Introduction

The local field potential from the hippocampus of an awake, stationary animal is filled with seemingly random low-amplitude high-frequency activity and arrhythmic high-amplitude low-frequency activity. To the untrained eye it is nearly impossible to discern any clear patterns or relationships of the signal to behavior, as the local field potential appears to be only noise. Then something striking happens, as the animal starts to move around, an extremely rhythmic high-amplitude 6–10-Hz sinusoidal waveform appears and instantly offers a window into the relationship between the hippocampal field potential and the animal's behavior. When Green and Arduini (1954) first recorded local field potentials activity from the hippocampus of behaving animals they saw this rhythmic activity (theta rhythm; 6–10-Hz high-amplitude sinusoidal activity: Buzsáki et al., 1986) and it was clear to them that theta rhythm played a significant role in hippocampal function. Theta appeared whenever the animal walked, ran, sniffed, oriented, reared, or went into rapid eye movement (REM) sleep, and theta was notably absent during consummatory behaviors (eating and drinking), grooming, and slow wave sleep.

The nature of theta's role in hippocampal processing was somewhat clarified when studies of the effect of hippocampectomy in humans and rodents were performed, and the hippocampus's central role in associative memory was suggested (Scoville and Milner, 1957; Morris et al., 1982). Since those original studies, further work has shown memory impairments in a wide range of behavioral and cognitive tasks that utilize multiple sensory modalities and behavioral responses (Eichenbaum, 2000).

Introduction

Neural representations are distributed. This means that more information can be gleaned from neural ensembles than from single cells. Modern recording technology allows the simultaneous recording of large neural ensembles (of more than 100 cells simultaneously) from awake behaving animals. Historically, the principal means of analyzing representations encoded within large ensembles has been to measure the immediate accuracy of the encoding of behavioral variables (“reconstruction”). In this chapter, we will argue that measuring immediate reconstruction only touches the surface of what can be gleaned from these ensembles. We will discuss the implications of distributed representation, in particular, the usefulness of measuring self-consistency of the representation within neural ensembles. Because representations are distributed, neurons in a population can agree or disagree on the value being represented. Measuring the extent to which a firing pattern matches expectations can provide an accurate assessment of the self-consistency of a representation. Dynamic changes in the self-consistency of a representation are potentially indicative of cognitive processes. We will also discuss the implications of representation of non-local (non-immediate) values for cognitive processes. Because cognition occurs at fast timescales, changes must be detectable at fast (millisecond, tens of milliseconds) timescales.

Representation

As an animal interacts with the world, it encounters various problems for which it must find a solution. The description of the world and the problems encountered within it play a fundamental role in how an animal behaves and finds a solution.

Introduction

The hippocampus lies at the apex of the hierarchical organization of cortical connectivity, receiving convergent multimodal inputs that are funneled through the adjacent entorhinal cortex (Fig. 2.1). The output of the hippocampus is relayed back through the entorhinal cortex, and thus these structures are ideally placed to both store novel associations and detect predictive errors (Lavenex and Amaral,2000; Witter et al., 2000). Indeed, while memories are likely to be stored across distributed brain regions, the learning and consolidation of explicit memories appear to depend upon the hippocampus and surrounding parahippocampal regions (Morris et al., 2003; Squire et al., 2004). However, while the anatomical substrate of such learning is becoming increasingly well defined, it remains unclear how cells act collectively within these neuronal networks to extract and store salient input correlations.

Over 50 years ago, Donald Hebb postulated a simple cellular learning rule, whereby the strength of the synaptic connection between two neurons would be increased if activity in the presynaptic neuron persistently contributed to discharging the postsynaptic neuron (Hebb, 1949). It has since then been shown that such repeated pairings of synaptic events with postsynaptic action potentials (spikes), within a window of tens of milliseconds, can produce long-term changes in synaptic efficacy in many different neuronal systems, both in vitro and in vivo (Paulsen and Sejnowski, 2000; Bi and Poo, 2001).

What kind of reception do regenerating axons or SCs encounter when they enter nerve trunks that have not housed axons for substantial periods of time? Are target organs receptive to reactivation after months or years of denervation? Unfortunately, the structural and molecular consequences of prolonged denervation substantially diminish the likelihood for reinnervation. This chapter deals with delayed reinnervation, a common and all too frequently unavoidable problem in patients.

