Sat, 05 Jul 2008 22:52:17 BST CiteULike: napvasconcelos's library [285 articles] http://www.citeulike.org/user/napvasconcelos CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/napvasconcelos/article/2949455 <i>(25 July 2005)</i><br /><br />Developed by an extremely active open source community, Moodle is a sophisticated course management system that's ideal for creating dynamic online learning communities and for supplementing face-to-face learning. Used in more than 115 countries and supporting over 60 languages, Moodle can scale from a single-teacher site to a 40,000- student university. Teachers who use Moodle have access to an array of powerful tools such as assignments, forums, journals, quizzes, surveys, chat rooms, and workshops. _Using Moodle_ is a comprehensive, hands-on guide that explains how the system works, with plenty of examples and best practices for its many features and plug-in modules. Authored by a member of the Moodle community, this authoritative book also exposes little-known but powerful hacks for more technically savvy users. For anyone who is using-or thinking of using-this CMS, _Using Moodle_ is required reading. Using Moodle (Community Press) Jason Cole (25 July 2005) 2008-07-01T22:59:05-00:00 2005 O'Reilly Media, Inc. ead http://www.citeulike.org/user/napvasconcelos/article/524201 <i>(01 August 1974)</i> Tools for Conviviality Ivan Illich (01 August 1974) 2006-02-28T14:54:11-00:00 1974 Marion Boyars Publishers ead http://www.citeulike.org/user/napvasconcelos/article/2906898 <i>Nature neuroscience (15 June 2008)</i><br /><br />It is unclear how the complex spatiotemporal organization of ongoing cortical neuronal activity recorded in anesthetized animals relates to the awake animal. We therefore used two-photon population calcium imaging in awake and subsequently anesthetized rats to follow action potential firing in populations of neurons across brain states, and examined how single neurons contributed to population activity. Firing rates and spike bursting in awake rats were higher, and pair-wise correlations were lower, compared with anesthetized rats. Anesthesia modulated population-wide synchronization and the relationship between firing rate and correlation. Overall, brain activity during wakefulness cannot be inferred using anesthesia. Population imaging of ongoing neuronal activity in the visual cortex of awake rats. David S Greenberg Arthur R Houweling Jason N D Kerr doi:10.1038/nn.2140 Nature neuroscience (15 June 2008) 2008-06-19T09:03:09-00:00 2008 Nature neuroscience 1097-6256 cortex imaging two-photon visual http://www.citeulike.org/user/napvasconcelos/article/2136105 <i>Proceedings of the National Academy of Sciences, Vol. 100, No. 12. (10 June 2003), pp. 7319-7324.</i><br /><br />Two-photon calcium imaging is a powerful means for monitoring the activity of distinct neurons in brain tissue in vivo. In the mammalian brain, such imaging studies have been restricted largely to calcium recordings from neurons that were individually dye-loaded through microelectrodes. Previous attempts to use membrane-permeant forms of fluorometric calcium indicators to load populations of neurons have yielded satisfactory results only in cell cultures or in slices of immature brain tissue. Here we introduce a versatile approach for loading membrane-permeant fluorescent indicator dyes in large populations of cells. We established a pressure ejection-based local dye delivery protocol that can be used for a large spectrum of membrane-permeant indicator dyes, including calcium green-1 acetoxymethyl (AM) ester, Fura-2 AM, Fluo-4 AM, and Indo-1 AM. We applied this dye-loading protocol successfully in mouse brain tissue at any developmental stage from newborn to adult in vivo and in vitro. In vivo two-photon Ca2+ recordings, obtained by imaging through the intact skull, indicated that whisker deflection-evoked Ca2+ transients occur in a subset of layer 2/3 neurons of the barrel cortex. Thus, our results demonstrate the suitability of this technique for real-time analyses of intact neuronal circuits with the resolution of individual cells. 10.1073/pnas.1232232100 In vivo two-photon calcium imaging of neuronal networks Christoph Stosiek Olga Garaschuk Knut Holthoff Arthur Konnerth doi:10.1073/pnas.1232232100 Proceedings of the National Academy of Sciences, Vol. 100, No. 12. (10 June 2003), pp. 7319-7324. 2007-12-17T12:11:57-00:00 2003 Proceedings of the National Academy of Sciences 100 12 7319 7324 imaging two-photon http://www.citeulike.org/user/napvasconcelos/article/2919569 <i>Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on, Vol. 1 (2001), pp. 347-352 vol.1.</i><br /><br />This paper presents the complexity analysis of a standard supervised MLP-learning algorithm in conjunction with the well-known backpropagation, an efficient method for evaluation of derivatives, in either batch or incremental learning mode. In particular, we detail the cost per epoch (i.e., operations required for processing one sweep of all the training data) using “approximate” FLOPs (floating point operations) in a typical backpropagation for solving neural networks nonlinear least squares problems. Furthermore, we identify erroneous complexity analyses found in the past NN literature. Our operation-count formula would be very useful for a given MLP architecture to compare learning algorithms On complexity analysis of supervised MLP-learning for algorithmic comparisons E Mizutani SE Dreyfus doi:10.1109/IJCNN.2001.939044 Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on, Vol. 1 (2001), pp. 347-352 vol.1. 