The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape-flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments.neural circuits | free energy | entropy production | nonequilibrium thermodynamics A grand goal of biology is to understand the function of the human brain. The brain is a complex dynamical system (1-6). The individual neurons can develop action potentials and connect with each other through synapses to form the neural circuits. The neural circuits of the brain perpetually generate complex patterns of activity that have been shown to be related with special biological functions, such as learning, long-term associative memory, working memory, olfaction, decision making and thinking (7-9), etc. Many models have been proposed for understanding how neural circuits generate different patterns of activity. HodgkinHuxley model gives a quantitative description of a single neuronal behavior based on the voltage-clamp measurements of the voltage (4). However, various vital functions are carried out by the circuit rather than individual neurons. It is at present still challenging to explore the underlying global natures of the large neural networks built from individual neurons.Hopfield developed a model (5, 6) that makes it possible to explore the global natures of the large neural networks without losing the information of essential biological functions. For symmetric neural circuits, an energy landscape can be constructed that decreas...

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This paper addresses missing value imputation for the Internet of Things (IoT). Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain. However, missing values are very common in the IoT for a variety of reasons, which results in the fact that the experimental data are incomplete. As a result of this, some work, which is related to the data of the IoT, can’t be carried out normally. And it leads to the reduction in the accuracy and reliability of the data analysis results. This paper, for the characteristics of the data itself and the features of missing data in IoT, divides the missing data into three types and defines three corresponding missing value imputation problems. Then, we propose three new models to solve the corresponding problems, and they are model of missing value imputation based on context and linear mean (MCL), model of missing value imputation based on binary search (MBS), and model of missing value imputation based on Gaussian mixture model (MGI). Experimental results showed that the three models can improve the accuracy, reliability, and stability of missing value imputation greatly and effectively.

The title of this paper may suggest such topics as routing, networking, and data mining, but we focus on new research angles regarding the Internet of Things (IoT) as the theme of this paper. These research angles come from other disciplines and are in the process of being adopted by the IoT. Our paper serves a key purpose: from the perspective of correlative technologies based on time, to review the evolutionary process of the IoT and depict the relations between the correlation techniques which are largely missing in current literature in which the focus has been more on the introduction and comparison of existing technologies and less on issues describing evolutionary process of the IoT. We consider that the latter is crucial to understanding the evolution of the IoT. Through generalizations of particular focus in different stages of each technology, we can better understand the current phase of the IoT and therefore predict future challenges. This paper aims to bridge this gap by providing guidance in terms of the evolutionary process of the IoT and gives readers a panoramic view of the IoT field without repeating what is already available in existing literature so as to complement the existing IoT survey papers which have not covered the evolutionary process of the IoT.