Search Results

This part presents four chapters on the concept of coding and representation. The first chapter focuses on the online coding and representation of information by means of neuronal activity. The second argues that the ability of the brain to segregate and integrate information, to make use of population and predictive coding, makes for a system that is specialized for memory. The third discusses the concept of memory trace. The fourth chapter presents a synthesis of the chapters in this part.

. This chapter emphasizes the general principles of brain metabolism and the haemodynamic response to neuronal activity. The precise mechanisms responsible for the links between brain energy metabolism and brain work are not well defined. The chapter gives a detailed description of the nature of the metabolic work for information transfer in the brain, which provides an understanding of the link between changes in energy metabolism affecting physiological parameters such as blood flow and neuronal activity. It proceeds with a discussion of biochemical pathways that provide energy for brain work and also discusses the role of astrocytes in the regulation of the metabolic response to neuronal excitation. The chapter attempts to identify an alternative regulator that changes in response to work and influences the rate of energy metabolism.

This chapter intends to describe the general observations that have been made on neurones in the precentral motor area of the cerebral cortex and then to examines the specific relationships which have been shown to exist between cortical elements, identified according to the targets of their projecting axons, and the management of the muscles which generate movement. Many studies have examined the neuronal activity in different cortical areas that is associated with natural self-initiated movements or with constrained, learned tasks. However, the overall impression from all these studies is that neurones whose activity is related to a particular aspect of movement are found in many different cortical and sub-cortical regions.

This chapter examines different ways in which timing and probability affect neuronal activity in motor tasks. It describes neuronal data from the motor cortex and analyses the spiking activity of individual neurons during implicit and explicit timing. It investigates the precise spike synchrony and local field potentials of collective activity across multiple neurons and suggests that time is clearly represented in motor cortex, albeit in a context-dependent way.

This chapter describes and illustrates the electroencephalographic changes following motor imagery practice. Specifically, it considers the sensorimotor activation and deactivation through event-related (de)synchronization pattern recordings. It shows that measuring the neuronal activity with high temporal resolution brings researchers a reliable tool to study the time-course of short-lasting changes of neuronal activity in distinct time windows before, during, and after motor imagery. It evaluates the effectiveness of employing multimodal techniques — combining functional magnetic resonance imagery and electroencephalography — to study the neuronal processes engaged in motor imagery.

This chapter explains the importance of normal and abnormal patterns of neuronal activity in the development of the visual system. It reveals that altering cause patterns of retinal activity can disrupt the normal development and/or the maintenance of eye-specific connections. It shows that altered patterns of activity can drive plasticity of segregation in the cortex. This chapter suggests the development of orientation and direction selectivity offer the best evidence in favor of an instructive role for activity in the development of specific connections in the visual system.

The medial temporal lobe (MTL) plays a central role in declarative memory. While the cytoarchitecture of the hippocampus, in particular, dominates neuroanatomical memory models, the manner by which neuronal activity in this area enables the permanent storage of declarative memories remains elusive. Patients with pharmacologically intractable epilepsy, who are candidates for resective surgery and whose seizure focus must be located via the implantation of intracranial electrodes, represent the only opportunity that scientists currently have to record the firing patterns of single neurons in the human hippocampus in vivo. Such direct recordings have shown that cells in the hippocampus appear to support declarative learning by distinguishing between novel and familiar stimuli via changes in firing patterns, using both excitation and inhibition to signal familiarity. Some cells with highly selective excitatory responses have also been described, and these responses seem to represent abstract concepts such as identity, rather than superficial perceptual features of items. New data show that selective and globally responsive cells behave differently depending on the conscious demands of the task. Cells in the MTL can be plastic or stable in terms of the information that they code: while some cells show highly-selective and reproducible excitatory responses when presented with a familiar object, other cells change their receptive fields in line with changes in experience and the cognitive environment. These findings present challenges for current network models of memory and inform our understanding of the neural basis of declarative memory.

Information processing in neurons is critically dependent on dendritic morphology. The overall extent and orientation of dendrites determines the kinds of input a neuron receives. Fine dendritic appendages called spines act as subcellular compartments devoted to processing synaptic information, and the dendritic branching pattern determines the efficacy with which synaptic information is transmitted to the soma. The development of the dendritic tree is influenced by a number of factors. Studies in Drosophila have identified key components of the genetic program that regulates dendritic morphogenesis. Parallel studies in vertebrates have revealed that extracellular signals and neuronal activity exert a major influence on the growth and branching of dendrites and the formation of dendritic spines. The identification of genes that mediate theses processes provides important insights into the molecular mechanisms of dendritic morphogenesis.

