Although science has made considerable progress in discovering the neural basis of cognitive processes, how consciousness arises remains elusive. In this book, Cyriel Pennartz analyzes which aspects of conscious experience can be peeled away to access its core: the hardest aspect, the relationship between brain processes and the subjective, qualitative nature of consciousness. Pennartz traces the problem back to its historical roots in the foundations of neuroscience and connects early ideas on sensory processing to contemporary computational neuroscience. What can we learn from neural network models, and where do they fall short in bridging the gap between neural processes and conscious experience? Do neural models of cognition resemble inanimate systems, and how can this help us define requirements for conscious processing in the brain? These questions underlie Pennartz's examination of the brain's anatomy and neurophysiology. The perspective of his account is t limited to visual perception but broadened to include other sensory modalities and their integration. Formulating a representational theory of the neural basis of consciousness, Pennartz outlines properties that complex structures must express to process information consciously. This theoretical framework is constructed using empirical findings from neuropsychology and neuroscience as well as such theoretical arguments as the Cuneiform Room and the Wall Street Banker. Positing that qualitative experience is a multimodal and multilevel phemen at its very roots, Pennartz places this body of theory in the wider context of mind-brain philosophy, examining implications for our thinking about animal and robot consciousness.

Studies of mechanisms in the brain that allow complicated things to happen in a coordinated fashion have produced some of the most spectacular discoveries in neuroscience. This book provides eloquent support for the idea that spontaneous neuron activity, far from being mere noise, is actually the source of our cognitive abilities. It takes a fresh look at the co-evolution of structure and function in the mammalian brain, illustrating how self-emerged oscillatory timing is the brains fundamental organiser of neuronal information. The small world-like connectivity of the cerebral cortex allows for global computation on multiple spatial and temporal scales. The perpetual interactions among the multiple network oscillators keep cortical systems in a highly sensitive metastable state and provide energy-efficient synchronising mechanisms via weak links. In a sequence of cycles, Gyorgy Buzsaki guides the reader from the physics of oscillations through neuronal assembly organisation to complex cognitive processing and memory storage. His clear, fluid writing accessible to any reader with some scientific knowledge is supplemented by extensive footnotes and references that make it just as gratifying and instructive a read for the specialist. The coherent view of a single author who has been at the forefront of research in this exciting field, this volume is essential reading for anyone interested in our rapidly evolving understanding of the brain.

《神经科学与技术的统计信号处理(导读版)》全面概述了统计信号处理、信息论、机器学习的基本原则、理论和方法，以及它们在神经科学中的应用。作为本领域独一无二的参考书，《神经科学与技术的统计信号处理(导读版)》总结了神经科学中用于解决新兴问题的信号处理、机器学习理论和技术的最新发展，并且特别强调了神经技术的基础和临床应用。《神经科学与技术的统计信号处理(导读版)》是神经工程、神经假体、脑-机接口、计算和系统神经科学、神经信息学、神经生理学等领域的工程研究人员和研究生的理想参考书。

《神经科学中的数学（导读版）》通过Matlab编程语言在众多模拟中的应用来介绍计算方法。这些程序为新的课程和研究提供有益的跳板。作者从介绍微分方程和线性代数在细胞、亚细胞和突起模型的应用开始，然后介绍概率论在突触传递和单细胞噪声中的应用，最后将信号处理理论应用于系统神经科学中。
神经科学依赖众多数学工具表达已有的理论、分析数据并提出新的实验。本书采用一系列扎实的计算模型将该领域最令人瞩目的工具由浅入深地介绍给读者。旨在为神经科学专业的本科生和研究生，以及对神经科学感兴趣的数学、物理和工程背景的学生提供一本教科书，亦可为进行神经科学相关研究的工作者提供有用的参考。

《神经信息学(神经系统的理论和模型)》把半个世纪以来有实验依据的神经系统(脑)中的主要理论和模型集中起来，给研究生们提供理论训练。这些理论和模型中有重要基础理论意义的，大都取材于诺贝尔奖获得者的工作（Hodgkin、Huxley、Hartline、Gabor、Bekesy、Eccles、Crick、Edelmen、Sperry……），也包括对信息科学、工程应用有重大影响的假设、理论和算法（Hebb学习律、平行分布式理论框架……）。
《神经信息学(神经系统的理论和模型)》共分三篇：第一篇包括固定结构的神经系统的理论模型，涵盖神经元模型、感受器的数学描述、节律产生和视觉信息加工等；第二篇是关于学习和记忆的理论模型，包括}tebb学习律、平行分布式理论框架、Hopfield模型以及短时程的突触修正规律，清晰讲述了神经系统的理论研究对人工智能、信息科学工程应用的意义；第三篇介绍神经科学和脑科学中当前的几个热点，包括神经编码、功能柱的结构和功能、脑的非线性和意识问题。
《神经信息学(神经系统的理论和模型)》可作为神经科学、认知科学、心理学等专业的研究生教材，也可作为人工视觉、神经假肢、人工智能、信息科学专业研究人员的参考书。

《脑电信号分析方法及其应用》共7章。第1、2章涉及生理基础和实验基础在内的相关知识。第3章至第5章是方法部分，其中：第3章重点回顾了传统脑电分析方法；第4章侧重于动力学特性的分析，重点介绍了一些新的分析方法，如混沌理论、信息论和复杂度分析等；第5章主要介绍其他重要分析方法，如同步分析和因果性分析。全书的最后两章是实例部分。第6章是脑电分析应用领域的综述，内容涉及临床疾病的辅助诊断、脑电逆问题、认知科学研究中的脑电分析以及脑一机接口。第7章是上述方法(第4、5章为主)的应用实例介绍。脑电信号分析已经在脑科学研究中占据了越来越重要的地位。

Drawing on strange and thought-provoking case studies, an eminent neurologist offers unprecedented insight into the evolution of the uniquely human brain.V. S. Ramachandran is at the forefront of his field-so much so that Richard Dawkins dubbed him the "Marco Polo of neuroscience." Now, in a major new work, Ramachandran sets his sights on the mystery of human uniqueness. Taking us to the frontiers of neurology, he reveals what baffling and extreme case studies can teach us about normal brain function and how it evolved. Synesthesia becomes a window into the brain mechanisms that make some of us more creative than others. And autism—for which Ramachandran opens a new direction for treatment—gives us a glimpse of the aspect of being human that we understand least: self-awareness. Ramachandran tackles the most exciting and controversial topics in neurology with a storyteller's eye for compelling case studies and a researcher's flair for new approaches to age-old questions. Tracing the strange links between neurology and behavior, this book unveils a wealth of clues into the deepest mysteries of the human brain. 15 black-and-white illustrations