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

Black hole: A new heuristic optimization approach for data clustering

Review: Multi-objective optimization methods and application in energy saving

Membrane systems (also called P systems) refer to the computing models abstracted from the structure and the functioning of the living cell as well as from the cooperation of cells in tissues, organs, and other populations of cells. Spiking neural P systems (SNPS) are a class of distributed and parallel computing models that incorporate the idea of spiking neurons into P systems. To attain the solution of optimization problems, P systems are used to properly organize evolutionary operators of heuristic approaches, which are named as membrane-inspired evolutionary algorithms (MIEAs). This paper proposes a novel way to design a P system for directly obtaining the approximate solutions of combinatorial optimization problems without the aid of evolutionary operators like in the case of MIEAs. To this aim, an extended spiking neural P system (ESNPS) has been proposed by introducing the probabilistic selection of evolution rules and multi-neurons output and a family of ESNPS, called optimization spiking neural P system (OSNPS), are further designed through introducing a guider to adaptively adjust rule probabilities to approximately solve combinatorial optimization problems. Extensive experiments on knapsack problems have been reported to experimentally prove the viability and effectiveness of the proposed neural system.

/pdf/an-optimization-spiking-neural-p-system-for-approximately-3a2dw9uok6.pdf

An optimization spiking neural p system for approximately solving combinatorial optimization problems.

Cluster Analysis: Low Temperatures and Voting in Congress

In this paper, we propose an adaptive audio watermarking scheme based on kernel fuzzy c-means (KFCM) clustering algorithm, which possesses robust ability against common signal processing and desynchronization attacks. The original audio signal is partitioned into audio frames and then each audio frame is further divided as two sub-frames. In order to resist desynchronization attacks, we embed a synchronization code into first sub-frame of each audio frame by using a mean quantization technique in temporal domain. Moreover, watermark signal is hid into DWT coefficients of second sub-frame of each audio frame by using an energy quantization technique. A local audio feature data set extracted from all audio frames is used to train a KFCM. The well-trained KFCM is used to adaptively control quantization steps in above two quantization techniques. The experimental results show the proposed scheme is robust to common signal processing (such as MP3 lossy compression, noise addition, filtering, re-sampling, re-quantizing) and desynchronization attacks (random cropping, pitch shifting, amplitude variation, time-scale modification, jittering).

Audio watermarking scheme robust against desynchronization attacks based on kernel clustering

A variant of spiking neural P systems was recently investigated by the authors, using astrocytes that have excitatory and inhibitory influence on synapses. In this work, we consider this system in the non-synchronized (i.e., asynchronous) mode: in any step, when a neuron is enabled, it is not obligatorily fired, making a global clock dispensable. It is proved that asynchronous spiking neural P systems with astrocytes are universal (when using extended rules). The construction is uniform in the sense that the form of the modules used is independent of the simulated register machine.

Asynchronous extended spiking neural p systems with astrocytes

This paper is concerned with the problems of stability and stabilization of impulsive switched system with time delays. First, a novel Razumikhin function is constructed; then based on LMI approach and optimization techniques, we derive our theoretical result with good properties; subsequently, we extend our results to the case with perturbations. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed techniques.

Novel LMI-based stability and stabilization analysis on impulsive switched system with time delays

This paper focuses on the fuzzy variable structure control for uncertain systems with disturbance. Specifically, the fuzzy control is introduced to estimate the control disturbance, the switching control is included to compensate for the approximation error, and they possess the characteristic of simpleness in design and effectiveness in attenuating the control chattering. Some typical numerical examples are presented to demonstrate the effectiveness and advantage of the fuzzy variable structure controller proposed.

/pdf/fuzzy-variable-structure-control-for-uncertain-systems-with-rd3b31u9r4.pdf

Jun Wang

Papers

Audio watermarking scheme robust against desynchronization attacks based on kernel clustering

Asynchronous extended spiking neural p systems with astrocytes

Novel LMI-based stability and stabilization analysis on impulsive switched system with time delays

Fuzzy variable structure control for uncertain systems with disturbance

ConvSNP: a deep learning model embedded with SNP-like neurons