de Graaf Myriam Lauren, Wagner Heiko, Mochizuki Luis, Le Mouel Charlotte
Dept. of Movement Science, University of Münster, Horstmarer Landweg 62b, 48149, Münster, Germany.
Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Fliednerstraße 21, 48149, Münster, Germany.
Biol Cybern. 2025 Jun 4;119(2-3):12. doi: 10.1007/s00422-025-01011-7.
During walking and running, animals display rich and coordinated motor patterns that are generated and controlled within the central nervous system. Previous computational and experimental results suggest that the balance between excitation and inhibition in neural circuits may be critical for generating such structured motor patterns. In this paper, we explore the influence of this balance on the ability of a reservoir computing artificial neural network to learn human locomotor patterns, using mean-field theory and simulations. We created networks with varying neuron numbers, connection percentages and connection strengths for the excitatory and inhibitory neuron populations, and introduced the anatomical imbalance that quantifies the overall effect of the two populations. We trained the networks to reproduce muscle activation patterns derived from human recordings and evaluated their performance. Our results indicate that network dynamics and performance depend critically on the anatomical imbalance in the network. Excitation-dominated networks lead to saturated firing rates, thereby reducing the firing rate heterogeneity and leading to muscle coactivation and inflexible motor patterns. Inhibition-dominated networks, on the other hand, perform well, displaying balanced input to the neurons and sufficient heterogeneity in the neuron firing rate patterns. This suggests that motor pattern generation may be robust to increased inhibition but not increased excitation in neural networks.
在行走和奔跑过程中，动物会展现出丰富且协调的运动模式，这些模式在中枢神经系统内产生并受到控制。先前的计算和实验结果表明，神经回路中兴奋与抑制之间的平衡对于产生这种结构化的运动模式可能至关重要。在本文中，我们运用平均场理论和模拟方法，探究这种平衡对储层计算人工神经网络学习人类运动模式能力的影响。我们创建了具有不同神经元数量、连接百分比以及兴奋性和抑制性神经元群体连接强度的网络，并引入了解剖学不平衡来量化这两个群体的总体效应。我们训练这些网络以重现源自人类记录的肌肉激活模式，并评估它们的性能。我们的结果表明，网络动态和性能严重依赖于网络中的解剖学不平衡。以兴奋为主导的网络会导致发放率饱和，从而降低发放率的异质性，并导致肌肉共同激活和运动模式僵化。另一方面，以抑制为主导的网络表现良好，显示出对神经元的平衡输入以及神经元发放率模式中足够的异质性。这表明运动模式生成对于神经网络中抑制增加可能具有鲁棒性，但对兴奋增加则不然。
