Computational Cognitive Neuroscience
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Computational Explorations in Cognitive Neuroscience
This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the computational cognitive neuroscience. The goal of computational cognitive neuroscience is to understand how the brain embodies the mind by using biologically based computational models comprising networks of neuronlike units. This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the field. The neural units in the simulations use equations based directly on the ion channels that govern the behavior of real neurons, and the neural networks incorporate anatomical and physiological properties of the neocortex. Thus the text provides the student with knowledge of the basic biology of the brain as well as the computational skills needed to simulate large-scale cognitive phenomena. The text consists of two parts. The first part covers basic neural computation mechanisms: individual neurons, neural networks, and learning mechanisms. The second part covers large-scale brain area organization and cognitive phenomena: perception and attention, memory, language, and higher-level cognition. The second part is relatively self-contained and can be used separately for mechanistically oriented cognitive neuroscience courses. Integrated throughout the text are more than forty different simulation models, many of them full-scale research-grade models, with friendly interfaces and accompanying exercises. The simulation software (PDP++, available for all major platforms) and simulations can be downloaded free of charge from the Web. Exercise solutions are available, and the text includes full information on the software.
Emerging Technologies in Computational Cognitive Neuroscience
Computational cognitive neuroscience examines the evolving relationship between brain research and artificial intelligence. It provides a comprehensive understanding of how computer models can simulate and explain complex mental processes. This book examines basic cognitive functions, including vision, attention, memory, language, and decision-making, through the lens of biologically inspired neural networks. The book emphasizes the importance of utilizing neuroscience-based models grounded in evidence to bridge the gap between theory and practice. It also discusses current issues, such as the complexity of models and their relationship to biology, while illustrating how the discipline may influence our understanding of both healthy and unhealthy brains. This book is a valuable resource for anyone seeking to learn more about the science of the mind and the future of intelligent systems. It was written for researchers, students, and professionals.
Models of Visual Attention Using Computational Cognitive Neuroscience in Machine Vision
Author: Manjunath R Kounte
language: en
Publisher: Lulu Publication
Release Date: 2021-04-28
Vision is the most important of the five human senses, since it provides over 85% of the information our brain receives from the external world. Its main goal is to interpret and to interact with the environments we are living in. In everyday life, humans are capable of perceiving thousands of objects, identifying hundreds of faces, recognizing numerous traffic signs, and appreciating beauty effortlessly. The ease with which humans achieve these tasks is in no way due to the simplicity of the tasks but is a proof of the high degree of development of our vision system.