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Sparse Algorithms For Decoding And Identification Of Neural Circuits
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Book Synopsis Sparse Algorithms for Decoding and Identification of Neural Circuits by : Nikul Ukani
Download or read book Sparse Algorithms for Decoding and Identification of Neural Circuits written by Nikul Ukani and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Key applications in FFBO, and the software and computational infrastructure enabling them, are described along with case studies.
Book Synopsis Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits by : Dorian Florescu
Download or read book Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits written by Dorian Florescu and published by Springer. This book was released on 2017-04-24 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work is motivated by the ongoing open question of how information in the outside world is represented and processed by the brain. Consequently, several novel methods are developed. A new mathematical formulation is proposed for the encoding and decoding of analog signals using integrate-and-fire neuron models. Based on this formulation, a novel algorithm, significantly faster than the state-of-the-art method, is proposed for reconstructing the input of the neuron. Two new identification methods are proposed for neural circuits comprising a filter in series with a spiking neuron model. These methods reduce the number of assumptions made by the state-of-the-art identification framework, allowing for a wider range of models of sensory processing circuits to be inferred directly from input-output observations. A third contribution is an algorithm that computes the spike time sequence generated by an integrate-and-fire neuron model in response to the output of a linear filter, given the input of the filter encoded with the same neuron model.
Book Synopsis Decoding Neural Circuit Structure and Function by : Arzu Çelik
Download or read book Decoding Neural Circuit Structure and Function written by Arzu Çelik and published by Springer. This book was released on 2017-07-24 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers representative examples from fly and mouse models to illustrate the ongoing success of the synergistic, state-of-the-art strategy, focusing on the ways it enhances our understanding of sensory processing. The authors focus on sensory systems (vision, olfaction), which are particularly powerful models for probing the development, connectivity, and function of neural circuits, to answer this question: How do individual nerve cells functionally cooperate to guide behavioral responses? Two genetically tractable species, mice and flies, together significantly further our understanding of these processes. Current efforts focus on integrating knowledge gained from three interrelated fields of research: (1) understanding how the fates of different cell types are specified during development, (2) revealing the synaptic connections between identified cell types (“connectomics”) using high-resolution three-dimensional circuit anatomy, and (3) causal testing of how iden tified circuit elements contribute to visual perception and behavior.
Book Synopsis Massively Parallel Spiking Neural Circuits by :
Download or read book Massively Parallel Spiking Neural Circuits written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Implications of this result on the problem of invariant object recognition in the spike domain are discussed.
Book Synopsis Interpretable Machine Learning by : Christoph Molnar
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Book Synopsis MATLAB for Neuroscientists by : Pascal Wallisch
Download or read book MATLAB for Neuroscientists written by Pascal Wallisch and published by Academic Press. This book was released on 2014-01-09 with total page 571 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional material on the creation of visual stimuli, advanced psychophysics, analysis of LFP data, choice probabilities, synchrony, and advanced spectral analysis. Users at a variety of levels—advanced undergraduates, beginning graduate students, and researchers looking to modernize their skills—will learn to design and implement their own analytical tools, and gain the fluency required to meet the computational needs of neuroscience practitioners. The first complete volume on MATLAB focusing on neuroscience and psychology applications Problem-based approach with many examples from neuroscience and cognitive psychology using real data Illustrated in full color throughout Careful tutorial approach, by authors who are award-winning educators with strong teaching experience
Book Synopsis Sparse Distributed Memory by : Pentti Kanerva
Download or read book Sparse Distributed Memory written by Pentti Kanerva and published by MIT Press. This book was released on 1988 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motivated by the remarkable fluidity of memory the way in which items are pulled spontaneously and effortlessly from our memory by vague similarities to what is currently occupying our attention "Sparse Distributed Memory "presents a mathematically elegant theory of human long term memory.The book, which is self contained, begins with background material from mathematics, computers, and neurophysiology; this is followed by a step by step development of the memory model. The concluding chapter describes an autonomous system that builds from experience an internal model of the world and bases its operation on that internal model. Close attention is paid to the engineering of the memory, including comparisons to ordinary computer memories."Sparse Distributed Memory "provides an overall perspective on neural systems. The model it describes can aid in understanding human memory and learning, and a system based on it sheds light on outstanding problems in philosophy and artificial intelligence. Applications of the memory are expected to be found in the creation of adaptive systems for signal processing, speech, vision, motor control, and (in general) robots. Perhaps the most exciting aspect of the memory, in its implications for research in neural networks, is that its realization with neuronlike components resembles the cortex of the cerebellum.Pentti Kanerva is a scientist at the Research Institute for Advanced Computer Science at the NASA Ames Research Center and a visiting scholar at the Stanford Center for the Study of Language and Information. A Bradford Book.