Recordings of single neurons in conscious animals started over three decades ago. In unrestrained animals, this activity was documented extracellularly and the recorded neuron was referred to as a “single unit.” Shortly after, single units were recorded in sensory-motor areas of the brain in which timing was often used to differentiate motor from sensory neuronal activity. If a cell's firing rate was systematically modified and tightly coupled to a short interval before the onset of the movement, it was assumed to be linked with motor activity. If it started after movement and/or synchronized with the leading edge or onset of a stimulus, it was assumed to be linked with sensory events.

This chapter describes the source estimation approaches to magnetoencephalography (MEG) analysis. Both MEG and electroencephalography (EEG) are measures of ongoing neuronal activity, and are ultimately generated by the same sources: postsynaptic currents in groups of neurons which have a geometrical arrangement favoring currents with a uniform direction across nearby neurons. From the outset, the overarching theme of MEG analysis methods has been the desire to transform the signals measured by the MEG sensors outside the head into estimates of source activity. This problem is challenging because of the ill-posed nature of the electromagnetic inverse problem. However, thanks to being able to capitalize on appropriate physiological and anatomical constraints, several reliable and widely used source estimation methods have emerged. The chapter then identifies the forward modeling approaches needed to relate the signals in the source and sensor spaces, and characterizes two popular approaches to source estimation: the parametric dipole model and distributed source estimates. Until 50 years ago, electroencephalography (EEG) was the only noninvasive technique capable of directly measuring neuronal activity with a millisecond time resolution. However, with the birth of magnetoencephalography (MEG), functional brain activity can now be resolved with this time resolution at a new level of spatial detail. The use of MEG in practical studies began with the first real-time measurements in the beginning of 1970s. During the following decade, multichannel MEG systems were developed in parallel with both investigations of normal brain activity and clinical studies, especially in epileptic patients. The first whole-head MEG system with more than 100 channels was introduced in 1992. Up to now, such instruments have been delivered to researchers and clinicians worldwide. The overarching theme of MEG analysis methods has been from the outset the desire to transform the signals measured by the MEG sensors outside the head into estimates of source activity. This problem is challenging because of the ill-posed nature of the electromagnetic inverse problem. However, thanks to being able to capitalize on appropriate physiological and anatomical constraints, several reliable and widely used source estimation methods have emerged. This chapter starts by describing the overall characteristics of MEG, followed a general description of the source estimation problem. The chapter then discusses the forward modeling approaches needed to relate the signals in the source and sensor spaces, and finally characterizes two popular approaches to source estimation: the parametric dipole model and distributed source estimates.

This chapter explores the evidence that the place cell representation may underlie navigational behaviour. It also deals with a correlational approach, reviewing data in which spatial information has been manipulated in a way that produces parallel effects on both behaviour and neuronal activity. It briefly outlines the behavioural evidence that navigation depends on a spatially selective information-processing system, and sees whether the same property holds true at the neural level. More specifically, the hippocampal place cell system is considered as a model of how spatial information is encoded in the brain. In addition, it reviews two sets of experiments showing the existence of a clear functional relationship between the nature of place cell positional activity and the properties of spatial navigation. Data shows that the hippocampus performs a very specific function.

Neuronal activity in the brain is regulated by a balance between excitatory and inhibitory influences. Population stability requires that GABAergic inhibition be temporally poised to exercise a restraining influence on reverberating synaptic excitation of local neurons due to recurrent connections. This chapter provides a comprehensive consideration of GABAergic inhibition, highlighting specific issues that are especially important to understanding how inhibition operates and how it is regulated. The intention throughout is to provide not only a synthesis of our present knowledge but also to state some of the principles and implications of GABAergic inhibition that are just emerging. The chapter is organized as follows: (1) the morphology and physiology of inhibitory neurons, (2) the recruitment of inhibition, and (3) the plasticity of the inhibitory circuit.

The ability to communicate fluently with others is surely the direct and obvious result of humans being conscious. This chapter is divided into three parts. The first explains how far one can get in explaining subjective experience in terms of neuronal activity, and why this approach must ultimately be abandoned. The second is on what consciousness does. This section is formed around two reports. The first points out that the success of the human species depends largely on the unique pattern of humans’ cooperative behaviour, while the second suggests that this depends upon what people call consciousness. Finally, the third section compares the characteristics of consciousness that would be expected if it had evolved to expand social communication with its ‘particulars’, as described by William James and others.

This chapter focuses on the constraints of attributing psychological properties to brain profiles. The constraint on the validity of inferences based on one source of evidence bears directly on the neuroscientist's habit of describing brain profiles with words whose meaning and validity originated in psychological data. This practice deserves careful scrutiny because animals or humans are the presumed agents in sentences containing terms describing the psychological processes of perception, memory, intention, feeling, emotion, reasoning, or action. These terms take on novel meanings in sentences in which neuronal activity is the noun. However, the practice of using psychological predicates—such as compute, regulate, or synthesize—to describe brain profiles remains popular because neuroscientists do not have a rich biological vocabulary for the diverse brain profiles that occur in response to incentives.