The cerebral cortex is well known for its complexity. At least 20 different cortical cell types can be classified with ease, and by many accounts this would represent a conservative estimate. Recent studies have used gene expression to identify cell types within the cortex and revealed a high degree of cell diversity (Sugino et al., 2006). It is not the purpose of this chapter to describe every nuance of cell type exhaustively, and indeed there are many excellent accounts elsewhere (Peters and Jones, 1984; Markram et al., 2004; Sugino et al., 2006). Instead, a brief summary of the salient features is given in the following sections as a prelude to describing their synaptic properties and some of the general features of cortical circuitry to emerge from studies in recent years. Neurons in the cortex are almost entirely glutamatergic and thus excitatory, or GABAergic and thus inhibitory (Section 3.3). The following two sections look at the excitatory and inhibitory cell types.

Excitatory cells

There are three main types of excitatory cell in the cortex: the spiny stellate, the star pyramid and the pyramidal cell. The star pyramid is intermediate in form between the stellate and pyramidal cell. The main features of the three cell types are described below, beginning with the spiny stellate and star pyramidal cells and ending with the diversity of pyramidal cell types in individual cortical layers.

Spiny stellate cells

Spiny stellate cells are characterized by, and indeed named after, their star-shaped dendritic pattern.

This chapter describes the formation of the barrel cortex from the birth of the cells through to maturation of synaptic circuits. Special consideration is given to formation of the distinctive somatotopic pattern in the barrel field that has captured the imagination of so many scientists over the years. Pattern formation is, of course, a field within developmental science in its own right, of which formation of the somatotopic pattern in the barrel field is an interesting example. Somatotopic pattern formation per se is, therefore, treated in a section on its own. Barrel formation, however, does not raise the same issues and could just as easily be concerned with the formation of a single barrel as with a pattern of barrels. For these reasons, pattern formation and barrel formation are treated in separate sections. Of course the cellular aggregates composing the barrels themselves also make a pattern (Chapter 1), but as we shall see the pattern is present in the thalamocortical afferents before they reach layer IV where the barrels form, and in some circumstances the pattern itself can form in the thalamocortical afferents without the barrels forming. Therefore, the sections on pattern formation concentrate on the origin of the pattern itself, including the role of the peripheral innervation (Section 4.2), while the sections on barrel formation concentrate on the behavior of the cellular aggregates that compose the walls of the barrels (Section 4.3).

This chapter differs from those preceding it in that it does not treat a single theme but several. Rather than concentrate on one particular field of barrel cortex research, this chapter explores research in several new and emerging fields. Each field is characterized by a strong continuing development of methodology. In each case, barrel cortex is either the central focus of the research or is particularly well suited to help to further research in an allied field. The area of cortical blood flow and stroke research is a good example of the latter category. Many of the common cortical strokes that lead to paralysis are caused by occlusions that involve or affect the middle cerebral artery, which supplies the somatosensory cortex in general and the barrel cortex in rats and mice. Stroke research is in many ways ideally suited to study in barrel cortex because the cortical tissue affected by the stroke can be readily defined from the barrel field histology and the clinically relevant artery can be occluded to observe its effect on barrel cortex blood flow, angiogenesis, cell death and recovery of function.

Arguably some of the topics treated in this section warrant chapters of their own or could add considerably to the previous chapters in this book. The reason they have been grouped together in one chapter stems from two main factors.

The field of barrel cortex research has grown rapidly over the past few years. Today, studies are directed not only at understanding the barrel cortex itself but also at understanding issues in related fields using the barrel cortex as a model system. In the three years it has taken to write this book, over 300 papers have been published on barrel cortex. While this rising tide of information has made writing a challenge, the fundamental studies of the preceding 34 years have provided a solid foundation and context in which to place the new work. Fortunately for me, the story has been enhanced by research in recent years and not entirely rewritten by it.

One of the reasons for writing this book has been the realization that barrel cortex research has matured to a point where a survey and a summary has become possible. The field has been characterized by classic studies that illuminate this and other areas of neuroscience and by a constant innovation in techniques and ideas. In fact, the barrel cortex has served as a test-bed system for several new methodologies, partly because of its unique and instantly identifiable form, and partly because the species that have barrels, the rodents, are the most commonly used laboratory mammal. The classic studies on the basic anatomy and physiology of this cortical area have certainly facilitated subsequent studies on barrel cortex. Two fundamental innovations have driven the field further.

