论文标题
与远程相互作用的神经形态网络中同步和放大振荡的出现
Emergence of synchronised and amplified oscillations in neuromorphic networks with long-range interactions
论文作者
论文摘要
可以用粗粒变量来描述神经形态网络，如果正确考虑随机性，则出现持续行为会自发出现。例如，最近发现，连接的神经元贴片的定向线性链会放大输入信号，并调整其特征频率。在这里，我们研究了这样一个简单模型的概括，在参数空间和远程相互作用中引入了异质性和可变性，从而破坏了有向链的信息传输的优先方向。一方面，扩大参数区域会导致我们在分析上表征的更复杂的状态空间。此外，我们将非本地相互作用的强度分布与网络振荡的频率分布联系起来。另一方面，我们发现添加长距离相互作用可能会导致新现象的发作，因为在所有相互作用的单元中，这也可以与信号的放大共存，因为它们在所有相互作用的单位中的连贯和同步振荡。
Neuromorphic networks can be described in terms of coarse-grained variables, where emergent sustained behaviours spontaneously arise if stochasticity is properly taken in account. For example it has been recently found that a directed linear chain of connected patch of neurons amplifies an input signal, also tuning its characteristic frequency. Here we study a generalization of such a simple model, introducing heterogeneity and variability in the parameter space and long-range interactions, breaking, in turn, the preferential direction of information transmission of a directed chain. On one hand, enlarging the region of parameters leads to a more complex state space that we analytically characterise; moreover, we explicitly link the strength distribution of the non-local interactions with the frequency distribution of the network oscillations. On the other hand, we found that adding long-range interactions can cause the onset of novel phenomena, as coherent and synchronous oscillations among all the interacting units, which can also coexist with the amplification of the signal.