Book Synopsis Multimedia Technology and Enhanced Learning by : Yu-Dong Zhang
Download or read book Multimedia Technology and Enhanced Learning written by Yu-Dong Zhang and published by Springer Nature. This book was released on 2020-07-18 with total page 507 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume book constitutes the refereed proceedings of the Second International Conference on Multimedia Technology and Enhanced Learning, ICMTEL 2020, held in Leicester, United Kingdom, in April 2020. Due to the COVID-19 pandemic all papers were presented in YouTubeLive. The 83 revised full papers have been selected from 158 submissions. They describe new learning technologies which range from smart school, smart class and smart learning at home and which have been developed from new technologies such as machine learning, multimedia and Internet of Things.
Book Synopsis Neuromodulation in Psychiatry by : Clement Hamani
Download or read book Neuromodulation in Psychiatry written by Clement Hamani and published by John Wiley & Sons. This book was released on 2016-01-26 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: Edited by an expert multidisciplinary team, Neuromodulation in Psychiatry is the first reference guide to address both invasive and non-invasive neuromodulation strategies used in psychiatry. Covers basic principles, technical aspects, clinical applications and ethical considerations Presents up-to-date evidence in comprehensive summaries suitable for all levels of experience Each technique is clearly explained along with its implications for real-world clinical practice Allows psychiatrists to make informed decisions regarding neuromodulation for their patients
Book Synopsis Statistical Signal Processing for Neuroscience and Neurotechnology by : Karim G. Oweiss
Download or read book Statistical Signal Processing for Neuroscience and Neurotechnology written by Karim G. Oweiss and published by Academic Press. This book was released on 2010-09-22 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and hardware tools that are specifically tailored to the nature of the neurobiological environment. It gives a broad overview of the basic principles, theories and methods in statistical signal processing for basic and applied neuroscience problems.Written by experts in the field, the book is an ideal reference for researchers working in the field of neural engineering, neural interface, computational neuroscience, neuroinformatics, neuropsychology and neural physiology. By giving a broad overview of the basic principles, theories and methods, it is also an ideal introduction to statistical signal processing in neuroscience. A comprehensive overview of the specific problems in neuroscience that require application of existing and development of new theory, techniques, and technology by the signal processing community Contains state-of-the-art signal processing, information theory, and machine learning algorithms and techniques for neuroscience research Presents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems
Book Synopsis Information Theory, Inference and Learning Algorithms by : David J. C. MacKay
Download or read book Information Theory, Inference and Learning Algorithms written by David J. C. MacKay and published by Cambridge University Press. This book was released on 2003-09-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
Book Synopsis Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design by : Nan Zheng
Download or read book Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design written by Nan Zheng and published by John Wiley & Sons. This book was released on 2019-10-18 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design also covers many fundamentals and essentials in neural networks (e.g., deep learning), as well as hardware implementation of neural networks. The book begins with an overview of neural networks. It then discusses algorithms for utilizing and training rate-based artificial neural networks. Next comes an introduction to various options for executing neural networks, ranging from general-purpose processors to specialized hardware, from digital accelerator to analog accelerator. A design example on building energy-efficient accelerator for adaptive dynamic programming with neural networks is also presented. An examination of fundamental concepts and popular learning algorithms for spiking neural networks follows that, along with a look at the hardware for spiking neural networks. Then comes a chapter offering readers three design examples (two of which are based on conventional CMOS, and one on emerging nanotechnology) to implement the learning algorithm found in the previous chapter. The book concludes with an outlook on the future of neural network hardware. Includes cross-layer survey of hardware accelerators for neuromorphic algorithms Covers the co-design of architecture and algorithms with emerging devices for much-improved computing efficiency Focuses on the co-design of algorithms and hardware, which is especially critical for using emerging devices, such as traditional memristors or diffusive memristors, for neuromorphic computing Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design is an ideal resource for researchers, scientists, software engineers, and hardware engineers dealing with the ever-increasing requirement on power consumption and response time. It is also excellent for teaching and training undergraduate and graduate students about the latest generation neural networks with powerful learning capabilities.
Book Synopsis Memristive Neuromorphics: Materials, Devices, Circuits, Architectures, Algorithms and their Co-Design by : Huanglong Li
Download or read book Memristive Neuromorphics: Materials, Devices, Circuits, Architectures, Algorithms and their Co-Design written by Huanglong Li and published by Frontiers Media SA. This book was released on 2022-02-21 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Scientific and Technical Aerospace Reports by :
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Science Abstracts written by and published by . This book was released on 1993 with total page 980 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advances in Neural Information Processing Systems 17 by : Lawrence K. Saul
Download or read book Advances in Neural Information Processing Systems 17 written by Lawrence K. Saul and published by MIT Press. This book was released on 2005 with total page 1710 pages. Available in PDF, EPUB and Kindle. Book excerpt: Papers presented at NIPS, the flagship meeting on neural computation, held in December 2004 in Vancouver.The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December, 2004 conference, held in Vancouver.
Book Synopsis Learning Deep Architectures for AI by : Yoshua Bengio
Download or read book Learning Deep Architectures for AI written by Yoshua Bengio and published by Now Publishers Inc. This book was released on 2009 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