Introduction

The barrel cortex is a remarkable structure. Its form has captured the imagination of researchers for decades and its versatility has ensured that it finds a place in each new wave of neuroscience research. Since its discovery by Woolsey and Van der Loos in the early 1970s, researchers have used barrel cortex to study some of the most pressing questions in neuroscience. How does the cortex develop? How does active touch work? What makes neurons plastic? In each case, the value of the barrel cortex has been to help neuroscientists to relate structure with function through its unique and easily defined form.

In order to understand how these questions are being addressed currently, it is useful to understand some of the basic structural and functional features of the barrel cortex. The first three chapters of this book address some of the fundamental anatomy and physiology of the barrel cortex. For the expert in the field, most of what is written in these chapters will probably be quite familiar but will hopefully still serve as a useful reference to the original studies. While most of the original anatomical studies span the 1970s and 1980s, new neuroanatomical findings are still being described into the current century. Curiously, a review of this anatomical literature has not previously been written. For those less familiar with barrel cortex, the anatomical pathways linking the periphery to barrel cortex are described in Chapter 2 along with the intracortical connections, the study of which, at the time of writing, is still an active area of research.

Plasticity is an important topic in neuroscience, both from a philosophical and a practical viewpoint. Plasticity is involved in development, learning and memory and in shaping the nervous system's response to injury and disease. With regard to development, some topics have been covered in Chapter 4, but here we concentrate in more detail on the mechanisms of plasticity rather than their developmental sequence and consequences for development. On the question of learning and memory, in Section 7.2 we look at the extent to which synaptic plasticity mechanisms are activated by changes in sensory experience. This issue relates in a general way to the means by which experience creates memories. Sensory experience can induce lasting memories and memory is thought to depend on synaptic plasticity. Therefore, understanding how sensory experience induces synaptic plasticity is germane to understanding learning and memory. In both cases, experience changes synaptic function. On the question of the nervous system's response to injury, we treat this topic separately from the general treatment of experience-dependent plasticity because even though it may involve components of the latter it certainly involves other factors too (Section 7.5). In many ways, this may be the more urgent category of plasticity to tackle because a full understanding of injury-induced plasticity may lead to therapies for the consequences of damage to peripheral or central structures.

The early studies on somatosensory cortical plasticity were concerned with the cortical response to peripheral injury.

Introduction

In everyday life, the scenes we view are typically cluttered with many different objects. However, the capacity of the visual system to process information about multiple objects at any given moment in time is limited (Broadbent, 1958; Neisser, 1967; Schneider & Shiffrin, 1977; Tsotsos, 1990). This limited processing capacity can be exemplified in a simple experiment. If subjects are presented with two different objects and asked to identify two different attributes at the same time (e.g., color of one and orientation of the other), the subjects' performance is worse than if the task had been performed with only a single object (Duncan, 1980, 1984; Treisman, 1969). Hence, multiple objects present at the same time in the visual field compete for neural representation due to limited processing resources.

How can the competition among multiple objects be resolved? One way is by bottom-up, stimulus-driven processes. For example, in Figure 4.1A, the vertical line among the multiple distracter lines is effortlessly and quickly detected because of its salience in the display, which biases the competition in favor of the vertical line. Stimulus salience depends on various factors, including simple feature properties such as line orientation or color of the stimulus (Treisman & Gelade, 1980; Treisman & Gormican, 1988), perceptual grouping of stimulus features by Gestalt principles (Driver & Baylis, 1989; Duncan, 1984; Lavie & Driver, 1996; Prinzmetal, 1981), and the dissimilarity between the stimulus and nearby distracter stimuli (Duncan & Humphreys, 1989, 1992; Nothdurft, 1993).

Introduction

The hair cell is the mechanosensory cell of the auditory and vestibular organs of the inner ear. Sounds and head movements deflect the hair cells' apical bundles of specialized microvilli, inducing current flow through mechanosensitive ion channels in the bundles. The resulting change in membrane potential – the receptor potential – in turn modulates diverse voltage-gated ion channels in the basolateral membrane. The most numerous channels are potassium (K+)- selective and may be voltage- and/or calcium (Ca2 +)- activated. Flow of current through K+ channels tends to drive the hair cell towards its resting potential, providing negative feedback on the transduction current or, in some cases, amplifying the voltage response through electrical resonance. The properties of hair cell K+ channels have been the focus of many studies, in part because of the opportunity to link diversity in the repertoire of K+ channels to sensory signaling (Fettiplace & Fuchs, 1999). The voltage-gated Ca2 + channels of hair cells have been investigated because of their functional significance as activators of K+ current (e.g., Art & Fettiplace, 1987; Hudspeth & Lewis, 1988) and/or mediators of chemical transmission (e.g., Beutner et al., 2001; Engel et al., 2002; Parsons et al., 1994).

