Author: Arthur Rudolf HouwelingPublisher:
ISBN:
Category :
Languages : en
Pages : 278
Book Description
Network Dynamics in Computer Models of Neocortex with Synaptic Plasticity
Author: Arthur Rudolf HouwelingPublisher:
ISBN:
Category :
Languages : en
Pages : 278
Book Description
Neural Network Dynamics
Author: J.G. TaylorPublisher: Springer Science & Business Media
ISBN: 1447120019
Category : Computers
Languages : en
Pages : 378
Book Description
Neural Network Dynamics is the latest volume in the Perspectives in Neural Computing series. It contains papers presented at the 1991 Workshop on Complex Dynamics in Neural Networks, held at IIASS in Vietri, Italy. The workshop encompassed a wide range of topics in which neural networks play a fundamental role, and aimed to bridge the gap between neural computation and computational neuroscience. The papers - which have been updated where necessary to include new results - are divided into four sections, covering the foundations of neural network dynamics, oscillatory neural networks, as well as scientific and biological applications of neural networks. Among the topics discussed are: A general analysis of neural network activity; Descriptions of various network architectures and nodes; Correlated neuronal firing; A theoretical framework for analyzing the behaviour of real and simulated neuronal networks; The structural properties of proteins; Nuclear phenomenology; Resonance searches in high energy physics; The investigation of information storage; Visual cortical architecture; Visual processing. Neural Network Dynamics is the first volume to cover neural networks and computational neuroscience in such detail. Although it is primarily aimed at researchers and postgraduate students in the above disciplines, it will also be of interest to researchers in electrical engineering, medicine, psychology and philosophy.
Coherent Behavior in Neuronal Networks
Author: Krešimir JosicPublisher: Springer Science & Business Media
ISBN: 1441903895
Category : Medical
Languages : en
Pages : 311
Book Description
Recent experimental research advances have led to increasingly detailed descriptions of how networks of interacting neurons process information. With these developments, it has become clear that dynamic network behaviors underlie information processing, and that the observed activity patterns cannot be fully explained by simple concepts such as synchrony and phase locking. These new insights raise significant challenges and offer exciting opportunities for experimental and theoretical neuroscientists. Coherent Behavior in Neuronal Networks features a review of recent research in this area from some of the world’s foremost experts on systems neuroscience. The book presents novel methodologies and interdisciplinary perspectives, and will serve as an invaluable resource to the research community. Highlights include the results of interdisciplinary collaborations and approaches as well as topics, such as the interplay of intrinsic and synaptic dynamics in producing coherent neuronal network activity and the roles of globally coherent rhythms and oscillations in the coordination of distributed processing, that are of significant research interest but have been underrepresented in the review literature. With its cutting-edge mathematical, statistical, and computational techniques, this volume will be of interest to all researchers and students in the field of systems neuroscience.
Neuronal Dynamics
Author: Wulfram GerstnerPublisher: Cambridge University Press
ISBN: 1107060834
Category : Computers
Languages : en
Pages : 591
Book Description
This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.
Computational Models for Neuroscience
Author: Robert Hecht-NielsenPublisher: Springer Science & Business Media
ISBN: 1447100859
Category : Medical
Languages : en
Pages : 311
Book Description
Formal study of neuroscience (broadly defined) has been underway for millennia. For example, writing 2,350 years ago, Aristotle! asserted that association - of which he defined three specific varieties - lies at the center of human cognition. Over the past two centuries, the simultaneous rapid advancements of technology and (conse quently) per capita economic output have fueled an exponentially increasing effort in neuroscience research. Today, thanks to the accumulated efforts of hundreds of thousands of scientists, we possess an enormous body of knowledge about the mind and brain. Unfortunately, much of this knowledge is in the form of isolated factoids. In terms of "big picture" understanding, surprisingly little progress has been made since Aristotle. In some arenas we have probably suffered negative progress because certain neuroscience and neurophilosophy precepts have clouded our self-knowledge; causing us to become largely oblivious to some of the most profound and fundamental aspects of our nature (such as the highly distinctive propensity of all higher mammals to automatically seg ment all aspects of the world into distinct holistic objects and the massive reorganiza tion of large portions of our brains that ensues when we encounter completely new environments and life situations). At this epoch, neuroscience is like a huge collection of small, jagged, jigsaw puz zle pieces piled in a mound in a large warehouse (with neuroscientists going in and tossing more pieces onto the mound every month).
Computational Neuroscience: Cortical Dynamics
Author: Péter ÉrdiPublisher: Springer Science & Business Media
ISBN: 3540225668
Category : Computers
Languages : en
Pages : 169
Book Description
This book presents thoroughly revised tutorial papers based on lectures given by leading researchers at the 8th International Summer School on Neural Networks in Erice, Italy, in October/November 2003. The eight tutorial papers presented provide competent coverage of the field of cortical dynamics, consolidating recent theoretical and experimental results on the processing, transmission, and imprinting of information in the brain as well as on important functions of the cortical area, such as cortical rhythms, cortical neural plasticity, and their structural basis and functional significance. The book is divided in two topical sections on fundamentals of cortical dynamics and mathematical models of cortical dynamics.