Clinical scenarios and long-term denervation

There are several reasons why axons may encounter denervated distal stumps or target organs months or years after an injury. The first is obvious. The most optimistic rates of axon recovery range between 1 and 3 mm/day or an inch per month. Many severe human nerve trunk injuries occur in large proximal nerves, such as the sciatic nerve in the thigh or buttock [557] (see Figure 1.2). These lesions rarely allow successful recovery of sensation or motor connections to muscle endplates in the distal leg or foot. In the case of a lesion of the sciatic nerve at the level of the thigh, it would require over a year for axons to regenerate an approximate distance of 800 mm to the foot unimpeded. Moreover, distal portions of the nerve trunk would not receive new axons for many months. This analysis, however, oversimplifies the true scenario that may exist. Estimates of regeneration are based on the time the first axons meet their target, whereas a substantial population of axons must connect to their targets for functional reinnervation.

This book is about peripheral nerves, their unique biology and how they repair themselves during regeneration. The biology of the peripheral nervous system is not often considered on its own. Much has been learned about the neurosciences of peripheral nerves, specifically during injury and regeneration, but it is my sense that some of this new and exciting information should be consolidated and considered in an overview.

Without nerves, specifically peripheral nerves, there is no movement, no sensation. Peripheral nerves are the essential connections between the body, brain, and spinal cord. The “peripheral nervous system (PNS)” distinguishes itself from the “central nervous system (CNS)” on many levels. Peripheral axons reside in many types of local environments including muscles, connective tissue, skin, and virtually every organ of the body. This reach extends into the meninges that surround the brain, a surprising fact to some. Moreover, peripheral neurons are very different from their CNS counterparts in how they respond to injury or disease, in which cells they partner with and in what axon trees they support. For example, a sensory neuron in the lumbar dorsal root sensory ganglion is required to maintain and support distal axon branches that can extend a meter or more to the skin of the toe. Only a small proportion of CNS neurons have comparable outreach and demands placed upon them.

“Neuropathies,” of which there are a large number, are simply disorders of peripheral nerves.

The cellular basis of theta-band oscillation and synchrony

The limbic cortex represents multiple synchronizing systems (Bland and Colom, 1993). Populations of cells in these structures display membrane potential oscillations as a result of intrinsic properties of membrane currents. These cells also receive inputs from other cells in the same structure and inputs from cells extrinsic to the structure, many of the latter from nuclei contributing to the ascending brainstem hippocampal synchronizing pathways. Theta-band oscillation and synchrony in the hippocampal formation (HPC) and related limbic structures is recorded as an extracellular field potential consisting of a sinusoidal-like waveform with an amplitude up to 2 mV and a narrow band frequency range of 3–12 Hz in mammals. The asynchronous activity termed large-amplitude irregular activity (LIA) is an irregular waveform with a broadband frequency range of 0.5–25 Hz (Leung et al., 1982). Kramis et al. (1975) were the first formally to propose the existence of two types of hippocampal theta activity, in both the rabbit and the rat (see review by Bland, 1986). One type was termed atropine-sensitive theta, since it were abolished by the administration of atropine sulfate. Atropine-sensitive theta occurred during immobility in rabbits in the normal state and occurred in both rabbits and rats during immobility produced by ethyl ether or urethane treatment. The other type of theta was termed atropine-resistant, since it was not sensitive to treatment with atropine sulfate but was abolished by anesthetics.

Introduction

Oscillatory synchronization in the gamma-band range (~30–100 Hz) has been proposed as a possible solution to the “binding problem,” i.e. the question of how the brain integrates perceptual features that are processed in distant cortical regions to generate a coherent object representation. Intracortical recordings in animals have demonstrated stimulus-specific synchronous oscillations of spatially distributed, feature-selective neurons (Eckhorn et al., 1988; Gray et al., 1989) that may provide a general mechanism for the temporal coordination of activity patterns in spatially separate regions of the cortex (Gray and Singer, 1989; Singer et al., 1997). In addition to visual feature binding, fast oscillations have been found to reflect modulations of arousal (Munk et al., 1996), perceptual integration (Fries et al., 1997), and attentional selection processes (Fries et al., 2001), and have even been proposed as a potential neural correlate of consciousness (Engel and Singer, 2001; Singer, 2001). In the middle of the last decade, the first studies of gamma-band activity (GBA) in human electroencephalogram (EEG) have relied on paradigms analogous to the early animal work (Lutzenberger et al., 1995; Müller et al., 1996). Since then, investigations using scalp EEG, magnetoencephalography (MEG), and intracranial recordings have supported the functional significance of fast oscillatory activity for a wide range of human cognitive functions. The present chapter will first provide a brief overview of the current state of human GBA research related to visual perception, selective attention, and memory.

Summary of chapters

The different chapters discuss a wide range of concepts, techniques, and strategies of how to investigate the issue of information encoding in neuronal populations. The diversity clearly shows that the question is of central interest, that there is a lively competition of ideas and concepts, and that it is now possible to address the issue adequately by using new technology, the lack of which hampered discoveries in the past. The multileveled concepts described here show that there will not be a simple model or coding mechanism that can adequately explain how the brain works.

