Sailamul Pachaya, Jang Jaeson, Paik Se-Bum
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
J Comput Neurosci. 2017 Dec;43(3):189-202. doi: 10.1007/s10827-017-0657-5. Epub 2017 Sep 12.
Correlated neural activities such as synchronizations can significantly alter the characteristics of spike transfer between neural layers. However, it is not clear how this synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. To address this question, we implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that, the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.
诸如同步之类的相关神经活动能够显著改变神经层之间尖峰传递的特征。然而，尚不清楚这种依赖同步的尖峰传递如何受到汇聚前馈布线结构的影响。为了解决这个问题，我们对模型神经网络进行了计算机模拟：一个源层和一个目标层通过不同类型的汇聚布线规则相连。在高斯 - 高斯（GG）模型中，连接概率和强度都作为空间距离的函数以高斯分布给出。在均匀 - 常数（UC）和均匀 - 指数（UE）模型中，连接概率密度在一定范围内是均匀常数，但连接强度分别设置为常数或指数衰减函数。然后，我们研究了在这些条件下尖峰传递函数是如何被调制的，同时引入静态或同步输入模式以模拟不同水平的前馈尖峰同步。我们观察到，对于每种汇聚条件，传递函数的依赖同步调制都明显不同。尖峰传递函数的调制在UC模型中最大，在UE模型中最小。我们的分析表明，这种差异是由每个模型中汇聚突触产生的不同尖峰权重分布引起的。我们的结果表明，前馈汇聚结构是依赖相关性的尖峰控制的关键因素，因此对于理解大脑中的信息传递机制必须被视为重要因素。
