Download Free Development Of Novel Neurotechnologies And Computational Models For Investigation Of Brain Function Book in PDF and EPUB Free Download. You can read online Development Of Novel Neurotechnologies And Computational Models For Investigation Of Brain Function and write the review.

The brain is the most complex part of human body. It is the source of intelligence and human behavior. Understanding how the brain works is essential for treatment of neurological disorders and development of more powerful artificial intelligence algorithms. To investigate its function, novel neurotechnologies are needed to record large-scale neural activity at high spatiotemporal resolution. Besides that, computational models could also be built to investigate possible mechanisms for neural activities at different levels. This dissertation presents research work on novel neurotechnologies and computational frameworks based on machine learning and biophysical modeling for investigation of brain functions. Chapter 1 presents a flexible insertable transparent microelectrode array (Neuro-FITM) for simultaneous electrical recordings from hippocampus and wide-field calcium imaging of dorsal cortex. We successfully recorded simultaneous activity of large-scale cortex and hippocampus. Using two-stage tensor component analysis and decoding analysis with support vector machines, we demonstrated distinct associations between hippocampus and cortex during hippocampal sharp-wave ripples. Chapter 2 presents a compact closed-loop optogenetics system based on transparent graphene microelectrode array. We extensively investigated light-induced artifacts for graphene electrodes and gold electrodes. We also validated the system for different frequencies of interest for neural recordings. The developed system can be used for various applications involving optogenetic stimulation and electrophysiological recordings. Chapter 3 presents the decoding of cortex-wide brain activity using surface electrical recordings over the cortex. We performed simultaneous local electrical recording and wide-field calcium imaging in awake mice. Using recurrent neural network, we demonstrated successful decoding of pixel-level cortical activity using locally recorded surface potentials. These results show that locally recorded surface potentials indeed contain rich information of large-scale neural activities. Chapter 4 presents a biophysical model for coupled network of hippocampal CA1 and prefrontal cortex to study mechanisms of memory trace transfer and reactivation. We found that stored memory traces in cortex can be retrieved through two different mechanisms, namely cell-specific inputs and non-specific spontaneous activities. Our study presents mechanistic explanations for memory transfer and retrieval. Chapter 5 is the conclusion of this dissertation. The future directions of neurotechnologies and computational models for neuroscience research are discussed.
Emerging Cognitive Neuroscience and Related Technologies, from the National Research Council, identifies and explores several specific research areas that have implications for U.S. national security, and should therefore be monitored consistently by the intelligence community. These areas include: neurophysiological advances in detecting and measuring indicators of psychological states and intentions of individuals the development of drugs or technologies that can alter human physical or cognitive abilities advances in real-time brain imaging breakthroughs in high-performance computing and neuronal modeling that could allow researchers to develop systems which mimic functions of the human brain, particularly the ability to organize disparate forms of data. As these fields continue to grow, it will be imperative that the intelligence community be able to identify scientific advances relevant to national security when they occur. To do so will require adequate funding, intelligence analysts with advanced training in science and technology, and increased collaboration with the scientific community, particularly academia. A key tool for the intelligence community, this book will also be a useful resource for the health industry, the military, and others with a vested interest in technologies such as brain imaging and cognitive or physical enhancers.
An anniversary edition of the classic work that influenced a generation of neuroscientists and cognitive neuroscientists. Before The Computational Brain was published in 1992, conceptual frameworks for brain function were based on the behavior of single neurons, applied globally. In The Computational Brain, Patricia Churchland and Terrence Sejnowski developed a different conceptual framework, based on large populations of neurons. They did this by showing that patterns of activities among the units in trained artificial neural network models had properties that resembled those recorded from populations of neurons recorded one at a time. It is one of the first books to bring together computational concepts and behavioral data within a neurobiological framework. Aimed at a broad audience of neuroscientists, computer scientists, cognitive scientists, and philosophers, The Computational Brain is written for both expert and novice. This anniversary edition offers a new preface by the authors that puts the book in the context of current research. This approach influenced a generation of researchers. Even today, when neuroscientists can routinely record from hundreds of neurons using optics rather than electricity, and the 2013 White House BRAIN initiative heralded a new era in innovative neurotechnologies, the main message of The Computational Brain is still relevant.