Part II: Organization of neuronal activity in neuronal populations

In this section, discussion focused on what general rules and physiological processes are in place that govern information encoding, processing, network formation, and laying down of memory traces.

In Chapter 2, Edward Mann and Ole Paulsen gave a detailed overview of the cellular mechanisms that underlie the establishment of oscillating networks. The fact that a multitude of specialized ion channels, interneuron subtypes, and neuronal projections are in place to establish defined oscillations in a controlled way clearly supports their concept that this mechanism is important for organizing brain processes. This point was also well illustrated by Brian Bland in Chapter 12, where he showed which “purpose-built” basal brain nuclei and projections are involved in the induction and control of theta oscillations in areas throughout the brain.

A thorough appreciation of the unique anatomy of the peripheral nervous system is essential in understanding how it regenerates. This information, already described in several texts, is nonetheless summarized here to prepare the reader for later chapters. There are many facets to peripheral nervous system anatomy that have a bearing on its response to injury including the multiplicity of neuron subtypes and the qualities of their housing.

Overall structure

The peripheral nervous system (PNS) is complex. The peripheral nerve “trunk” refers to a cable of tissue in which hundreds to thousands of axons may travel. Peripheral nerve trunks form connections from the brain and spinal cord to all skeletal muscles in the body through motor axons. They also connect all sensory organs to the brain and spinal cord through sensory axons (Figure 2.1). Finally, they connect the CNS to smooth muscles, sweat glands, blood vessels, and other structures through axons of the autonomic nervous system. Axons traveling through nerve trunks originate from cell bodies of neurons (perikarya) in the brainstem, spinal cord, and ganglia. Motor neuron cell bodies found in cranial motor nuclei supply the head and neck, and those in the anterior gray matter horn of the spinal cord supply the limbs and trunks. Motor neuron cell bodies have their greatest numbers in the cervical and lumbar enlargements of the spinal cord so that they can supply the large number of muscles in the upper limbs and lower limbs, respectively.

Distributed representations are the inevitable consequence of devoting large neuronal circuits to a detailed and adaptive analysis of complex information. The neocortical sheet with its extensive cortico-cortical connectivity is characterized by ubiquitous massive divergence and convergence, sparseness and reciprocity of the vast majority of connections. It therefore appears as an optimized dynamical structure for detailed and adaptive analysis on the one hand and the operation of multiple parallel neuronal processes required for optimizing speed and accuracy of information processing on the other hand. The established concepts of information coding in the cortex are based on tuning functions of many individual neurons thought to express their stimulus specificity in an independent way. A second level of organization is usually attributed to the spatial relations of neurons in topographically organized representations like those in sensory areas and the convergence of neuronal signals carrying information from different modalities into “higher” areas which are more involved in executive functions or the formation of complex memory representations. It has been argued that the collection of neuronal signals from consecutive recording sessions can be used to reconstruct population codes as it has been done with the “population vector” analysis in motor, sensory, and memory areas of the cortex. It is clear that the success of this method relies on fixed neuronal response properties which have been consolidated in cortical circuits over long periods of time such that the spatial pattern and the mixture of neurons contributing to the population response is reasonably stable.

Introduction

Sensory information progresses centrally from the primary sensors in the periphery to the central neural structures that derive relevant environmental information from these sensory data and determine appropriate physiological and behavioral responses. In this chapter, I present a general theory of early olfactory sensory processing in the primary olfactory epithelium and olfactory bulb (OB). The theory depicts olfactory sensory processing as a cascade of representations, each of which exhibits characteristic physical properties and is sampled by appropriate neural mechanisms in order to construct the subsequent representation. The primary olfactory representation is mediated by the activation pattern across the population of primary olfactory sensory neurons (OSNs) in the sensory epithelium. The secondary olfactory representation is similarly mediated by the activation pattern across the population of principal neurons immediately postsynaptic to the OSNs, known as mitral cells. (Mitral cell axons diverge dramatically, projecting to roughly ten different central structures within the brain; the resulting tertiary and subsequent olfactory representations are constructed outside the olfactory bulb and are not discussed at length herein.) The transformation between the primary and secondary representations is a robust, intricate, two-stage process that corrects for artefacts that can hinder the recognition of odor qualities, regulates stimulus selectivity, and transduces the underlying mechanics from a robust but costly rate-coding scheme on a slow respiratory (theta-band) timescale to a sparse dynamical representation operating on the beta- and gamma-band timescales and suitable for integration with other central neural processes.