2008-06-23T18:42:21-00:00 2001 Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on 1 347 352 vol.1 no-tag http://www.citeulike.org/user/napvasconcelos/article/113848 <i>(20 December 2002)</i><br /><br />The long-anticipated revision of this best-selling book offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For those interested in artificial intelligence. Artificial Intelligence: A Modern Approach (2nd Edition) Stuart Russell Peter Norvig (20 December 2002) 2005-03-04T08:43:21-00:00 2002 Prentice Hall no-tag http://www.citeulike.org/user/napvasconcelos/article/340759 <i>(01 January 2001)</i> Data Mining. Ian Witten Frank Eibe (01 January 2001) 2005-10-04T14:53:45-00:00 2001 Hanser Fachbuch no-tag http://www.citeulike.org/user/napvasconcelos/article/2910537 <i>Cerebral cortex (New York, N.Y. : 1991), Vol. 4, No. 5. (t 1994), pp. 484-498.</i><br /><br />Inferior temporal (IT) cortex is critical for visual pattern recognition in adult primates. However, the functional development of IT cortex appears to be incomplete until late in the first year of life in monkeys and probably beyond. Responses of neurons in IT are substantially weaker, of longer latency, and more susceptible to anesthesia within at least the first half year of life. In addition, refinement of connections of IT, particularly those with regions in the opposite hemisphere and with regions related to memory and attention, continues for at least several months after birth. Moreover, many of the pattern recognition functions that IT supports in adulthood themselves show a very protracted period of development, and damage to IT cortex in infancy appears to have relatively little effect on pattern recognition abilities, despite the pronounced effects of comparable damage in adulthood. These findings all suggest that IT undergoes an extended period of postnatal development, during which both visual experience and the maturation of other brain structures may contribute to the emergence of mechanisms of pattern recognition within IT. In other respects, fundamental characteristics of IT emerge quite early. For example, despite their weaker responses, IT neurons have adult-like patterns of responsiveness--including pronounced form selectivity and large bilateral receptive fields--as early as we were able to test (approximately 6 weeks). Thus, IT cortex appears to be prewired with (or predisposed to develop rapidly) neural circuitry sufficient to produce basic properties remarkably similar to those found in the adult animal. Future studies of IT cortex will need to address the development of signals related to perceptual constancies and to formation and retrieval of visual object memories, the development of interactions with other regions involved in visual recognition (particularly frontal cortex), and the specific mechanisms underlying various types of plasticity present in IT cortex in both developing and mature primates. Development of inferior temporal cortex in the monkey. HR Rodman Cerebral cortex (New York, N.Y. : 1991), Vol. 4, No. 5. (t 1994), pp. 484-498. 2008-06-20T13:42:03-00:00 1994 Cerebral cortex (New York, N.Y. : 1991) 1047-3211 4 5 484 498 cortex inferior temporal v1 http://www.citeulike.org/user/napvasconcelos/article/131501 <i>(15 January 2002)</i> Digital Image Processing (2nd Edition) Rafael Gonzalez Richard Woods (15 January 2002) 2005-03-17T20:14:20-00:00 2002 Prentice Hall no-tag http://www.citeulike.org/user/napvasconcelos/article/312794 <i>(01 June 1982)</i> Vision: A Computational Investigation into the Human Representation and Processing of Visual Information David Marr (01 June 1982) 2005-09-07T20:18:14-00:00 1982 Henry Holt & Company no-tag http://www.citeulike.org/user/napvasconcelos/article/2906364 <i>(15 April 2001)</i><br /><br />This book presents a broad-ranging edited survey of computational and analytical methods and tools for digital image analysis and interpretation. The book brings together the recent results and methods in a uniform manner, thereby making the information accessible to nonspecialists and specialists alike. Topics and features: * Diverse topics are treated in an integrative style, using a common notation * With theory and applications covered in a single volume, the reader sees immediately that the proposed methods also work in practice * Overview of some key research in digital image processing and pattern recognition methods and tools * Up-to-date coverage of current topics: information fusion, stochastic shape theory, graph-based image analysis and hierarchical systems The book offers a uniquely comprehensive technical survey that not only provides in-depth coverage of the fundamental topics in the field, but also incorporates the newest developments that have arisen. It serves as an excellent and current resource for researchers, practitioners and professionals in computer science and electrical engineering focusing on methodology for digital imaging and analysis. Digital Image Analysis: Selected Techniques and Applications H Bischof W Kropatsch (15 April 2001) 2008-06-18T23:41:54-00:00 2001 Springer no-tag http://www.citeulike.org/user/napvasconcelos/article/2906361 <i>Proc. of SPIE, Vol. 1913 (1993), pp. 548-560.</i> Behavioral model of visual perception and recognition IA Rybak AV Golovan VI Gusakova Proc. of SPIE, Vol. 1913 (1993), pp. 548-560. 2008-06-18T23:32:02-00:00 1993 Proc. of SPIE 1913 548 560 no-tag http://www.citeulike.org/user/napvasconcelos/article/2906357 <i>SPIE proceedings, Vol. 5600 (2004), pp. 182-191.