Neuro-informatics and Neural Modelling


Book Description

How do sensory neurons transmit information about environmental stimuli to the central nervous system? How do networks of neurons in the CNS decode that information, thus leading to perception and consciousness? These questions are among the oldest in neuroscience. Quite recently, new approaches to exploration of these questions have arisen, often from interdisciplinary approaches combining traditional computational neuroscience with dynamical systems theory, including nonlinear dynamics and stochastic processes. In this volume in two sections a selection of contributions about these topics from a collection of well-known authors is presented. One section focuses on computational aspects from single neurons to networks with a major emphasis on the latter. The second section highlights some insights that have recently developed out of the nonlinear systems approach.




Principles of Computational Modelling in Neuroscience


Book Description

Learn to use computational modelling techniques to understand the nervous system at all levels, from ion channels to networks.




Artificial Neural Networks


Book Description

The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new algorithms for prototype selection, and group structure discovering. Moreover, the book discusses one-class support vector machines for pattern recognition, handwritten digit recognition, time series forecasting and classification, and anomaly identification in data analytics and automated data analysis. By presenting the state-of-the-art and discussing the current challenges in the fields of artificial neural networks, bioinformatics and neuroinformatics, the book is intended to promote the implementation of new methods and improvement of existing ones, and to support advanced students, researchers and professionals in their daily efforts to identify, understand and solve a number of open questions in these fields.




Fundamentals of Computational Neuroscience


Book Description

The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the first edition. Completely redesigned and revised, it introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain.







Python in Neuroscience


Book Description

Python is rapidly becoming the de facto standard language for systems integration. Python has a large user and developer-base external to theneuroscience community, and a vast module library that facilitates rapid and maintainable development of complex and intricate systems. In this Research Topic, we highlight recent efforts to develop Python modules for the domain of neuroscience software and neuroinformatics: - simulators and simulator interfaces - data collection and analysis - sharing, re-use, storage and databasing of models and data - stimulus generation - parameter search and optimization - visualization - VLSI hardware interfacing. Moreover, we seek to provide a representative overview of existing mature Python modules for neuroscience and neuroinformatics, to demonstrate a critical mass and show that Python is an appropriate choice of interpreter interface for future neuroscience software development.




Data-Driven Computational Neuroscience


Book Description

Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.




Neuroinformatics for Neuropsychology


Book Description

Bioinformatics involves specialized application of computer technology to investigative and conceptual problems in biology and medicine; neuroinformatics (NI) is the practice of bioinformatics in the neurosciences. Over the past two decades the biomedical sciences have been revolutionized by databases, data mining and data modeling techniques. The Human Genome Project, which depended on informatics methods, has been the most well recognized bioinformatics undertaking. Bioinformatics has since been applied all across biology and medicine, and has also transformed almost every avenue in neuroscience. Yet in neuropsychology, NI perspectives remain largely unrealized. Ironically, NI offers enormous potential to the essential praxis of neuropsychology - assessing cognitive behavior and relating cognition to neural systems. Neuroinformatics can be applied to neuropsychology as richly as it has been applied across the neurosciences. Neuroinformatics for Neuropsychology is the first book to explain the relevance and value of NI to neuropsychology. It systematically describes NI tools, applications and models that can enhance the efforts of neuropsychologists. It also describes the implications of NI for neuropsychology in the 21st century – fundamental shifts away from the conventional modes of research, practice and communication that have thus far characterized the field. One of the foremost experts on the subject: Illustrates the vital role NI is playing throughout the neurosciences. Provides a sampling of NI tools and applications in neuroscience research, and lays out current organization structures that support NI. Describes the lack of NI in neuropsychology, differentiates between NI systems for neuropsychology and conventional computerized assessment methods, and proposes criteria for neuropsychology-specific NI systems. Describes NI applications and models currently in use in neuropsychology, and NI models for neuropsychology that are being pioneered in phenomics research. Discusses potential obstacles and aids to NI in neuropsychology, including issues such as data sharing, standardization of methods, and data ontology. Projects the future of neuropsychological research and practice in light of the new generation of the internet, Web 2.0, geared to collective knowledge building. A vital introduction to a profound technological practice, Neuroinformatics for Neuropsychology is important reading for clinical neuropsychologists, cognitive neuroscientists, behavioral neurologists, and speech-language pathologists. Researchers, clinicians, and graduate students interested in informatics for the brain-behavioral sciences will especially welcome this unique volume.




Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence


Book Description

Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. The book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.




Cognitive Informatics, Computer Modelling, and Cognitive Science


Book Description

Cognitive Informatics, Computer Modelling, and Cognitive Science: Theory, Case Studies, and Applications presents the theoretical background and history of cognitive science to help readers understand its foundations, philosophical and psychological aspects, and applications in a wide range of engineering and computer science case studies. Cognitive science, a cognitive model of the brain, knowledge representation, and information processing in the human brain are discussed, as is the theory of consciousness, neuroscience, intelligence, decision-making, mind and behavior analysis, and the various ways cognitive computing is used for information manipulation, processing and decision-making. Mathematical and computational models, structures and processes of the human brain are also covered, along with advances in machine learning, artificial intelligence, cognitive knowledge base, deep learning, cognitive image processing and suitable data analytics.