Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons
Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons
复制标题
DOI：
10.1038/nn.3658
复制
发表时间：
2014-04-01
影响因子：
25
通讯作者：
Ostojic, Srdjan
中科院分区：
文献类型：
--
作者：
Ostojic, Srdjan
兴奋性和抑制性神经元平衡网络中的异步活动被认为是构成新皮层中信息传播和转化的主要媒介。在这里，我们展示了一个非结构化的、稀疏连接的模型尖峰神经元网络可以显示两种根本不同类型的异步活动，这意味着巨大不同的计算特性。对于弱突触耦合，静止的网络处于被充分研究的异步状态，在异步状态下，单个神经元以恒定的速率不规则地放电。在这种状态下，外部输入导致不同神经元的高度冗余响应，有利于信息传递，但阻碍了更复杂的计算。对于强耦合，我们发现静止的网络表现出丰富的内部动态，其中单个神经元的放电率在时间和神经元之间波动强烈。在这种情况下，内部动态与传入的刺激相互作用，为复杂的信息处理和学习提供了基础。
Asynchronous activity in balanced networks of excitatory and inhibitory neurons is believed to constitute the primary medium for the propagation and transformation of information in the neocortex. Here we show that an unstructured, sparsely connected network of model spiking neurons can display two fundamentally different types of asynchronous activity that imply vastly different computational properties. For weak synaptic couplings, the network at rest is in the well-studied asynchronous state, in which individual neurons fire irregularly at constant rates. In this state, an external input leads to a highly redundant response of different neurons that favors information transmission but hinders more complex computations. For strong couplings, we find that the network at rest displays rich internal dynamics, in which the firing rates of individual neurons fluctuate strongly in time and across neurons. In this regime, the internal dynamics interact with incoming stimuli to provide a substrate for complex information processing and learning.