This is a student text, introducing the scope and problems of a new scientific discipline - Computational Neurogenetic Modeling (CNGM). CNGM is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This new area brings together knowledge from various scientific disciplines.
One of the most exciting and potentially rewarding areas of scientific research is the study of the principles and mechanisms underlying brain function. It is also of great promise to future generations of computers. A growing group of researchers, adapting knowledge and techniques from a wide range of scientific disciplines, have made substantial progress understanding memory, the learning process, and self organization by studying the properties of models of neural networks - idealized systems containing very large numbers of connected neurons, whose interactions give rise to the special qualities of the brain. This book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. It is written at a level accessible to the wide range of researchers working on these problems - statistical physicists, biologists, computer scientists, computer technologists and cognitive psychologists. The author presents a coherent and clear nonmechanical presentation of all the basic ideas and results. More technical aspects are restricted, wherever possible, to special sections and appendices in each chapter. The book is suitable as a text for graduate courses in physics, electrical engineering, computer science and biology.
The book "Cognitive and Computational Neuroscience - Principles, Algorithms and Applications" will answer the following question and statements: System-level neural modeling: what and why? We know a lot about the brain! Need to integrate data: molecular/cellular/system levels. Complexity: need to abstract away higher-order principles. Models are tools to develop explicit theories, constrained by multiple levels (neural and behavioral). Key: models (should) make novel testable predictions on both neural and behavioral levels. Models are useful tools for guiding experiments. The hope is that the information provided in this book will trigger new researches that will help to connect basic neuroscience to clinical medicine.
The essence of brain function consists in how information is processed, transferred and stored. Current neurophysiological doctrine remains focused within a spike timing paradigm, but this has a limited capacity for advancing the understanding of how the brain works. This book puts forward a new model; the neuroelectrodynamic model (NED), which describes the intrinsic computational processes by the dynamics and interaction of charges. It uses established laws of physics, such as those of classical mechanics, thermodynamics and quantum physics, as the guiding principle to develop a general theoretical construct of the brain s computational model, which incorporates the neurobiology of the cells and the molecular machinery itself, along with the electrical activity in neurons, to explain experimental results and predict the organization of the system. After addressing the deficiencies of current approaches, the laws and principles required to build a new model are discussed. In addition, as well as describing experiments which provide the required link between computation and semantics, the book highlights important concepts relating the theory of information with computation and the electrical properties of neurons. The NED model is explained and expounded and several examples of its application are shown. Of interest to all those involved in the fields of neuroscience, neurophysiology, computer science and the development of artificial intelligence, NED is a step forward in understanding the mind in computational terms. IOS Press is an international science, technical and medical publisher of high-quality books for academics, scientists, and professionals in all fields. Some of the areas we publish in: -Biomedicine -Oncology -Artificial intelligence -Databases and information systems -Maritime engineering -Nanotechnology -Geoengineering -All aspects of physics -E-governance -E-commerce -The knowledge economy -Urban studies -Arms control -Understanding and responding to terrorism -Medical informatics -Computer Sciences
This is a student text, introducing the scope and problems of a new scientific discipline - Computational Neurogenetic Modeling (CNGM). CNGM is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This new area brings together knowledge from various scientific disciplines.
Computational neuroscience is a relatively new but rapidly expanding area of research which is becoming increasingly influential in shaping the way scientists think about the brain. Computational approaches have been applied at all levels of analysis, from detailed models of single-channel function, transmembrane currents, single-cell electrical activity, and neural signaling to broad theories of sensory perception, memory, and cognition. This book provides a snapshot of this exciting new field by bringing together chapters on a diversity of topics from some of its most important contributors. This includes chapters on neural coding in single cells, in small networks, and across the entire cerebral cortex, visual processing from the retina to object recognition, neural processing of auditory, vestibular, and electromagnetic stimuli, pattern generation, voluntary movement and posture, motor learning, decision-making and cognition, and algorithms for pattern recognition. Each chapter provides a bridge between a body of data on neural function and a mathematical approach used to interpret and explain that data. These contributions demonstrate how computational approaches have become an essential tool which is integral in many aspects of brain science, from the interpretation of data to the design of new experiments, and to the growth of our understanding of neural function. . Includes contributions by some of the most influential people in the field of computational neuroscience . Demonstrates how computational approaches are being used today to interpret experimental data . Covers a wide range of topics from single neurons, to neural systems, to abstract models of learning