Correlations between synapses in pairs of neurons slow down dynamics in randomly connected neural networks.
Correlations between synapses in pairs of neurons slow down dynamics in randomly connected neural networks.
复制标题
神经元对中突触之间的相关性会减慢随机连接的神经网络中的动力学。
DOI：
10.1103/physreve.97.062314
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
S. Ostojic
中科院分区：
文献类型：
--
作者：
Daniel Martí;N. Brunel;S. Ostojic
随机连接的神经元网络是理论神经科学中最流行的模型之一。然而，皮层神经元之间的连接并不是完全随机的，在实验数据中发现的最简单和最显著的随机性偏差是锥体细胞之间的双向连接的过度表现。利用数值和解析方法，我们研究了部分对称连通性对速率单元网络动力学的影响。我们考虑随机神经网络所表现出的两个动力学制度：弱耦合制度，其中的发射活动衰减到一个单一的固定点，除非网络受到刺激，和强耦合或混沌制度，其特征在于内部产生的波动的发射率。在弱耦合制度，我们计算分析，对于任意程度的对称性，在外部噪声的存在下，网络活动的自相关性。在混沌政权，我们进行模拟，以确定内在波动的时间尺度。在这两种情况下，对称性增加了自相关函数的特征渐近衰减时间，因此减慢了网络中的动态。
Networks of randomly connected neurons are among the most popular models in theoretical neuroscience. The connectivity between neurons in the cortex is however not fully random, the simplest and most prominent deviation from randomness found in experimental data being the overrepresentation of bidirectional connections among pyramidal cells. Using numerical and analytical methods, we investigate the effects of partially symmetric connectivity on the dynamics in networks of rate units. We consider the two dynamical regimes exhibited by random neural networks: the weak-coupling regime, where the firing activity decays to a single fixed point unless the network is stimulated, and the strong-coupling or chaotic regime, characterized by internally generated fluctuating firing rates. In the weak-coupling regime, we compute analytically, for an arbitrary degree of symmetry, the autocorrelation of network activity in the presence of external noise. In the chaotic regime, we perform simulations to determine the timescale of the intrinsic fluctuations. In both cases, symmetry increases the characteristic asymptotic decay time of the autocorrelation function and therefore slows down the dynamics in the network.
大型相关随机矩阵的特征值谱。
DOI：
10.1103/physreve.94.050101
发表时间：
2016
期刊：
Physical review. E
影响因子：
--
作者：
Kuczala,Alexander;Sharpee,TatyanaO
通讯作者：
Sharpee,TatyanaO
影响因子：
2.4
作者：
Rajan, Kanaka;Abbott, L. F.;Sompolinsky, Haim
通讯作者：
Sompolinsky, Haim