</i> Stereo watermarking scheme based on discrete wavelet transform and feature-based window matching algorithm DC Hwang JH Ko JS Park ES Kim SPIE proceedings, Vol. 5600 (2004), pp. 182-191. 2008-06-18T23:27:23-00:00 2004 SPIE proceedings 5600 182 191 no-tag http://www.citeulike.org/user/napvasconcelos/article/2906355 <i>ASIC Conference and Exhibit, 1996. Proceedings., Ninth Annual IEEE International (1996), pp. 287-290.</i><br /><br />A new synthesis method of a multiport two-dimensional filter, a set of design tools based on this method and a library of subcells are described in the paper. It is possible to include the programs as modules into design system of integrated circuits (silicon compiler system). The basic cells of the presented multiport filter are delay lines and bilinear fully differential integrators. Realizations in switched current (SI) technique of these subcells, which can be included into the library of the system, are proposed SI technique application for colour image processing A Handkiewicz P Sniatala M Domanski M Lukowiak doi:10.1109/ASIC.1996.552012 ASIC Conference and Exhibit, 1996. Proceedings., Ninth Annual IEEE International (1996), pp. 287-290. 2008-06-18T23:21:57-00:00 1996 ASIC Conference and Exhibit, 1996. Proceedings., Ninth Annual IEEE International 287 290 no-tag http://www.citeulike.org/user/napvasconcelos/article/1476074 <i>(23 September 1988)</i><br /><br /><P><B></B> Presents a thorough overview of the major topics of digital image processing, beginning with the basic mathematical tools needed for the subject. Includes a comprehensive chapter on stochastic models for digital image processing. <B></B> Covers aspects of image representation including luminance, color, spatial and temporal properties of vision, and digitization. Explores various image processing techniques. Discusses algorithm development (software/firmware) for image transforms, enhancement, reconstruction, and image coding. </P> Fundamentals of Digital Image Processing (Prentice Hall Information and System Sciences Series) Anil Jain (23 September 1988) 2007-07-24T02:03:29-00:00 1988 Prentice Hall no-tag http://www.citeulike.org/user/napvasconcelos/article/2906343 <i>(04 February 2000)</i><br /><br />A unique collection of algorithms and lab experiments for practitioners and researchers of digital image processing technology With the field of digital image processing rapidly expanding, there is a growing need for a book that would go beyond theory and techniques to address the underlying algorithms. Digital Image Processing Algorithms and Applications fills the gap in the field, providing scientists and engineers with a complete library of algorithms for digital image processing, coding, and analysis. Digital image transform algorithms, edge detection algorithms, and image segmentation algorithms are carefully gleaned from the literature for compatibility and a track record of acceptance in the scientific community. The author guides readers through all facets of the technology, supplementing the discussion with detailed lab exercises in EIKONA, his own digital image processing software, as well as useful PDF transparencies. He covers in depth filtering and enhancement, transforms, compression, edge detection, region segmentation, and shape analysis, explaining at every step the relevant theory, algorithm structure, and its use for problem solving in various applications. The availability of the lab exercises and the source code (all algorithms are presented in C-code) over the Internet makes the book an invaluable self-study guide. It also lets interested readers develop digital image processing applications on ordinary desktop computers as well as on Unix machines. Digital Image Processing Algorithms and Applications Ioannis Pitas (04 February 2000) 2008-06-18T23:09:13-00:00 2000 Wiley-Interscience no-tag http://www.citeulike.org/user/napvasconcelos/article/903973 <i>J Neurosci, Vol. 18, No. 20. (15 October 1998), pp. 8455-8466.</i><br /><br />Populations of 10-39 CA1 pyramidal cells were recorded from four rats foraging for food reward in an environment consisting of two nearly identical boxes connected by a corridor. For each rat, a higher-than-chance fraction of cells had similarly shaped spatial firing fields in both boxes, but other cells had completely different fields in the two boxes. The level of correlation of fields in the two boxes differed greatly across rats and, for three of the four rats, across recording sessions. Thus, the factors controlling the level of correlation are likely to be subtle. Two control manipulations were performed. First, the two boxes were physically interchanged. In no case did firing fields move along with the boxes. Second, on the final session of recording, the rat was started in the south box, after having been started in the north box for every previous session. For at least two of the four rats, the north fields from the previous session were instantiated in the south during the first visit of the second session, but thereafter reverted. Thus neither differences between the physical boxes nor sensory input from outside the apparatus could account for the differences in firing fields: most likely they were caused by a combination of learned expectations and a neural mechanism for remembering movements. These findings could be explained either by hypothesizing a more sophisticated attractor-map architecture than has been proposed previously, or by hypothesizing that the hippocampus conjunctively encodes both map information and some other type of information. Spatial firing properties of hippocampal CA1 populations in an environment containing two visually identical regions. WE Skaggs BL Mcnaughton J Neurosci, Vol. 18, No. 20. (15 October 1998), pp. 8455-8466. 