The voltage-gated sodium (Na+) currents of hair cells have only recently attracted significant attention. Because they have fast inactivation kinetics and frequently very negative voltage ranges of inactivation, hair cell Na+ currents can be negligible during standard voltage protocols, so that their distribution is not fully characterized.

Introduction

Classical fear conditioning is a form of associative learning in which subjects are trained to express fear responses to a neutral conditioned stimulus (CS) that is paired with an aversive unconditioned stimulus (US). As a result of such pairing, the CS comes to elicit behavioral, autonomic, and endocrine responses that are characteristically expressed in the presence of danger (Blanchard & Blanchard, 1969; Bolles & Fanselow, 1980; Smith et al., 1980). Fear conditioning has emerged as an especially useful behavioral model for investigating the neurobiological mechanisms of learning and memory, because fear memories are rapidly acquired and long-lasting, involve well-defined stimuli and responses, and depend upon similar neural circuits in different vertebrate species (see Davis & Lee, 1998; LeDoux, 2000; Maren, 1999; Rogan et al., 2001).

In this chapter, we review studies that have investigated the role of the amygdala in fear learning. We argue that neural plasticity in the lateral amygdala is critical for storing memories of the association between the CS and US during fear conditioning, and discuss how learning and memory are achieved at the cellular or molecular level. Alternative views of amygdala contributions to fear conditioning are also considered.

The amygdala and fear conditioning

Fear learning depends critically upon the amygdala (Davis & Shi, 2000; Fendt & Fanselow, 1999; LeDoux, 1996, 2000), a cluster of nuclei in the brain's temporal lobe that plays a key role in regulating emotions (Kluver & Bucy, 1939; LeDoux, 1996).

Introduction

Linguists, psychologists, and neuroscientists have studied language acquisition with the tools and models available to their respective fields. Linguists elaborated some of the most sophisticated theories to account for how this unique human competence arises in the infants' brains. Chomsky (1980) formulated the parameter setting theory (hereafter, PS) to account for how infants, on the basis of partial and noisy language input, acquire grammar. PS assumes that infants are born with “knowledge” of Universal Grammar (UG). This includes both genetically determined universal principles and binary parameters. Universal principles describe the properties common to all natural languages. Binary parameters capture the grammatical properties on which natural languages differ from one another. The linguistic input determines the particular value of a parameter. PS postulates that exposure to the surrounding language determines how the parameters of UG are set.

We acknowledge that PS has many virtues. It addresses the problem of language acquisition without making unjustified but common simplifications, for example, that imitation is the privileged mechanism responsible for the emergence of linguistic competence. The theory, furthermore, is quite appealing because it assumes, realistically, a biological perspective, namely, that the child is equipped with a species-specific mechanism to acquire natural language. Moreover, the PS theory has been formulated with sufficient detail and precision as to make it easy to falsify. In contrast, proposals that assume that language is acquired by means of a general learning device appear more difficult to support.

There are periods in development during which experience plays its largest role in shaping the eventual structure and function of mature language-processing systems. These spans of peak cortical plasticity have been called “sensitive periods.” Here, we describe a series of studies investigating the effects of delays in second language (L2) acquisition on different subsystems within language. First, we review the effects of the altered language experience of congenitally deaf subjects on cerebral systems important for processing written English and American Sign Language (ASL). Second, we present behavioral and electrophysiological studies of L2 semantic and syntactic processing in Chinese-English bilinguals who acquired their second language over a wide range of ages. Third, we review semantic, syntactic, and prosodic processing in native Spanish and native Japanese late-learners of English. These approaches have provided converging evidence, indicating that delays in language acquisition have minimal effects on some aspects of semantic processing. In contrast, delays of even a few years result in deficits in some types of syntactic processing and differences in the organization of cortical systems used to process syntactic information. The different subsystems of language which rely on different cortical areas, including semantic, syntactic, phonological, and prosodic processing, may have different developmental time courses that in part determine the different sensitive period effects observed.

Humans, in comparison to other animals, go through a protracted period of post-natal development that lasts at least 15 years (Chugani & Phelps, 1986; Huttenlocher, 1990).