusion dynamics for directed edges: consensus and advection. We observe a constructive synergy between adaptive and spatial rewiring, which contributes to network connectedness. In particular, wiring distance minimization facilitates adaptive rewiring in creating convergent-divergent units. These units support the flow of neural information and enable context-sensitive information processing in the sensory cortex and elsewhere. Convergent-divergent units consist of convergent hub nodes, which collect inputs from pools of nodes and project these signals via a densely interconnected set of intermediate nodes onto divergent hub nodes, which broadcast their output back to the network. Convergent-divergent units vary in the degree to which their intermediate nodes are isolated from the rest of the network. This degree, and hence the context-sensitivity of the network's processing style, is parametrically determined in the evolving network model by the relative prominence of spatial versus adaptive rewiring.
自适应重连为进化神经网络拓扑中自组织连接提供了一个基本原理。通过选择性地向信号流较强的区域添加连接，并删除未充分利用的连接，自适应重连生成了优化的类似大脑的、即模块化的、小世界的和丰富俱乐部的连接结构。除了拓扑结构，神经自组织还遵循空间优化原则，例如最小化神经布线距离和神经通路的地形对准。我们使用加权和有向连接模拟了进化神经网络中这些空间原则和自适应重连的相互作用。网络内的神经流量由有向边的扩散动力学等效物表示：一致性和平流。我们观察到自适应和空间重连之间存在建设性的协同作用，这有助于网络的连通性。特别是，布线距离最小化有助于自适应重连创建收敛发散单元。这些单元支持神经信息的流动，并使感觉皮层和其他地方的上下文敏感信息处理成为可能。收敛发散单元由收敛集线器节点组成，这些节点从节点池收集输入，并通过一组密集互连的中间节点将这些信号投射到发散集线器节点上，这些节点将其输出广播回网络。收敛发散单元在其中间节点与网络其余部分隔离的程度上有所不同。这种程度，以及网络处理风格的上下文敏感性，在进化网络模型中通过空间与自适应重连的相对突出程度来参数确定。