2006-10-18T19:45:50-00:00 1998 J Neurosci 0270-6474 18 20 8455 8466 no-tag http://www.citeulike.org/user/napvasconcelos/article/2906192 <i>Nature, Vol. 425, No. 6958. (October 2003), pp. 614-616.</i> Consolidation during sleep of perceptual learning of spoken language Kimberly Fenn Howard Nusbaum Daniel Margoliash doi:10.1038/nature01951 Nature, Vol. 425, No. 6958. (October 2003), pp. 614-616. 2008-06-18T20:49:03-00:00 2003 Nature 425 6958 614 616 no-tag http://www.citeulike.org/user/napvasconcelos/article/578755 <i>Commun. ACM, Vol. 32, No. 1. (January 1989), pp. 9-23.</i> Computing as a discipline DE Comer David Gries Michael Mulder Allen Tucker Joe Turner Paul Young doi:10.1145/63238.63239 Commun. ACM, Vol. 32, No. 1. (January 1989), pp. 9-23. 2006-04-06T20:26:07-00:00 1989 Commun. ACM 0001-0782 32 1 9 23 ACM Press no-tag http://www.citeulike.org/user/napvasconcelos/article/2302057 <i>Nat Neurosci, Vol. 3, No. 12. (December 2000), pp. 1237-1238.</i> Visual discrimination learning requires sleep after training. R Stickgold L James JA Hobson doi:10.1038/81756 Nat Neurosci, Vol. 3, No. 12. (December 2000), pp. 1237-1238. 2008-01-29T13:06:52-00:00 2000 Nat Neurosci 1097-6256 3 12 1237 1238 no-tag http://www.citeulike.org/user/napvasconcelos/article/2906133 <i>Physiology & behavior, Vol. 6, No. 4. (April 1971), pp. 279-282.</i> Disruptive effects of rapid eye movement sleep deprivation on long-term memory. W Fishbein Physiology & behavior, Vol. 6, No. 4. (April 1971), pp. 279-282. 2008-06-18T20:20:23-00:00 1971 Physiology & behavior 0031-9384 6 4 279 282 no-tag http://www.citeulike.org/user/napvasconcelos/article/2906130 <i>The American Journal of Psychology, Vol. 35, No. 4. (1924), pp. 605-612.</i> Obliviscence during Sleep and Waking John Jenkins Karl Dallenbach doi:10.2307/1414040 The American Journal of Psychology, Vol. 35, No. 4. (1924), pp. 605-612. 2008-06-18T20:18:35-00:00 1924 The American Journal of Psychology 35 4 605 612 University of Illinois Press sleep http://www.citeulike.org/user/napvasconcelos/article/2894701 <i>(2000)</i> A Framework for IP Based Virtual Private Networks. RFC 2764 B Gleeson A Lin J Heinanen Telia Finland G Armitage A Malis (2000) 2008-06-14T13:42:23-00:00 2000 vpn http://www.citeulike.org/user/napvasconcelos/article/2888154 <i>Front. Neurosci, Vol. 1, No. 1. (2008), pp. 43-55.</i> Novel experience induces persistent sleep-dependent plasticity in the cortex but not in the hippocampus Ribeiro Front. Neurosci, Vol. 1, No. 1. (2008), pp. 43-55. 2008-06-12T13:40:34-00:00 2008 Front. Neurosci 1 1 43 55 novel platicity http://www.citeulike.org/user/napvasconcelos/article/190492 <i>(28 December 2000)</i> Self-Organizing Maps Teuvo Kohonen (28 December 2000) 2005-05-10T06:12:32-00:00 2000 Springer no-tag http://www.citeulike.org/user/napvasconcelos/article/463065 <i>Proceedings of the IEEE, Vol. 78, No. 9. (1990), pp. 1464-1480.</i><br /><br />The self-organized map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications. The self-organizing map has the property of effectively creating spatially organized internal representations of various features of input signals and their abstractions. One result of this is that the self-organization process can discover semantic relationships in sentences. Brain maps, semantic maps, and early work on competitive learning are reviewed. The self-organizing map algorithm (an algorithm which order responses spatially) is reviewed, focusing on best matching cell selection and adaptation of the weight vectors. Suggestions for applying the self-organizing map algorithm, demonstrations of the ordering process, and an example of hierarchical clustering of data are presented. Fine tuning the map by learning vector quantization is addressed. The use of self-organized maps in practical speech recognition and a simulation experiment on semantic mapping are discussed The self-organizing map T Kohonen doi:10.1109/5.58325 Proceedings of the IEEE, Vol. 78, No. 9. (1990), pp. 1464-1480. 2006-01-12T14:45:42-00:00 1990 Proceedings of the IEEE 78 9 1464 1480 no-tag http://www.citeulike.org/user/napvasconcelos/article/167581 <i>(21 November 2000)</i> Pattern Classification (2nd Edition) Richard Duda Peter Hart David Stork (21 November 2000) 2005-04-22T17:32:18-00:00 2000 Wiley-Interscience no-tag http://www.citeulike.org/user/napvasconcelos/article/2845974 <i>(01 August 2001)</i><br /><br />This book is for students and researchers who have a specific interest in learning and memory and want to understand how computational models can be integrated into experimental research on the hippocampus and learning. It emphasizes the function of brain structures as they give rise to behavior, rather than the molecular or neuronal details. It also emphasizes the process of modeling, rather than the mathematical details of the models themselves. The book is divided into two parts. The first part provides a tutorial introduction to topics in neuroscience, the psychology of learning and memory, and the theory of neural network models. The second part, the core of the book, reviews computational models of how the hippocampus cooperates with other brain structures--including the entorhinal cortex, basal forebrain, cerebellum, and primary sensory and motor cortices--to support learning and memory in both animals and humans. The book assumes no prior knowledge of computational modeling or mathematics. For those who wish to delve