Zheng Pengsheng, Kozloski James
Computational Neuroscience and Multiscale Brain Modeling, Computational Biology Center, IBM Research Division, IBM T. J. Watson Research CenterNew York, NY, United States.
Front Comput Neurosci. 2017 Jul 27;11:70. doi: 10.3389/fncom.2017.00070. eCollection 2017.
We present a network model of striatum, which generates "winnerless" dynamics typical for a network of sparse, unidirectionally connected inhibitory units. We observe that these dynamics, while interesting and a good match to normal striatal electrophysiological recordings, are fragile. Specifically, we find that randomly initialized networks often show dynamics more resembling "winner-take-all," and relate this "unhealthy" model activity to dysfunctional physiological and anatomical phenotypes in the striatum of Huntington's disease animal models. We report plasticity as a potent mechanism to refine randomly initialized networks and create a healthy winnerless dynamic in our model, and we explore perturbations to a healthy network, modeled on changes observed in Huntington's disease, such as neuron cell death and increased bidirectional connectivity. We report the effect of these perturbations on the conversion risk of the network to an unhealthy state. Finally we discuss the relationship between structural and functional phenotypes observed at the level of simulated network dynamics as a promising means to model disease progression in different patient populations.
我们提出了一种纹状体网络模型,该模型产生了稀疏、单向连接的抑制性单元网络典型的“无胜者”动态。我们观察到,这些动态虽然有趣且与正常纹状体电生理记录非常匹配,但却很脆弱。具体而言,我们发现随机初始化的网络通常表现出更类似于“胜者全得”的动态,并将这种“不健康”的模型活动与亨廷顿舞蹈病动物模型纹状体中功能失调的生理和解剖表型联系起来。我们报告了可塑性作为一种有效机制,可优化随机初始化的网络并在我们的模型中创建健康的无胜者动态,并且我们探索了对健康网络的扰动,该扰动以在亨廷顿舞蹈病中观察到的变化为模型,例如神经元细胞死亡和双向连接性增加。我们报告了这些扰动对网络转变为不健康状态的风险的影响。最后,我们讨论了在模拟网络动态水平上观察到的结构和功能表型之间的关系,这是一种在不同患者群体中模拟疾病进展的有前景的方法。