Dynamic Interactions in Neural Networks: Models and Data
Author: Michael A. ArbibPublisher: Springer Science & Business Media
ISBN: 1461245362
Category : Computers
Languages : en
Pages : 275
Book Description
This is an exciting time. The study of neural networks is enjoying a great renaissance, both in computational neuroscience - the development of information processing models of living brains - and in neural computing - the use of neurally inspired concepts in the construction of "intelligent" machines. Thus the title of this volume, Dynamic Interactions in Neural Networks: Models and Data can be given two interpretations. We present models and data on the dynamic interactions occurring in the brain, and we also exhibit the dynamic interactions between research in computational neuroscience and in neural computing, as scientists seek to find common principles that may guide us in the understanding of our own brains and in the design of artificial neural networks. In fact, the book title has yet a third interpretation. It is based on the U. S. -Japan Seminar on "Competition and Cooperation in Neural Nets" which we organized at the University of Southern California, Los Angeles, May 18-22, 1987, and is thus the record of interaction of scientists on both sides of the Pacific in advancing the frontiers of this dynamic, re-born field. The book focuses on three major aspects of neural network function: learning, perception, and action. More specifically, the chapters are grouped under three headings: "Development and Learning in Adaptive Networks," "Visual Function", and "Motor Control and the Cerebellum.
Synaptic Plasticity for Neuromorphic Systems
Author: Christian MayrPublisher: Frontiers Media SA
ISBN: 2889198774
Category : Neurosciences. Biological psychiatry. Neuropsychiatry
Languages : en
Pages : 178
Book Description
One of the most striking properties of biological systems is their ability to learn and adapt to ever changing environmental conditions, tasks and stimuli. It emerges from a number of different forms of plasticity, that change the properties of the computing substrate, mainly acting on the modification of the strength of synaptic connections that gate the flow of information across neurons. Plasticity is an essential ingredient for building artificial autonomous cognitive agents that can learn to reliably and meaningfully interact with the real world. For this reason, the neuromorphic community at large has put substantial effort in the design of different forms of plasticity and in putting them to practical use. These plasticity forms comprise, among others, Short Term Depression and Facilitation, Homeostasis, Spike Frequency Adaptation and diverse forms of Hebbian learning (e.g. Spike Timing Dependent Plasticity). This special research topic collects the most advanced developments in the design of the diverse forms of plasticity, from the single circuit to the system level, as well as their exploitation in the implementation of cognitive systems.
Neural Network Models of Cognition
Author: J.W. DonahoePublisher: Elsevier
ISBN: 0080537367
Category : Computers
Languages : en
Pages : 601
Book Description
This internationally authored volume presents major findings, concepts, and methods of behavioral neuroscience coordinated with their simulation via neural networks. A central theme is that biobehaviorally constrained simulations provide a rigorous means to explore the implications of relatively simple processes for the understanding of cognition (complex behavior). Neural networks are held to serve the same function for behavioral neuroscience as population genetics for evolutionary science. The volume is divided into six sections, each of which includes both experimental and simulation research: (1) neurodevelopment and genetic algorithms, (2) synaptic plasticity (LTP), (3) sensory/hippocampal systems, (4) motor systems, (5) plasticity in large neural systems (reinforcement learning), and (6) neural imaging and language. The volume also includes an integrated reference section and a comprehensive index.
Space-Time Computing with Temporal Neural Networks
Author: James E. SmithPublisher: Morgan & Claypool Publishers
ISBN: 1627058907
Category : Computers
Languages : en
Pages : 245
Book Description
Understanding and implementing the brain's computational paradigm is the one true grand challenge facing computer researchers. Not only are the brain's computational capabilities far beyond those of conventional computers, its energy efficiency is truly remarkable. This book, written from the perspective of a computer designer and targeted at computer researchers, is intended to give both background and lay out a course of action for studying the brain's computational paradigm. It contains a mix of concepts and ideas drawn from computational neuroscience, combined with those of the author. As background, relevant biological features are described in terms of their computational and communication properties. The brain's neocortex is constructed of massively interconnected neurons that compute and communicate via voltage spikes, and a strong argument can be made that precise spike timing is an essential element of the paradigm. Drawing from the biological features, a mathematics-based computational paradigm is constructed. The key feature is spiking neurons that perform communication and processing in space-time, with emphasis on time. In these paradigms, time is used as a freely available resource for both communication and computation. Neuron models are first discussed in general, and one is chosen for detailed development. Using the model, single-neuron computation is first explored. Neuron inputs are encoded as spike patterns, and the neuron is trained to identify input pattern similarities. Individual neurons are building blocks for constructing larger ensembles, referred to as "columns". These columns are trained in an unsupervised manner and operate collectively to perform the basic cognitive function of pattern clustering. Similar input patterns are mapped to a much smaller set of similar output patterns, thereby dividing the input patterns into identifiable clusters. Larger cognitive systems are formed by combining columns into a hierarchical architecture. These higher level architectures are the subject of ongoing study, and progress to date is described in detail in later chapters. Simulation plays a major role in model development, and the simulation infrastructure developed by the author is described.