Dipartimento di Fisica e Scienza della Terra, Università di Parma, via G.P. Usberti, 7/A-43124 Parma, Italy.
INFN, Gruppo Collegato di Parma, via G.P. Usberti, 7/A-43124 Parma, Italy.
Phys Rev E. 2017 Jan;95(1-1):012308. doi: 10.1103/PhysRevE.95.012308. Epub 2017 Jan 10.
We investigate the dynamical role of inhibitory and highly connected nodes (hub) in synchronization and input processing of leaky-integrate-and-fire neural networks with short term synaptic plasticity. We take advantage of a heterogeneous mean-field approximation to encode the role of network structure and we tune the fraction of inhibitory neurons f_{I} and their connectivity level to investigate the cooperation between hub features and inhibition. We show that, depending on f_{I}, highly connected inhibitory nodes strongly drive the synchronization properties of the overall network through dynamical transitions from synchronous to asynchronous regimes. Furthermore, a metastable regime with long memory of external inputs emerges for a specific fraction of hub inhibitory neurons, underlining the role of inhibition and connectivity also for input processing in neural networks.
我们研究了具有短期突触可塑性的漏电积分和放电神经网络的同步和输入处理中抑制性和高度连接节点（中枢）的动态作用。我们利用异质平均场近似来编码网络结构的作用，并调整抑制性神经元的分数 f_{I}及其连接水平，以研究中枢特征与抑制之间的合作。我们表明，取决于 f_{I}，高度连接的抑制性节点通过从同步到异步状态的动态转变，强烈驱动整个网络的同步特性。此外，在中枢抑制性神经元的特定分数下，出现了具有外部输入长记忆的亚稳状态，突出了抑制和连接在神经网络输入处理中的作用。
