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

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.

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

在Microsoft Excel中标记某个单元格或区域时，我们通常会看到在该单元格或区域的右下角有一个小黑方块，此小黑方块就是“填充柄”(如图1)。“填充柄”有什么作用?如何使用“填充柄”实现自动填充?下面就作以简单介绍。

活用Microsoft Excel中的“填充柄”功能

Cluster Analysis: Low Temperatures and Voting in Congress

We brie y present (basic ideas, some examples, classes of spiking neural P systems, some results concerning their power, research topics) a recently initiated branch of membrane computing with motivation from neural computing. Further details can be found at the web page of membrane computing, from http://psystems.disco.unimib.it. 1 The General Framework The most intuitive way to introduce spiking neural P systems (in short, SN P systems) is by watching the movie available at http://www.igi.tugraz. at/tnatschl/spike_trains_eng.html, in the web page of Wofgang Maass, Graz, Austria: neurons are sending to each others spikes, electrical impulses of identical shape (duration, voltage, etc.), with the information encoded in the frequency of these impulses, hence in the time passes between consecutive spikes. For neurologists, this is nothing new, related drawings already appears in papers by Ramon y Cajal, a pioneer of neuroscience at the beginning of the last century, but in the recent years computing by spiking is a vivid research area, with the hope to lead to a neural computing of the third generation see [12], [21], etc. For membrane computing it is somehow natural to incorporate the idea of spiking neurons (already neural-like P systems exist, based on di erent ingredients see [23], e orts to compute with a small number of objects were recently made in several papers see, e.g., [2], using the time as a support of information, for instance, taking the time between two events as the result of a computation, was also considered see [3]), but still important di erences exist between the general way of working with multisets of objects in the compartments of a cell-like membrane structure as in membrane computing and the way the neurons communicate by spikes. A way to answer this challenge was proposed in [18]: neurons as single membranes, placed in the nodes of a graph corresponding to synapses, only one type of objects present

Spiking Neural P Systems: A Tutorial.

In tissue P systems several cells (elementary membranes) communicate through symport/antiport rules, thus carrying out a computation. We add to such systems the basic feature of (cell–like) P systems with active membranes – the possibility to divide cells. As expected (as it is the case for P systems with active membranes), in this way we get the possibility to solve computationally hard problems in polynomial time; we illustrate this possibility with SAT problem.

/pdf/tissue-p-systems-with-cell-division-67uc4rpyc7.pdf

Tissue P Systems with Cell Division

Pulse coupled neural networks (PCNN, for short) are models abstracting the synchronization behavior observed experimentally for the cortical neurons in the visual cortex of a cat’s brain, and the intersecting cortical model is a simplified version of the PCNN model. Membrane computing (MC) is a kind computation paradigm abstracted from the structure and functioning of biological cells that provide models working in cell-like mode, neural-like mode and tissue-like mode. Inspired from intersecting cortical model, this paper proposes a new kind of neural-like P systems, called dynamic threshold neural P systems (for short, DTNP systems). DTNP systems can be represented as a directed graph, where nodes are dynamic threshold neurons while arcs denote synaptic connections of these neurons. DTNP systems provide a kind of parallel computing models, they have two data units (feeding input unit and dynamic threshold unit) and the neuron firing mechanism is implemented by using a dynamic threshold mechanism. The Turing universality of DTNP systems as number accepting/generating devices is established. In addition, an universal DTNP system having 109 neurons for computing functions is constructed.

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Dynamic threshold neural P systems

This book constitutes the thoroughly refereed post-workshop proceedings of the 10th International Workshop on Membrane Computing, WMC 2009, held in Curtea de Arges, Romania, during August 24 to 27, 2009 under the auspices of the European Molecular Computing Consortium (EMCC) and the Molecular Computing Task Force of IEEE Computational Intelligence Society. The 22 revised full papers presented together with 10 invited papers went through two rounds of reviewing and improvement. The papers in this volume cover all the main directions of research in membrane computing, ranging from theoretical topics in mathematics and computer science to application issues; the invited lectures present fundamental contributions to membrane computing; thus highlighting important directions of current research in this area.

Membrane Computing: 10th International Workshop, WMC 2009, Curtea de Arges, Romania, August 24-27, 2009. Revised Selected and Invited Papers

In this paper we study membrane dissolution rules in the framework of P systems with active membranes but without using electrical charges. More precisely, we prove that the polynomial computational complexity class associated with the class of recognizer P systems with active membranes, without polarizations and without dissolution coincides with the standard complexity class P. Furthermore, we demonstrate that if we consider dissolution rules, then the resulting complexity class contains the class NP.

On the power of dissolution in P systems with active membranes

P–Lingua is a programming language for membrane computing which aims to be a standard to define P systems. In order to implement this idea, a Java library called pLinguaCore has been developed as a software framework for cell–like P systems. It is able to handle input files (either in XML or in P–Lingua format) defining P systems from a number of different cell–like P system models. Moreover, the library includes several built–in simulators for each supported model. For the sake of software portability, pLinguaCore can export a P system definition to any convenient output format (currently XML and binary formats are available). This software is not a closed product, but it can be extended to accept new input or output formats and also new models or simulators.

The term P–Lingua 2.0 refers to the software package consisting of the above mentioned library together with a user interface called pLinguaPlugin (more details can be found at http://www.p-lingua.org).

Finally, in order to illustrate the software, this paper includes an application using pLinguaCore for describing and simulating ecosystems by means of P systems.

/pdf/an-overview-of-p-lingua-2-0-2z5enkq846.pdf

Agustín Riscos-Núñez

Papers

Tissue P Systems with Cell Division

Dynamic threshold neural P systems

Membrane Computing: 10th International Workshop, WMC 2009, Curtea de Arges, Romania, August 24-27, 2009. Revised Selected and Invited Papers

On the power of dissolution in P systems with active membranes

An overview of p-lingua 2.0