more deeply into the formal details of the models, there are optional "mathboxes" and appendices. The book also includes extensive references and suggestions for further readings. Gateway to Memory: An Introduction to Neural Network Modeling of the Hippocampus and Learning (Issues in Clinical and Cognitive Neuropsychology) Mark Gluck Catherine Myers (01 August 2001) 2008-05-29T22:28:18-00:00 2001 The MIT Press no-tag http://www.citeulike.org/user/napvasconcelos/article/368926 <i>(06 July 1998)</i> Neural Networks: A Comprehensive Foundation (2nd Edition) Simon Haykin (06 July 1998) 2005-10-28T10:00:04-00:00 1998 Prentice Hall no-tag http://www.citeulike.org/user/napvasconcelos/article/2845968 <i>(16 April 2004)</i><br /><br />This textbook differs from others in the field in that it has been prepared very much with students and their needs in mind, having been classroom tested over many years.  It is a true “learner’s book” made for students who require a deeper understanding of probability and statistics. It presents the fundamentals of the subject along with concepts of probabilistic modelling, and the process of model selection, verification and analysis.  Furthermore, the inclusion of more than 100 examples and 200 exercises (carefully selected from a wide range of topics), along with a solutions manual for instructors, means that this text is of real value to students and lecturers across a range of engineering disciplines. Key features: * Presents the fundamentals in probability and statistics along with relevant applications. * Explains the concept of probabilistic modelling and the process of model selection, verification and analysis. * Definitions and theorems are carefully stated and topics rigorously treated. * Includes a chapter on regression analysis. * Covers design of experiments. * Demonstrates practical problem solving throughout the book with numerous examples and exercises purposely selected from a variety of engineering fields. * Includes an accompanying online Solutions Manual for instructors containing complete step-by-step solutions to all problems. Fundamentals of Probability and Statistics for Engineers TT Soong (16 April 2004) 2008-05-29T22:23:43-00:00 2004 Wiley-Interscience no-tag http://www.citeulike.org/user/napvasconcelos/article/340715 <i>(08 June 2005)</i><br /><br />As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. <br><br>The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.<br><br>+ Authors, Ian Witten and Eibe Frank, recipients of the 2005 ACM SIGKDD Service Award.<br>+ Algorithmic methods at the heart of successful data miningincluding tried and true techniques as well as leading edge methods; <br>+ Performance improvement techniques that work by transforming the input or output; <br>+ Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualizationin a new, interactive interface. Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) Ian Witten Eibe Frank (08 June 2005) 2005-10-04T14:35:45-00:00 2005 Morgan Kaufmann data learning machine mining http://www.citeulike.org/user/napvasconcelos/article/825800 <i>(01 July 2004)</i><br /><br />Covering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate 'real-world' applications it is destined to become essential reading. Covering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate 'real-world' applications it is destined to become essential reading. Combining Pattern Classifiers: Methods and Algorithms Ludmila Kuncheva (01 July 2004) 2006-09-02T17:12:18-00:00 2004 Wiley-Interscience no-tag http://www.citeulike.org/user/napvasconcelos/article/106556 <i>(15 August 2002)</i><br /><br />This introduction to spiking neurons can be used in advanced-level courses in computational neuroscience, theoretical biology, neural modeling, biophysics, or neural networks. It focuses on phenomenological approaches rather than detailed models in order to provide the reader with a conceptual framework. The authors formulate the theoretical concepts clearly without many mathematical details. While the book contains standard material for courses in computational neuroscience, neural modeling, or neural networks, it also provides an entry to current research. No prior knowledge beyond undergraduate mathematics is required. Spiking Neuron Models Wulfram Gerstner Werner Kistler (15 August 2002) 2005-02-28T14:30:48-00:00 2002 Cambridge University Press model neuron population http://www.citeulike.org/user/napvasconcelos/article/90415 <i>Science, Vol. 265, No. 5172. (29 July 1994), pp. 676-679.</i><br /><br />Simultaneous recordings were made from large ensembles of hippocampal "place cells" in three rats during spatial behavioral tasks and in slow-wave sleep preceding and following these behaviors. Cells that fired together when the animal occupied particular locations in the environment exhibited an increased tendency to fire together during subsequent sleep, in comparison to sleep episodes preceding the behavioral tasks. Cells that were inactive during behavior, or that were active but had non-overlapping spatial firing, did not show this increase. This effect, which declined gradually during each post-behavior sleep session, may result from synaptic modification during waking experience. Information acquired during active behavior is thus re-expressed in hippocampal circuits during sleep, as postulated by some theories of memory consolidation. Reactivation of hippocampal ensemble memories during sleep. MA Wilson BL McNaughton doi:10.1126/science.8036517 Science, Vol. 265, No. 5172. (29 July 1994), pp. 676-679. 