Maslennikov Oleg V, Gao Chao, Nekorkin Vladimir I
Federal Research Center A.V. Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences, Nizhny Novgorod, Russia.
School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University, Xian, China.
Chaos. 2023 Sep 1;33(9). doi: 10.1063/5.0166359.
How complex patterns generated by neural systems are represented in individual neuronal activity is an essential problem in computational neuroscience as well as machine learning communities. Here, based on recurrent neural networks in the form of feedback reservoir computers, we show microscopic features resulting in generating spatiotemporal patterns including multicluster and chimera states. We show the effect of individual neural trajectories as well as whole-network activity distributions on exhibiting particular regimes. In addition, we address the question how trained output weights contribute to the autonomous multidimensional dynamics.
神经系统产生的复杂模式是如何在单个神经元活动中得以体现的，这是计算神经科学以及机器学习领域的一个基本问题。在此，基于反馈储备计算机形式的递归神经网络，我们展示了导致产生包括多簇态和嵌合态在内的时空模式的微观特征。我们展示了个体神经轨迹以及全网络活动分布对呈现特定状态的影响。此外，我们还探讨了经过训练的输出权重如何促成自主多维动力学这一问题。
