I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

http://apsychoserver.psychofizz.psych.arizona.edu/JJBAReprints/PSYC501A/Readings/Polich_2007_Clin_Neurophys_Review_P3s.pdf

Updating P300: An Integrative Theory of P3a and P3b

Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this "hierarchical prediction machine" approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.

/pdf/whatever-next-predictive-brains-situated-agents-and-the-epj56cs868.pdf

Whatever next? Predictive brains, situated agents, and the future of cognitive science

中枢神経系疾患の治療は正常細胞（ニューロン）の機能維持を目的とするが，脳血管障害のように機能障害の原因が細胞の死滅に基づくことは多い．一方，脳腫瘍の治療においては薬物療法や放射線療法といった腫瘍細胞の死滅を目標とするものが大きな位置を占める．いずれの場合にも，細胞死の機序を理解することは各種病態や治療法の理解のうえで重要である．現在のところ最も研究の進んでいる細胞死の型はアポトーシスである．そのなかで重要な位置を占めるミトコンドリアにおける反応および抗アポトーシス因子について概要を紹介する．

Neuroscience 細胞死：最近の知見

Thank you for downloading an introduction to the event related potential technique. Maybe you have knowledge that, people have look hundreds times for their favorite readings like this an introduction to the event related potential technique, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious bugs inside their laptop.

An Introduction To The Event Related Potential Technique

In order to embark on the study of so complex organ as the brain, we select, observe, and measure one of the various forms of energy produced and used by the brain—electric, magnetic, chemical, thermal, and metabolic—and a hierarchical level of analysis—microscopic, mesoscopic, and macroscopic—each with its characteristic space-time scales. By choosing to read our book, we infer that readers have chosen to analyze the electroencephalogram from the scalp (EEG), the electrocorticogram from cortical surfaces (ECoG, Fig. 6.1), and the local field potentials from the depth of the brain (LFP) in any or all accessible forms and locations (Lopes da Silva 1993; Basar 1998). Then we characterize and classify the phenomena that we want to analyze and understand. We have begun with time series analysis of single channel recordings; now we undertake the spatial analysis of signals from arrays of channels. To that end, we require some basic concepts that we introduce in this chapter, with references to detailed treatments in other chapters.

Basic Concepts for Spatial Analysis

Single-cell electrical activity during human spontaneous interictal epileptiform discharges and responses to single pulse electrical stimulation (SPES)

In the previous chapter, we mentioned that one of the main limitations of the Fourier transform is that it does not have time resolution. For calculating the Fourier transform, we assume that the signal is stationary and, consequently, that the activity at different frequencies is constant throughout the whole signal. In many occasions, however, signals have time-varying features that cannot be resolved with the Fourier transform. This is the case of music, speech, animal sounds, radar data, and many other signals (see examples in Cohen 1995). For EEG signals, this limitation is critical when we analyze processes that change in time, such as the response to a particular stimulus or the development of an epileptic seizure.

Time-Frequency Analysis

At the pinnacle of the 17th century scientific revolution, Rene Descartes, the father of modern philosophy, published his monumental Meditations on First Philosophy (1), in which he proposed a division between soul and body—mind and brain—with the former in charge of our thoughts and conscious decisions ( res cogitans ) and the latter executing them through mechanical acts ( res extensa ). Scientists nowadays have departed from Cartesian dualism and mainly embrace materialism , the notion that mind and brain are one and the same thing: The mind is not an intangible entity in charge of our thoughts, but, rather, the activity of neurons, and the way neurons connect and form circuits determine our behavior. In the late 1940s, Donald Hebb (2) described a natural mechanism by which the connection between neurons can be changed to encode new experiences, something that was, a few decades later, implemented in the very influential Hopfield neural networks (3), updating the weights between nodes to store different memories. So, it seems that the structural connectivity between neurons, what has been named the “connectome” (4), determines behavior. However, although structural connectivity should clearly constrain brain function, the vast richness of behavior cannot merely be reduced to physical connections, and the “functional connectivity” taking place at a particular time is context and state dependent (5). Furthermore, neuromodulators regulate neural rhythms and should play a key role in modulating functional connectivity (6). Within this framework, the study by Kringelbach et al. (7) in PNAS shows how to integrate the structural anatomical connectivity with the dynamics of neuromodulatory systems into a “dynamic connectome.”

If … 

[↵][1]1Email: rqqg1{at}le.ac.uk.

 [1]: #xref-corresp-1-1

Closing the gap between mind and brain with the dynamic connectome.

In this article I illustrate and briefly describe the main characteristics of concept cells; that means, neurons in the human medial temporal lobe that respond selectively to specific concepts, like a particular person, place or object. Given the converging evidence on the behavior of concept cells and the well-known role of the medial temporal lobe in declarative memory, I argue that concept cells are the building blocks of declarative memory functions.

Rodrigo Quian Quiroga

Papers

Basic Concepts for Spatial Analysis

Single-cell electrical activity during human spontaneous interictal epileptiform discharges and responses to single pulse electrical stimulation (SPES)

Time-Frequency Analysis

Closing the gap between mind and brain with the dynamic connectome.

Concept Cells in the Human Brain