2005-02-08T16:20:18-00:00 1994 Science 0036-8075 265 5172 676 679 ensemble hippocampal reactivation sleep http://www.citeulike.org/user/napvasconcelos/article/2048791 <i>Learn. Mem., Vol. 14, No. 11. (15 November 2007), pp. 745-757.</i><br /><br />The hippocampal CA3 subregion is critical for rapidly encoding new memories, which suggests that neuronal computations are implemented in its circuitry that cannot be performed elsewhere in the hippocampus or in the neocortex. Recording studies show that CA3 cells are bound to a large degree to a spatial coordinate system, while CA1 cells can become more independent of a map-based mechanism and allow for a larger degree of arbitrary associations, also in the temporal domain. The mapping of CA3 onto a spatial coordinate system intuitively points to its role in spatial navigation but does not directly suggest how such a mechanism may support memory processing. Although bound to spatial coordinates, the CA3 network can rapidly alter its firing rate in response to novel sensory inputs and is thus not as strictly tied to spatial mapping as grid cells in the medial entorhinal cortex. Such rate coding within an otherwise stable spatial map can immediately incorporate new sensory inputs into the two-dimensional matrix of CA3, where they can be integrated with already stored information about each place. CA3 cell ensembles may thus support the fast acquisition of detailed memories by providing a locally continuous, but globally orthogonal representation, which can rapidly provide a new neuronal index when information is encountered for the first time. This information can be interpreted in CA1 and other downstream cortical areas in the context of less spatially restricted information. 10.1101/lm.703907 Pattern separation, pattern completion, and new neuronal codes within a continuous CA3 map Stefan Leutgeb Jill Leutgeb doi:10.1101/lm.703907 Learn. Mem., Vol. 14, No. 11. (15 November 2007), pp. 745-757. 2007-12-03T10:10:29-00:00 2007 Learn. Mem. 14 11 745 757 pattern recognition http://www.citeulike.org/user/napvasconcelos/article/666230 <i>Neuron, Vol. 49, No. 3. (2 February 2006), pp. 433-445.</i><br /><br />Local field potentials (LFPs) arise largely from dendritic activity over large brain regions and thus provide a measure of the input to and local processing within an area. We characterized LFPs and their relationship to spikes (multi and single unit) in monkey inferior temporal cortex (IT). LFP responses in IT to complex objects showed strong selectivity at 44% of the sites and tolerance to retinal position and size. The LFP preferences were poorly predicted by the spike preferences at the same site but were better explained by averaging spikes within approximately 3 mm. A comparison of separate sites suggests that selectivity is similar on a scale of approximately 800 microm for spikes and approximately 5 mm for LFPs. These observations imply that inputs to IT neurons convey selectivity for complex shapes and that such input may have an underlying organization spanning several millimeters. Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex. G Kreiman CP Hung A Kraskov RQ Quiroga T Poggio JJ DiCarlo doi:10.1016/j.neuron.2005.12.019 Neuron, Vol. 49, No. 3. (2 February 2006), pp. 433-445. 2006-05-23T14:26:51-00:00 2006 Neuron 0896-6273 49 3 433 445 object quality recognition spike http://www.citeulike.org/user/napvasconcelos/article/2291502 <i>PLoS Computational Biology, Vol. 4, No. 1. (1 January 2008), e27.</i><br /><br />Progress in understanding the brain mechanisms underlying vision requires the construction of computational models that not only emulate the brain's anatomy and physiology, but ultimately match its performance on visual tasks. In recent years, “natural” images have become popular in the study of vision and have been used to show apparently impressive progress in building such models. Here, we challenge the use of uncontrolled “natural” images in guiding that progress. In particular, we show that a simple V1-like model—a neuroscientist's “null” model, which should perform poorly at real-world visual object recognition tasks—outperforms state-of-the-art object recognition systems (biologically inspired and otherwise) on a standard, ostensibly natural image recognition test. As a counterpoint, we designed a “simpler” recognition test to better span the real-world variation in object pose, position, and scale, and we show that this test correctly exposes the inadequacy of the V1-like model. Taken together, these results demonstrate that tests based on uncontrolled natural images can be seriously misleading, potentially guiding progress in the wrong direction. Instead, we reexamine what it means for images to be natural and argue for a renewed focus on the core problem of object recognition—real-world image variation. Why is Real-World Visual Object Recognition Hard? Nicolas Pinto David Cox James Dicarlo doi:10.1371/journal.pcbi.0040027 PLoS Computational Biology, Vol. 4, No. 1. (1 January 2008), e27. 2008-01-25T22:16:09-00:00 2008 PLoS Computational Biology 4 1 e27 object qualify recognition http://www.citeulike.org/user/napvasconcelos/article/2820212 <i>Journal of Neuroscience Methods, Vol. 116, No. 2. (15 May 2002), pp. 179-187.