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Principles of Computational Modelling in Neuroscience
Learn to use computational modelling techniques to understand the nervous system at all levels, from ion channels to networks.
Principles of Computational Modelling in Neuroscience
Providing a step-by-step and practical account of how to model neurons and neural circuitry, this textbook is designed for advanced undergraduate and postgraduate students of computational neuroscience as well as for researchers in neuroscience and related sciences wishing to apply computational approaches to interpret data and make predictions.
The Basal Ganglia VII
This volume, The Basal Ganglia VII, is derived from the proceedings ofthe Seventh Triennial Meeting of the International Basal Ganglia Society (IBAGS). The Meeting was held from II - 15 February 2001 at The Copthorne Resort, Waitangi, Bay of Islands, New Zealand, the site of the signing of the Treaty ofWaitangi in 1840 and the traditional birth-place of the New Zealand Nation. As at previous Meetings, our aim was to hear and discuss new ideas and research developments on the basal ganglia and the implications of these findings for novel treatment strategies for basal ganglia disorders. The International Basal Ganglia Society (IBAGS) was founded in September 1983 when a small group of about 50 neuroscientists and clinicians with a passion for research on the basal ganglia met for a three day meeting in a small isolated seaside resort, Lome, 150km from Melbourne in Australia. The meeting was organised by John McKenzie and was so successful that the participants decided to establish IBAGS and to meet every 3 years at an isolated seaside resort in different countries of the world.
Advances in Neural Information Processing Systems 7
November 28-December 1, 1994, Denver, Colorado NIPS is the longest running annual meeting devoted to Neural Information Processing Systems. Drawing on such disparate domains as neuroscience, cognitive science, computer science, statistics, mathematics, engineering, and theoretical physics, the papers collected in the proceedings of NIPS7 reflect the enduring scientific and practical merit of a broad-based, inclusive approach to neural information processing. The primary focus remains the study of a wide variety of learning algorithms and architectures, for both supervised and unsupervised learning. The 139 contributions are divided into eight parts: Cognitive Science, Neuroscience, Learning ...
Emergent Neural Computational Architectures Based on Neuroscience
It is generally understood that the present approachs to computing do not have the performance, flexibility, and reliability of biological information processing systems. Although there is a comprehensive body of knowledge regarding how information processing occurs in the brain and central nervous system this has had little impact on mainstream computing so far. This book presents a broad spectrum of current research into biologically inspired computational systems and thus contributes towards developing new computational approaches based on neuroscience. The 39 revised full papers by leading researchers were carefully selected and reviewed for inclusion in this anthology. Besides an introductory overview by the volume editors, the book offers topical parts on modular organization and robustness, timing and synchronization, and learning and memory storage.
Computational Neuroscience
This volume includes papers originally presented at the 7th annual Computational Neuroscience Meeting (CNS'98) held in July of 1998 at the Fess Parker Doubletree Inn in Santa Barbara, California. The CNS meetings bring together computational neuroscientists representing many different fields and backgrounds as well as many different experimental preparations and theoretical approaches. The papers published here range from pure experimental neurobiology, to neuro-ethology, mathematics, physics, and engineering. In all cases the research described is focused on understanding how nervous systems compute. The actual subjects of the research include a highly diverse number of preparations, modeling approaches, and analysis techniques. Accordingly, this volume reflects the breadth and depth of current research in computational neuroscience taking place throughout the world.
Computational Neuroscience: Trends in Research 2004
The CNS meetings bring together computational neuroscientists representing many different fields and backgrounds as well as many different experimental preparations and theoretical approaches. The papers published here range from pure experimental neurobiology, to neuro-ethology, mathematics, physics, and engineering. In all cases the research described is focused on understanding how nervous systems compute. The actual subjects of the research include a highly diverse number of preparations, modeling approaches and analysis techniques. Accordingly, this volume reflects the breadth and depth of current research in computational neuroscience taking place throughout the world.
Brain Leitmotifs
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From the Retina to the Neocortex
David Courtnay Marr was born on January 19, 1945 in Essex, England. He went to the English public school, Rugby, on scholarship and between 1963 and 1966 studied mathematics at Trinity College, Cambridge University where he obtained his B.S. and M.S. degrees. Rather than pursue a Ph. D. in math ematics he preferred to switch to neurophysiology under Giles Brindley. His education involved training in neuroanatomy, neurophysiology, biochemistry, and molecular biology. Marr's Ph. D. work resulted in a theory of the cerebel lar cortex, the essence of which became "A Theory of the Cerebellar Cortex," reproduced in Chapter 1 of this volume with a commentary by Thomas Thach. He wrote a short paper ...
Vision-Based Robot Navigation
Starting with a summary of the history of Artificial Intelligence, this book makes the bridge to the modern debate on the definition of Intelligence and the path to building Intelligent Machines. Since the definition of Intelligence is itself subject to open debate, the quest for Intelligent machines is pursuing a moving target. Apparently, intelligent behaviour is, to a great extent, the result of using a sophisticated associative memory, more than the result of heavy processing. The book describes theories on how the brain works, associative memory models and how a particular model - the Sparse Distributed Memory (SDM) - can be used to navigate a robot based on visual memories. Other robot navigation methods are also comprehensively revised and compared to the method proposed. The performance of the SDM-based robot has been tested in different typical problems, such as illumination changes, occlusions and image noise, taking the SDM to the limits. The results are extensively discussed in the book.
