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 Networks in Brain Function, CNS Disorders, and Therapeutics, edited by two leaders in the field, offers a current and complete review of what we know about neural networks. How the brain accomplishes many of its more complex tasks can only be understood via study of neuronal network control and network interactions. Large networks can undergo major functional changes, resulting in substantially different brain function and affecting everything from learning to the potential for epilepsy. With chapters authored by experts in each topic, this book advances the understanding of: - How the brain carries out important tasks via networks - How these networks interact in normal brain function - Major mechanisms that control network function - The interaction of the normal networks to produce more complex behaviors - How brain disorders can result from abnormal interactions - How therapy of disorders can be advanced through this network approach This book will benefit neuroscience researchers and graduate students with an interest in networks, as well as clinicians in neuroscience, pharmacology, and psychiatry dealing with neurobiological disorders. - Utilizes perspectives and tools from various neuroscience subdisciplines (cellular, systems, physiologic), making the volume broadly relevant - Chapters explore normal network function and control mechanisms, with an eye to improving therapies for brain disorders - Reflects predominant disciplinary shift from an anatomical to a functional perspective of the brain - Edited work with chapters authored by leaders in the field around the globe – the broadest, most expert coverage available

This dissertation, "Global Coherent Activities in Inhibitory Neural Systems: Chik Tai Wai David." by Tai-wai, David, Chik, 戚大衛, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled GLOBAL COHERENT ACTIVITIES IN INHIBITORY NEURAL SYSTEMS submitted by Chik Tai Wai David for the degree of Doctor of Philosophy at The University of Hong Kong in August 2004 Global coherent activities have been observed in the brains of both human beings and animals. The electrical discharges of a large number of neurons are either synchronized or grouped into a few clusters. These phenomena are suggested to function as information binding, which is essential to various cognitive processes. On the other hand, post-inhibitory rebound is a nonlinear phenomenon present in a variety of nerve cells. Following a period of hyper-polarization, this effect allows a neuron to fire a spike or packet of spikes before returning to rest. Based on suitable mathematical models, global coherent behaviours in a network of inhibitory coupled neurons were studied. First, noise-induced weak synchronized oscillatory activities in a globally in- hibitory coupled Hodgkin-Huxley neuronal network were studied numerically.A kind of intrinsic delay was observed and was found to be important in de- termining the overall frequency of the network. Synchronization occurred in an optimal range of noise intensity with a bell-shaped curve when the inhibitory coupling strength was sufficiently strong. Comparisons with the results for the excitatory coupling were also addressed. Next, it was found that networks of neurons, which did not intrinsically oscillate, might make use of inhibitory synaptic connections to generate large scale coherent rhythms in the form of cluster states. They were classified into two cases: (i) the rebound mechanism was due to anode break excitation and (ii) it was due to a slow T-type calcium current. In the former case, a geometric analysis on a McKean type model was used to obtain expressions for the number of clusters in terms of the speed and strength of synaptic coupling. Results were found to be in good qualitative agreement with numerical simulations of the more detailed Hodgkin-Huxley model. In the second case, a particular firing rate model of a neuron with a slow calcium current was considered, that admitted to an exact analysis. Once again existence regions for cluster states were explicitly calculated. Both mechanisms were shown to prefer globally synchronous states for slow synapses as long as the strength of coupling is sufficiently large. With a decrease in the duration of synaptic inhibition, both systems were found to break into clusters. A major difference between the two mechanisms for cluster generation was that anode break excitation could support clusters with several groups, while slow T-type calcium currents predominantly gave rise to clusters of just two (anti-synchronous) populations. The results agreed with biological experiments and offered a better under- standing on the nonlinear dynamics underlying synchrony and clustering in the nervous system. DOI: 10.5353/th_b3104040 Subjects: Neural networks (Neurobiology) Neurons - Physiology Neurophysiology

Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same "object" of, for example, a visual scene by spiking at the very same time. The traditional description of a neural network through a firing rate, the famous S-shaped curve, presupposes a wide time window of, say, at least 100 ms. It thus fails to exploit the capacity to "bind" sets of coherently firing neurons for the purpose of both scene segmentation and figure-ground segregation. Feedback is a dominant feature of the structural organization of the brain. Recurrent neural networks have been studied extensively in the physical literature, starting with the ground breaking work of John Hop field (1982).

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.

In recent years, complex-valued neural networks have widened the scope of application in optoelectronics, imaging, remote sensing, quantum neural devices and systems, spatiotemporal analysis of physiological neural systems, and artificial neural information processing. In this first-ever book on complex-valued neural networks, the most active scientists at the forefront of the field describe theories and applications from various points of view to provide academic and industrial researchers with a comprehensive understanding of the fundamentals, features and prospects of the powerful complex-valued networks.

The most conspicuous function of the nervous system is to control animal behav ior. From the complex operations of learning and mentation to the molecular con figuration of ionic channels, the nervous system serves as the interface between an animal and its environment. To study and understand the fundamental mecha nisms underlying the control of behavior, it is often both necessary and desirable to employ biological systems with characteristics especially suitable for answering specific questions. In neurobiology, many invertebrates have become established as model systems for investigations at both the systems and the cellular level. Large, readily identifiable neurons have made invertebrates especially useful for cellular studies. The fact that these neurons occur in much smaller numbers than those in higher animals also makes them important for circuit analysis. Although important differences exist, some of the questions that would be tech nically impossible to answer with vertebrates can become experimentally tractable with invertebrates.

This monograph instructs graduate- and undergraduate-level students in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering on the concepts of complex-valued neural networks. Emphasizing basic concepts and ways of thinking about neural networks, the author focuses on neural networks that deal with complex numbers; the practical advantages of complex-valued neural networks, and their origins; the development of principal applications? The book uses detailed examples to answer these questions and more.

The result of the first Appalachian Conference on neurodynamics, this volume focuses on processing in biological neural networks. How do brain processes become organized during decision making? That is, what are the neural antecedents that determine which course of action is to be pursued? Half of the contributions deal with modelling synapto-dendritic and neural ultrastructural processes; the remainder, with laboratory research findings, often cast in terms of the models. The interchanges at the conference and the ensuing publication also provide a foundation for further meetings. These will address how processes in different brain systems, coactive with the neural residues of experience and with sensory input, determine decisions.