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Modeling Neural Development
An important collection showing how computational and mathematical modeling can be used to study the complexities of neural development.
Dendrites
Dendrites are complex neuronal structures that receive and integrate synaptic input from other nerve cells. They therefore play a critical role in brain function. Although dendrites were discovered over a century ago, due to the development of powerful new techniques there has been a dramatic resurgence of interest in the properties and function of these beautiful structures. This is the third edition of the first book devoted exclusively to dendrites. It contains a comprehensive survey of the current state of dendritic research across a wide range of topics, from dendritic morphology, evolution, development, and plasticity through to the electrical, biochemical and computational properties of dendrites, and finally to the key role of dendrites in brain disease. The third edition has been thoroughly revised, with the addition of a number of new chapters and comprehensive updates or rewrites of existing chapters by leading experts. "Dendrites" will be of interest to researchers and students in neuroscience and related fields, as well as to anyone interested in how the brain works.
Artificial Neural Networks and Machine Learning -- ICANN 2012
The two-volume set LNCS 7552 + 7553 constitutes the proceedings of the 22nd International Conference on Artificial Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers included in the proceedings were carefully reviewed and selected from 247 submissions. They are organized in topical sections named: theoretical neural computation; information and optimization; from neurons to neuromorphism; spiking dynamics; from single neurons to networks; complex firing patterns; movement and motion; from sensation to perception; object and face recognition; reinforcement learning; bayesian and echo state networks; recurrent neural networks and reservoir computing; coding architectures; interacting with the brain; swarm intelligence and decision-making; mulitlayer perceptrons and kernel networks; training and learning; inference and recognition; support vector machines; self-organizing maps and clustering; clustering, mining and exploratory analysis; bioinformatics; and time weries and forecasting.
New Approaches for the Generation and Analysis of Microbial Typing Data
Rapid molecular identification and typing of micro-organisms is extremely important in efforts to monitor the geographical spread of virulent, epidemic or antibiotic-resistant pathogens. It has become a mainstay of integrated hospital infection control service. In addition, numerous industrial and biotechnological applications require the study of the diversity of organisms. Conventional phenotypic identification and typing methods have long been the mainstay of microbial population and epidemiological studies, but such methods often lack adequate discrimination and their use is normally confined to the group of organisms for which they were originally devised. Molecular fingerprinting metho...
The Cortex and the Critical Point
How the cerebral cortex operates near a critical phase transition point for optimum performance. Individual neurons have limited computational powers, but when they work together, it is almost like magic. Firing synchronously and then breaking off to improvise by themselves, they can be paradoxically both independent and interdependent. This happens near the critical point: when neurons are poised between a phase where activity is damped and a phase where it is amplified, where information processing is optimized, and complex emergent activity patterns arise. The claim that neurons in the cortex work best when they operate near the critical point is known as the criticality hypothesis. In th...
Computational Neuroscience: Trends in Research 2004
The CNS meetings bring together computational neuroscientists representing many different fields and backgrounds as well as many different experimental preparations and theoretical approaches. The papers published here range from pure experimental neurobiology, to neuro-ethology, mathematics, physics, and engineering. In all cases the research described is focused on understanding how nervous systems compute. The actual subjects of the research include a highly diverse number of preparations, modeling approaches and analysis techniques. Accordingly, this volume reflects the breadth and depth of current research in computational neuroscience taking place throughout the world.
Advances in Cognitive Neurodynamics (II)
Within our knowledge, the series of the International Conference on Cognitive Neurodynamics (ICCN) is the only conference series dedicating to cognitive neurodynamics. This volume is the proceedings of the 2nd International Conference on Cognitive Neurodynamics held in 2009, which reviews the progress in this field since the 1st ICCN -2007. The topics include: Neural coding and realistic neural network dynamics, Neural population dynamics, Firing Oscillations and Patterns in Neuronal Networks, Brain imaging, EEG, MEG, Sensory and Motor Dynamics, Global cognitive function, Multi-scalar Neurodynamics - from Physiology to Systems Theory, Neural computing, Emerging Technologies for Brain Computer Interfaces, Neural dynamics of brain disorders.
The Self-Organizing Brain: From Growth Cones to Functional Networks
This book concentrates on the organizational level of neurons and neuronal networks under the unifying theme "The Self-Organizing Brain - From Growth Cones to Functional Networks". Such a theme is attractive because it incorporates all phases in the emergence of complexity and (adaptive) organization, as well as involving processes that remain operative in the mature state.The order of the sections follows successive levels of organization from neuronal growth cones, neurite formation, neuronal morphology and signal processing to network development, network dynamics and, finally, to the formation of functional circuits.
Gradient Expectations
An insightful investigation into the mechanisms underlying the predictive functions of neural networks—and their ability to chart a new path for AI. Prediction is a cognitive advantage like few others, inherently linked to our ability to survive and thrive. Our brains are awash in signals that embody prediction. Can we extend this capability more explicitly into synthetic neural networks to improve the function of AI and enhance its place in our world? Gradient Expectations is a bold effort by Keith L. Downing to map the origins and anatomy of natural and artificial neural networks to explore how, when designed as predictive modules, their components might serve as the basis for the simula...
Anatomy and Plasticity in Large-Scale Brain Models
Supercomputing facilities are becoming increasingly available for simulating activity dynamics in large-scale neuronal networks. On today's most advanced supercomputers, networks with up to a billion of neurons can be readily simulated. However, building biologically realistic, full-scale brain models requires more than just a huge number of neurons. In addition to network size, the detailed local and global anatomy of neuronal connections is of crucial importance. Moreover, anatomical connectivity is not fixed, but can rewire throughout life (structural plasticity)—an aspect that is missing in most current network models, in which plasticity is confined to changes in synaptic strength (sy...