</i><br /><br />A common problem in neuroscience is to identify the features by which a set of measurements can be segregated into different classes, for example into different responses to sensory stimuli. A main difficulty is that the derived distributions are often high-dimensional and complex. Many multivariate analysis techniques, therefore, aim to find a simpler low-dimensional representation. Most of them either involve huge efforts in implementation and data handling or ignore important structures and relationships within the original data. We developed a dimension reduction method by means of radial basis functions (RBF), where only a system of linear equations has to be solved. We show that this approach can be regarded as an extension of a linear correlation-based classifier. The validity and reliability of this technique is demonstrated on artificial data sets. Its practical relevance is further confirmed by discriminating recordings from monkey visual cortex evoked by different stimuli. Classification of neural signals by a generalized correlation classifier based on radial basis functions Alexander Kremper Thomas Schanze Reinhard Eckhorn doi:10.1016/S0165-0270(02)00041-9 Journal of Neuroscience Methods, Vol. 116, No. 2. (15 May 2002), pp. 179-187. 2008-05-21T14:31:31-00:00 2002 Journal of Neuroscience Methods 116 2 179 187 classification neural of qualify signals http://www.citeulike.org/user/napvasconcelos/article/444793 <i>Science, Vol. 262, No. 5134. (29 October 1993), pp. 685-688.</i><br /><br />Recognition of objects from their visual images is a key function of the primate brain. This recognition is not a template matching between the input image and stored images like the vision in lower animals but is a flexible process in which considerable change in images, resulting from different illumination, viewing angle, and articulation of the object, can be tolerated. Recent experimental findings about the representation of object images in the inferotemporal cortex, a brain structure that is thought to be essential for object vision, are summarized and discussed in relation to the computational frames proposed for object recognition. Neuronal mechanisms of object recognition. K Tanaka doi:10.1126/science.8235589 Science, Vol. 262, No. 5134. (29 October 1993), pp. 685-688. 2005-12-19T20:25:18-00:00 1993 Science 0036-8075 262 5134 685 688 computation neural qualifyreview http://www.citeulike.org/user/napvasconcelos/article/460034 <i>Science, Vol. 310, No. 5749. (4 November 2005), pp. 863-866.</i><br /><br />Understanding the brain computations leading to object recognition requires quantitative characterization of the information represented in inferior temporal (IT) cortex. We used a biologically plausible, classifier-based readout technique to investigate the neural coding of selectivity and invariance at the IT population level. The activity of small neuronal populations (approximately 100 randomly selected cells) over very short time intervals (as small as 12.5 milliseconds) contained unexpectedly accurate and robust information about both object "identity" and "category." This information generalized over a range of object positions and scales, even for novel objects. Coarse information about position and scale could also be read out from the same population. Fast readout of object identity from macaque inferior temporal cortex. CP Hung G Kreiman T Poggio JJ DiCarlo doi:10.1126/science.1117593 Science, Vol. 310, No. 5749. (4 November 2005), pp. 863-866. 2006-01-08T23:02:37-00:00 2005 Science 1095-9203 310 5749 863 866 computation object recognition http://www.citeulike.org/user/napvasconcelos/article/2708116 <i>(2006)</i> OpenVPN: Building and Integrating Virtual Private Networks M Feilner (2006) 2008-04-23T16:25:56-00:00 2006 Packt Publishing vpn http://www.citeulike.org/user/napvasconcelos/article/2693904 <i>Nature, Vol. 408, No. 6810. (16 November 2000), pp. 361-365.</i> Real-time prediction of hand trajectory by ensembles of cortical neurons in primates Johan Wessberg Christopher Stambaugh Jerald Kralik Pamela Beck Mark Laubach John Chapin Jung Kim James Biggs Mandayam Srinivasan Miguel Nicolelis doi:10.1038/35042582 Nature, Vol. 408, No. 6810. (16 November 2000), pp. 361-365. 2008-04-21T01:06:03-00:00 2000 Nature 408 6810 361 365 dropping neuron http://www.citeulike.org/user/napvasconcelos/article/436795 <i>Journal of Neuroscience Methods, Vol. In Press, Corrected Proof</i><br /><br />We present a client-server application for the distributed multivariate analysis of time series using standard PCs. We here concentrate on analyses of multichannel EEG/MEG data, but our method can easily be adapted to other time series. Due to the rapid development of new analysis techniques, the focus in the design of our application was not only on computational performance, but also on high flexibility and expandability of both the client and the server programs. For this purpose, the communication between the server and the clients as well as the building of the computational tasks has been realized via the Extensible Markup Language (XML). Running our newly developed method in an asynchronous distributed environment with random availability of remote and heterogeneous resources, we tested the system's performance for a number of different univariate and bivariate analysis techniques. Results indicate that for most of the currently available analysis techniques, calculations can be performed in real time, which, in principle, allows on-line analyses at relatively low cost. A distributed computing system for multivariate time series analyses of multichannel neurophysiological data Andy Muller Hannes Osterhage Robert Sowa Ralph Andrzejak Florian Mormann Klaus Lehnertz doi:10.1016/j.jneumeth.2005.09.002 Journal of Neuroscience Methods, Vol. In Press, Corrected Proof 2005-12-13T11:41:39-00:00 Journal of Neuroscience Methods In Press, Corrected Proof computing grid http://www.citeulike.org/user/napvasconcelos/article/149265 <i>Lecture Notes in Computer Science, Vol. 2150 (2001)</i><br /><br />"Grid" computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and, in some cases, high-performance orientation. In this article, we define this new field. First, we review the "Grid problem," which we define as flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources---what we refer to as virtual organizations. In such ... The Anatomy of the Grid: Enabling Scalable Virtual Organizations Ian Foster Carl Kesselman Steven Tuecke Lecture Notes in Computer Science, Vol. 2150 (2001) 2005-04-04T15:20:27-00:00 2001 Lecture Notes in Computer Science 2150 computing grid http://www.citeulike.org/user/napvasconcelos/article/168648 <i>(2003), pp. 164-177.