Using ecological networks to answer questions in global biogeography and ecology

Ecological networks have classically been studied at site and landscape scales, yet recent efforts have been made to collate these data into global repositories. This offers an opportunity to integrate and upscale knowledge about ecological interactions from local to global scales to gain enhanced insights from the mechanistic information provided by these data. By drawing on existing research investigating patterns in ecological interactions at continental to global scales, we show how data on ecological networks, collected at appropriate scales, can be used to generate an improved understanding of many aspects of ecology and biogeography—for example, species distribution modelling, restoration ecology and conservation. We argue that by understanding the patterns in the structure and function of ecological networks across scales, it is possible to enhance our understanding of the natural world.     

具有节点偏置的高阶神经网络模型

在汪涛文献基础上提出了一个具有节点偏置的高阶神经网络模型、给出了模型的哈密顿量和学习算法,证明了学习算法的收敛性,该模型能对每一神经元自动引入一个节点偏置使得网络能够存储所有学习图样包括相关图样,其存储容量远高于Hebb—rule—like学习算法下的高阶神经网络模型.对由30个神经元组成的二阶神经网络进行了计算机仿真,结果证实了上述结论.此外,对初始突触强度对学习效果的影响和不同存储图样数目下的平均吸引半径进行了仿真计算并分析了所得结果.新模型的特点使其具有良好的应用前景     

采用Reichardt运动检测器和Boltzmann Machine神经网络的运动计算

建立了一个探讨灵长类视皮层从Ｖ１区到ＭＴ区的运动信息加工原理的计算模型,这个过程的突出特征是视觉运动信息经过了从局部检测进步到整体感知。模型的第一层由用于抽提运动模式的局部速度以及结构性质的Ｒｅｉｃｈａｒｄｔ运动检测器组成,进一步的加工是通过Ｂｏｌｔｚｍａｎｎ Ｍａｃｈｉｎｅ神经网络来实现的。这种网络的学习算法具有局部更新的显著性质,在学习阶段,网络不断地修改联结权重以形成对于记录在网络的显单元上     

N-Heterocyclic Carbene-Palladium Polymers Network: Recyclable Pre-catalyst for Effective Suzuki–Miyaura Coupling Reaction in Water

Rational design of high-efficiency N-heterocyclic carbene (NHC) palladium catalyst is of great importance to modern organic synthesis, especially in chemical and pharmaceutical industries. Herein, we fabricate a polymer network containing N-heterocyclic carbene palladium (PNNHC-Pd) catalytic active sites via an immobilization process. The N-heterocyclic carbene palladium can serve as a promising linkage of polymer network as well as an effective catalytic active site owing to its structural preference and strong σ-donating ability with palladium species. The results display that N-heterocyclic carbene palladium disperses homogeneously in polymer network, thus rendering PNNHC-Pd excellent catalytic activity, high stability and superior reusability in palladium-catalyzed Suzuki–Miyaura coupling reaction in aqueous medium. This work provides a new insight into the development of heterogenization of homogeneous catalysts based on polymer network.     

具有竞争指针的短时记忆神经网络模型

在我们以前提出的短时记忆神经网络模型基础上［３］,我们在新模型中引入突触竞争机制,提出了一个新的短时记忆神经网络模型。模型仍由两个神经网络所组成;其一为与长时记忆共有的信息内容表达网络,另一个为指针神经元环路。由于表达区神经元与指针神经元间的突触权重的竞争,使得模型可以表现出由干扰引起的短时记忆的遗忘。相应于自由回忆序列位置效应和汉字组块两个心理学实验,对模型做了计算机仿真。仿真结果显示模型的行为与两个心理实验定量地符合得很好。由此表明现在的模型更合适于作为短时记忆的模型。     

动态神经网络中的同步振荡

目前有一种假设认为同一视觉对象是由一群神经元的同步振荡活动来表征的。这一神经元发放活动的时间特性,是解决视觉信息处理中“结合问题（Ｂｉｎｄｉｎｇｐｒｏｂｌｅｍ）”的可能机制。本文用我们所提出的一种简化现实性神经网络模型［１］所构造的时滞非线性振子网络［２］,模拟生物神经网络的同步振荡活动。并考虑了振子各参数的设置与振荡活动的关系,以及网络振子间耦联对同步活动的影响．     

耳蜗核神经元的简化现实性模型

采用我们以前提出的简化现实性神经网络模型［１］的一种修正形式,通过选定适当的参数,仿真了耳蜗核的类初级细胞（Ｐｒｉｍａｒｙ－ｌｉｋｅｃｅｌｌ）,梳状反应细胞（Ｃｈｏｐｐｅｒｒｅｓｐｏｎｓｅｃｅｌｌ）以及给刺激反应细胞（Ｏｎｓｅｔｒｅｓｐｏｎｓｅｃｅｌｌ）对短纯青刺激的刺激后时间直方图。并通过分析参数配置和仿真行为的对应关系,指出模型在生理学上的合理性。