Neural-like oscillatory network models allow to elucidate the possibilities of dynamical, synchronization-based types of image processing exploited by the brain. The oscillatory network capabilities, studied by means of computer modeling and qualitative analysis, are presented and discussed in this book, as well as several other problems of parallel distributed information processing.

Written for those interested in designing machines to perform intelligent functions & those interested in studying how these functions are performed by living organisms,this bk dicusses the mathematical structure & functional significance of neural oscil

Understanding of the human brain functioning currently represents a challenging problem. In contrast to usual serial computers and complicated hierarchically organized artificial man-made systems, decentralized, parallel and distributed information processing principles are inherent to the brain. Besides adaptation and learning, which play a crucial role in brain functioning, oscillatory neural activity, synchronization and resonance accompany the brain work. Neural-like oscillatory network models, designed by the authors for image processing, allow to elucidate the capabilities of dynamical, synchronization-based types of image processing, presumably exploited by the brain. The oscillatory network models, studied by means of computer modeling and qualitative analysis, are presented and discussed in the book. Some other problems of parallel distributed information processing are also considered, such as a recall process from network memory for large-scale recurrent associative memory neural networks, performance of oscillatory networks of associative memory, dynamical oscillatory network methods of image processing with synchronization-based performance, optical parallel information processing based on the nonlinear optical phenomenon of photon echo, and modeling random electric fields of quasi-monochromatic polarized light beams using systems of superposed stochastic oscillators. This makes the book highly interesting to researchers dealing with various aspects of parallel information processing.

Devoted to local and global analysis of weakly connected systems with applications to neurosciences, this book uses bifurcation theory and canonical models as the major tools of analysis. It presents a systematic and well motivated development of both weakly connected system theory and mathematical neuroscience, addressing bifurcations in neuron and brain dynamics, synaptic organisations of the brain, and the nature of neural codes. The authors present classical results together with the most recent developments in the field, making this a useful reference for researchers and graduate students in various branches of mathematical neuroscience.

A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.

Dendrites form the major receiving part of neurons. This text presents a survey of knowledge on dendrites, from their morphology and development, through to their electrical chemical, and computational properties.

The purpose of this work is to review recent findings highlighting the mechanisms and functions of the neuronal oscillations that structure brain activity across the sleep-wake cycle. An increasing number of studies conducted in humans and animals, and using a variety of techniques ranging from intracellular recording to functional neuroimaging, has provided important insight into the mechanisms and functional properties of these brain rhythms. Studies of these rhythms are fundamental not only for basic neuroscience, but also for clinical neuroscience. At the basic science level, neuronal oscillations shape the interactions between different areas of the brain and profoundly impact neural responses to the environment, thereby mediating the processing of information in the brain. At the clinical level, brain oscillations are affected in numerous neurological conditions and might provide useful biomarkers that inform about patients’ evolution and vulnerability. During sleep, these brain rhythms could provide functional support to internal states that govern the basic maintenance of local circuit and systemic interactions. During wake, the rhythmicity of cortical and subcortical circuits have been linked with sensory processing, cognitive operations, and preparation for action. This book will attempt to link together these sleep and wake functional roles at the level of neuroimaging and electroencephalographic measures, local field potentials, and even at the cellular level. ​