</i> Xen and the art of virtualization Paul Barham Boris Dragovic Keir Fraser Steven Hand Tim Harris Alex Ho Rolf Neugebauer Ian Pratt Andrew Warfield doi:10.1145/945445.945462 (2003), pp. 164-177. 2005-04-24T03:21:56-00:00 2003 164 177 ACM Press no-tag http://www.citeulike.org/user/napvasconcelos/article/2674070 <i>e-Science and Grid Computing, 2005. First International Conference on (2005), 1 pp..</i><br /><br />The importance of service-oriented architecture for science is widely recognized. Increasingly, scientific communities are making information tools accessible as services that clients can access over the network, without knowledge of their internal workings. In this way, tools formerly accessible only to the specialist can be made available to all. Equally importantly, new value-added services can be constructed that integrate other services to automate useful tasks. The value of such service-oriented science has been demonstrated in disciplines as diverse as astronomy, biology, and fusion science. The mechanisms required to achieve these goals are provided, in part, by grid infrastructure. I review the mechanisms that have been developed to date for grid infrastructure and experience gained implementing these mechanisms, for example within the open source Globus Toolkit version 4. I present a range of dynamic service deployment scenarios, in which for example the TeraGrid and Open Science Grid are used to host services for science communities. I discuss how these scenarios demonstrate the potential for scaling service-oriented science. Service-oriented science: scaling the application and impact of eResearch I Foster doi:10.1109/E-SCIENCE.2005.75 e-Science and Grid Computing, 2005. First International Conference on (2005), 1 pp.. 2008-04-15T16:59:28-00:00 2005 e-Science and Grid Computing, 2005. First International Conference on 1 pp. no-tag http://www.citeulike.org/user/napvasconcelos/article/820297 <i>Pattern Recognition Letters, Vol. 27, No. 8. (June 2006), pp. 861-874.</i><br /><br />Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been used increasingly in machine learning and data mining research. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research. An introduction to ROC analysis Tom Fawcett doi:10.1016/j.patrec.2005.10.010 Pattern Recognition Letters, Vol. 27, No. 8. (June 2006), pp. 861-874. 2006-08-29T01:24:20-00:00 2006 Pattern Recognition Letters 27 8 861 874 curve roc http://www.citeulike.org/user/napvasconcelos/article/2671038 <i>Proceedings of the IEEE, Vol. 88, No. 2. (2000), pp. 163-169.</i><br /><br />This paper provides a review of some of the main methodological issues and techniques which have become innovative in addressing the problem of forecasting daily loads and prices in the new competitive power markets. Particular emphasis is placed upon computationally intensive methods, including variable segmentation, multiple modeling, combinations, and neural networks for forecasting the demand side, and strategic simulation using artificial agents for the supply side Forecasting loads and prices in competitive power markets DW Bunn doi:10.1109/5.823996 Proceedings of the IEEE, Vol. 88, No. 2. (2000), pp. 163-169. 2008-04-15T01:53:24-00:00 2000 Proceedings of the IEEE 88 2 163 169 forecasting load markets http://www.citeulike.org/user/napvasconcelos/article/2670950 <i>Neural Networks, 2006. IJCNN '06. International Joint Conference on (2006), pp. 4382-4386.</i><br /><br />This paper presents a grid computing approach to parallel-process a neural network time-series model for forecasting electricity market prices. A grid computing environment introduced in a university computing laboratory provides an access to otherwise unused computing resources. The grid computing of the neural network model not only processes several times faster than a single iterative process but also provides chances of improving forecasting accuracy. Results of numerical tests using the real market data by over twenty grid-connected PCs are reported. Grid Computing Solutions for Artificial Neural Network-based Electricity Market Forecasts N Sakamoto K Ozawa T Niimura doi:10.1109/IJCNN.2006.247037 Neural Networks, 2006. IJCNN '06. International Joint Conference on (2006), pp. 4382-4386. 2008-04-15T01:06:05-00:00 2006 Neural Networks, 2006. IJCNN '06. International Joint Conference on 4382 4386 computing grid network neural http://www.citeulike.org/user/napvasconcelos/article/2653449 <i>Science, Vol. 320, No. 5873. (11 April 2008), pp. 185-186.</i><br /><br />10.1126/science.1157605 NEUROSCIENCE: Axons Seek Neighborly Advice Keith Murai Elena Pasquale doi:10.1126/science.1157605 Science, Vol. 320, No. 5873. (11 April 2008), pp. 185-186. 2008-04-11T11:01:38-00:00 2008 Science 320 5873 185 186 no-tag