Computational Theories And Their Implementation In The Brain

In the late 1960s and early 1970s David Marr produced three astonishing papers in which he gave a detailed account of how the fine structure and known cell types of the cerebellum, hippocampus and neocortex perform the functions that they do. Marr went on to become one of the main founders of Computational Neuroscience. In his classic work 'Vision' he distinguished between the computational, algorithmic, and implementational levels, and the three early theories concerned implementation. However, they were produced when Neuroscience was in its infancy. Now that so much more is known, it is timely to revisit these early theories to see to what extent they are still valid and what needs to be altered to produce viable theories that stand up to current evidence. This book brings together some of the most distinguished scientists in their fields to evaluate Marr's legacy. After a general introduction there are three chapters on the cerebellum, three on the hippocampus and two on the neocortex. The book ends with an appreciation of the life of David Marr by Lucia Vaina.

Written from the physicist’s perspective, this book introduces computational neuroscience with in-depth contributions by system neuroscientists. The authors set forth a conceptual model for complex networks of neurons that incorporates important features of the brain. The computational implementation on supercomputers, discussed in detail, enables you to adapt the algorithm for your own research. Worked-out examples of applications are provided.

Memory and the Computational Brain offers a provocative argument that goes to the heart of neuroscience, proposing that the field can and should benefit from the recent advances of cognitive science and the development of information theory over the course of the last several decades. A provocative argument that impacts across the fields of linguistics, cognitive science, and neuroscience, suggesting new perspectives on learning mechanisms in the brain Proposes that the field of neuroscience can and should benefit from the recent advances of cognitive science and the development of information theory Suggests that the architecture of the brain is structured precisely for learning and for memory, and integrates the concept of an addressable read/write memory mechanism into the foundations of neuroscience Based on lectures in the prestigious Blackwell-Maryland Lectures in Language and Cognition, and now significantly reworked and expanded to make it ideal for students and faculty

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.

Biology undergraduates, medical students and life-science graduate students often have limited mathematical skills. Similarly, physics, math and engineering students have little patience for the detailed facts that make up much of biological knowledge. Teaching computational neuroscience as an integrated discipline requires that both groups be brought forward onto common ground. This book does this by making ancillary material available in an appendix and providing basic explanations without becoming bogged down in unnecessary details. The book will be suitable for undergraduates and beginning graduate students taking a computational neuroscience course and also to anyone with an interest in the uses of the computer in modeling the nervous system.

In this work, Marcin Milkowski argues that the mind can be explained computationally because it is itself computational - whether it engages in mental arithmetic, parses natural language, or processes the auditory signals that allow us to experience music.

This collection brings together a set of new papers that advance the debate concerning the nature of explanation in mind and brain science, and help to clarify the prospects for bonafide integration across these fields. Long a topic of debate among philosophers and scientists alike, there is growing appreciation that understanding the complex relationship between the psychological sciences and the neurosciences, especially how their respective explanatory frameworks interrelate, is of fundamental importance for achieving progress across these scientific domains. Traditional philosophical discussions tend to construe the relationship between them in stark terms - either they are related in terms of complete independence (i.e., autonomy) or complete dependence (i.e., reduction), leaving little room for more interesting relations such as that of mutually beneficial interaction or integration. A unifying thread across the diverse set of contributions to this volume is the rejection of the assumption that no stable middle ground exists between these two extremes, and common embrace of the idea that these sciences are partially dependent on or constrained by one another. By addressing whether the explanatory patterns employed across these domains are similar or different in kind, and to what extent they inform and constrain each another, this volume helps to deepen our understanding of the prospects for successfully integrating mind and brain science.

Hardbound. The present collection of papers focuses on the subject of vision. The papers bring together new insights and facts from various branches of experimental and theoretical neuroscience. The experimental facts presented in the volume stem from disparate fields, such as neuroanatomy, electrophysiology, optical imaging and psychophysics. The theoretical models in part are unsophisticated, yet still inspiring, while others skilfully apply advanced mathematical reasoning to results of experimental measurements. The book is the fifth in a series of volumes intending to define a theory of the brain by bringing together formal reasoning and experimental facts. The reader is thus being introduced to a new kind of brain science, where facts and theory are beginning to blend together.