Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

A significant amount of effort in neural modeling is directed towards understanding the representation of external objects in the brain. There is also a rapidly growing interest in modeling the intrinsically-generated activity in the brain, as represented by the default mode network hypothesis, and the emergent behavior that gives rise to critical phenomena such as neural avalanches. Time plays a critical role in these intended modeling domains, from the exquisite discriminations in the mammalian auditory system to the precise timing involved in high-end activities such as competitive sports or professional music performance. The growth in experimental high-throughput neuroscience techniques has allowed the multi-scale acquisition of neural signals, from individual electrode recordings to whole-brain functional magnetic resonance imaging activity, including the ability to manipulate neural signals with optogenetic approaches. This has created a deluge of experimental data, spanning multiple spatial and temporal scales, and posing the enormous challenge of its interpretation in terms of a predictive theory of brain function. In addition, there has been a massive growth in availability of computational power through parallel computing. The Relevance of the Time Domain to Neural Network Models aims to develop a unified view of how the time domain can be effectively employed in neural network models. The book proposes that conceptual models of neural interaction are required in order to understand the data being collected. Simultaneously, these proposed models can be used to form hypotheses of neural interaction and system behavior that can be neuroscientifically tested. The book concentrates on a crucial aspect of brain modeling: the nature and functional relevance of temporal interactions in neural systems. This book will appeal to a wide audience consisting of computer scientists and electrical engineers interested in brain-like computational mechanisms, computer architects exploring the development of high-performance computing systems to support these computations, neuroscientists probing the neural code and signaling mechanisms, mathematicians and physicists interested in modeling complex biological phenomena, and graduate students in all these disciplines who are searching for challenging research questions.

Neural networks (Neurobiology). --- Neurobiology -- Computer simulation. --- Neuroplasticity. --- Neural networks (Neurobiology) --- Neural networks (Computer science) --- Brain --- Time-domain analysis --- Mathematical Concepts --- Time --- Artificial Intelligence --- Computing Methodologies --- Pattern Recognition, Automated --- Information Science --- Physical Phenomena --- Phenomena and Processes --- Neural Networks (Computer) --- Computer Systems --- Time Factors --- Human Anatomy & Physiology --- Engineering & Applied Sciences --- Medicine --- Health & Biological Sciences --- Computer Science --- Neurology --- Neuroscience --- Brain. --- Time-domain analysis. --- Analysis, Time-domain --- Cerebrum --- Mind --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Biological neural networks --- Nets, Neural (Neurobiology) --- Networks, Neural (Neurobiology) --- Neural nets (Neurobiology) --- Medicine. --- Neurosciences. --- Computers. --- Artificial intelligence. --- Biomedicine. --- Computation by Abstract Devices. --- Artificial Intelligence (incl. Robotics). --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Calculators --- Cyberspace --- Neural sciences --- Neurological sciences --- Medical sciences --- Nervous system --- Clinical sciences --- Medical profession --- Human biology --- Life sciences --- Pathology --- Physicians --- System analysis --- Central nervous system --- Head --- Artificial intelligence --- Natural computation --- Soft computing --- Cognitive neuroscience --- Neurobiology --- Neural circuitry --- Computer science. --- Artificial Intelligence. --- Informatics --- Science

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

A significant amount of effort in neural modeling is directed towards understanding the representation of external objects in the brain. There is also a rapidly growing interest in modeling the intrinsically-generated activity in the brain, as represented by the default mode network hypothesis, and the emergent behavior that gives rise to critical phenomena such as neural avalanches. Time plays a critical role in these intended modeling domains, from the exquisite discriminations in the mammalian auditory system to the precise timing involved in high-end activities such as competitive sports or professional music performance. The growth in experimental high-throughput neuroscience techniques has allowed the multi-scale acquisition of neural signals, from individual electrode recordings to whole-brain functional magnetic resonance imaging activity, including the ability to manipulate neural signals with optogenetic approaches. This has created a deluge of experimental data, spanning multiple spatial and temporal scales, and posing the enormous challenge of its interpretation in terms of a predictive theory of brain function. In addition, there has been a massive growth in availability of computational power through parallel computing. The Relevance of the Time Domain to Neural Network Models aims to develop a unified view of how the time domain can be effectively employed in neural network models. The book proposes that conceptual models of neural interaction are required in order to understand the data being collected. Simultaneously, these proposed models can be used to form hypotheses of neural interaction and system behavior that can be neuroscientifically tested. The book concentrates on a crucial aspect of brain modeling: the nature and functional relevance of temporal interactions in neural systems. This book will appeal to a wide audience consisting of computer scientists and electrical engineers interested in brain-like computational mechanisms, computer architects exploring the development of high-performance computing systems to support these computations, neuroscientists probing the neural code and signaling mechanisms, mathematicians and physicists interested in modeling complex biological phenomena, and graduate students in all these disciplines who are searching for challenging research questions.

Neuropathology --- Computer science --- Artificial intelligence. Robotics. Simulation. Graphics --- neurologie --- informatica --- robots

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book constitutes the revised selected papers from the 4th International Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2014, held in Montreal, QC, Canada, in December 2014 as a satellite event of the 11th annual conference on Neural Information Processing Systems, NIPS 2014. The 10 MLINI 2014 papers presented in this volume were carefully reviewed and selected from 17 submissions. They were organized in topical sections named: networks and decoding; speech; clinics and cognition; and causality and time-series. In addition, the book contains the 3 best papers presented at MLINI 2013.

Computer science. --- Mathematical statistics. --- Data mining. --- Artificial intelligence. --- Image processing. --- Pattern recognition. --- Computer Science. --- Pattern Recognition. --- Image Processing and Computer Vision. --- Artificial Intelligence (incl. Robotics). --- Information Systems Applications (incl. Internet). --- Probability and Statistics in Computer Science. --- Data Mining and Knowledge Discovery. --- Design perception --- Pattern recognition --- Pictorial data processing --- Picture processing --- Processing, Image --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Informatics --- Statistical methods --- Form perception --- Perception --- Figure-ground perception --- Imaging systems --- Optical data processing --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Database searching --- Statistics --- Probabilities --- Sampling (Statistics) --- Science --- Optical pattern recognition. --- Computer vision. --- Artificial Intelligence. --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Pattern perception --- Perceptrons --- Visual discrimination --- Machine learning --- Optical data processing. --- Application software. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Optical computing --- Visual data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment --- Pattern recognition systems. --- Computer science --- Automated Pattern Recognition. --- Computer Vision. --- Computer and Information Systems Applications. --- Mathematics. --- Computer mathematics --- Pattern classification systems --- Pattern recognition computers --- Computer vision