Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands.
Institute 'Carlos I' for Theoretical and Computational Physics, University of Granada, Spain.
Neural Netw. 2021 Oct;142:44-56. doi: 10.1016/j.neunet.2021.04.027. Epub 2021 Apr 26.
The interplay between structure and function affects the emerging properties of many natural systems. Here we use an adaptive neural network model that couples activity and topological dynamics and reproduces the experimental temporal profiles of synaptic density observed in the brain. We prove that the existence of a transient period of relatively high synaptic connectivity is critical for the development of the system under noise circumstances, such that the resulting network can recover stored memories. Moreover, we show that intermediate synaptic densities provide optimal developmental paths with minimum energy consumption, and that ultimately it is the transient heterogeneity in the network that determines its evolution. These results could explain why the pruning curves observed in actual brain areas present their characteristic temporal profiles and they also suggest new design strategies to build biologically inspired neural networks with particular information processing capabilities.
结构与功能的相互作用影响着许多自然系统的新兴特性。在这里，我们使用一种自适应神经网络模型，该模型将活动和拓扑动力学耦合在一起，并再现了在大脑中观察到的突触密度的实验时间分布。我们证明了在噪声环境下，相对高的突触连接的短暂时期的存在对于系统的发展至关重要，这样，所得到的网络可以恢复存储的记忆。此外，我们还表明，中间的突触密度提供了具有最小能量消耗的最佳发展路径，最终，正是网络中的瞬态异质性决定了它的进化。这些结果可以解释为什么在实际的大脑区域中观察到的修剪曲线呈现出其特征性的时间分布，它们还为构建具有特定信息处理能力的生物启发神经网络提供了新的设计策略。
