Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

/pdf/collective-dynamics-of-small-world-networks-205zhem3gm.pdf

Collective dynamics of small-world networks

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.

/pdf/representation-learning-a-review-and-new-perspectives-4sidphwbhg.pdf

Representation Learning: A Review and New Perspectives

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

A FUNDAMENTAL step in visual pattern recognition is the establishment of relations between spatially separate features. Recently, we have shown that neurons in the cat visual cortex have oscillatory responses in the range 40–60 Hz (refs 1,2) which occur in synchrony for cells in a functional column and are tightly correlated with a local oscillatory field potential. This led us to hypothesize that the synchronization of oscillatory responses of spatially distributed, feature selective cells might be a way to establish relations between features in different parts of the visual field2,3. In support of this hypothesis, we demonstrate here that neurons in spatially separate columns can synchronize their oscillatory responses. The synchronization has, on average, no phase difference, depends on the spatial separation and the orientation preference of the cells and is influenced by global stimulus properties.

Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties.

INFORMATION processing in the cerebral cortex invariably involves the activation of millions of neurons that are widely distributed over its various areas. These distributed activity patterns need to be integrated into coherent representational states. A candidate mechanism for the integration and coordination of neuronal activity between different brain regions is synchronization on a fine temporal scale1–3. In the visual cortex, synchronization occurs selectively between the responses of neurons that represent related features2–5 and that need to be integrated for the generation of coherent percepts; neurons in other areas of the cerebral cortex also synchronize their discharges6–10. However, little is known about the patterns and the behavioural correlates of synchrony among widely separated cortical regions. Here we report that synchronization occurs between areas of the visual and parietal cortex, and between areas of the parietal and motor cortex, in the awake cat. When cats responded to a sudden change of a visual pattern, neuronal activity in cortical areas exhibited synchrony without time lags; this synchrony was particularly strong between areas subserving related functions. During reward and inter-trial episodes, zero-time-lag synchrony was lost and replaced by interactions exhibiting large and unsystematic time lags.

/pdf/visuomotor-integration-is-associated-with-zero-time-lag-hmdca4roa5.pdf

Visuomotor integration is associated with zero time-lag synchronization among cortical areas

Neurons in area 17 of cat visual cortex display oscillatory responses that can synchronize across spatially separate columns in a stimulus-specific way. Response synchronization has now been shown to occur also between neurons in area 17 of the right and left cerebral hemispheres. This synchronization was abolished by section of the corpus callosum. Thus, the response synchronization is mediated by corticocortical connections. These data are compatible with the hypothesis that temporal synchrony of neuronal discharges serves to bind features within and between the visual hemifields.

https://science.sciencemag.org/content/sci/252/5009/1177.full.pdf

Interhemispheric synchronization of oscillatory neuronal responses in cat visual cortex.

Perception and cortical responses are not only driven “bottom-up” by the external stimulus but are altered by internal constraints such as expectancy or the current behavioral goal. To investigate neurophysiological mechanisms of such top-down effects, we analyzed the temporal interactions of neurons on different levels of the cortical hierarchy during perception of stimuli with varying behavioral significance. We found that interareal interactions in a middle-frequency range (θ and α; 4–12 Hz) strongly depend on the associated behavior, with a phase relationship and a layer specificity indicating a top-down-directed interaction. For novel unexpected stimuli, presumably processed in a feed-forward fashion, no such interactions occurred but high-frequency interactions (γ; 20–100 Hz) were observed. Thus corticocortical synchronization reflects the internal state of the animal and may mediate top-down processes.

https://www.pnas.org/content/pnas/97/26/14748.full.pdf

Peter König

Papers

Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties.

Visuomotor integration is associated with zero time-lag synchronization among cortical areas

Interhemispheric synchronization of oscillatory neuronal responses in cat visual cortex.

Top-down processing mediated by interareal synchronization

Temporal coding in the visual cortex: new vistas on integration in the nervous system