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Disease processes affecting the cerebellum and its connections, such as can occur in multiple sclerosis, often lead to lack of motor coordination, postural tremor, and tremor on directed movement; these symptoms can be difficult to treat. The cerebellum generates oscillations over a range of frequencies (beta, gamma, very fast) and some of these are coherent with oscillations in thalamus and in muscle. Genetically modified ataxic mice can exhibit short runs of very fast oscillations that are gap junction dependent. Oscillations can also be induced in cerebellar cortex slices: gamma and very fast oscillations both require gap junctions, and gamma also depends on synaptic inhibition.

This chapter focuses on the types and distributions of the main voltage-gated ion channels presently known to exist within the dendrites of CA1 pyramidal neurons, neocortical layer Vm pyramidal neurons, cerebellar Purkinje cells, and olfactory bulb mitral cells. This is a rapidly expanding field, and the focus of this chapter is intentionally limited to these four distinctly different types of central neurons because a substantial amount of high-quality information is available concerning their dendritic voltage-gated channels, and because they express a wide range of different types of dendritic electrogenesis. The chapter begins with a short survey of the known dendritic voltage-gated ion channel types and their modulation. A table comparing the physiologically relevant biophysical properties and some pharmacology of these ion channels is also included.

The cerebellar cortex appears to be involved in predictive feedforward control to generate smooth movements. There is a beautiful network architecture which suggests that the granule cells perform expansion recoding of the inputs; that these connect to the Purkinje cells via an architecture that ensures regular sampling; and that each Purkinje cell has a single teacher, the climbing fibre, which produces associative long-term synaptic depression as part of perceptron-like learning.

The development of spines in two types of neurons: cerebellar Purkinje cells, and cortical pyramidal neurons, are described. The chapter asserts that some of the features of spinogenesis may be intrinsic to the neuron and some could be extrinsic and activity-reliant. It also points out that Purkinje cells exemplify the two aspects of spine development: the extrinsic, “plastic,” and activity-reliant; and the intrinsic, “hardwired” plan.

First, this comparison does highlight how the design of different types of cortex is very different, and at the same time how precisely types of cortex are designed. The implication is that the quantitative details of the connectivity are important, a point understood by Marr, but hardly emphasized in most ‘canonical models’ of the microcircuitry of the cerebral cortex (Chapter 18), most of which may not even show recurrent collateral connections between classes of pyramidal cells, let alone consider their number, and what the computational significance may be. Second, the cerebellar cortex does not have excitatory recurrent collaterals, and the cerebral cortex does. This helps to make excitatory recurrent collaterals what is probably the most fundamental aspect of the design of the cerebral cortex, which provides the ability to maintain information by positive excitatory feedback, to enable short-term memory, long-term memory, decision-making, top-down attention, etc, as described throughout this book (see also Chapter 18). Third, analysis of the cerebellum shows how important the quantitative aspects of the connectivity are for brain computation, with large numbers of parallel fibre synapses for example onto each Purkinje cell. The importance of this is that the number of synapses onto each neuron is the leading factor in the number of memories that can be stored in pattern associators, perceptrons, and autoassociation networks, so these numbers are just as important for cerebral cortex (Appendix B). Fourth, analysis of the cerebellum shows how important it is for associative memories to have relatively orthogonal patterns presented to the network, with this being implemented by the 10¹⁰ or more granule cells in the cerebellum. In the same way, the hippocampus has granule cells in the dentate gyrus to perform pattern separation, and the neocortex has stellate cells in layer 4 to do the same, especially where it appears that much orthogonalization is required, in primary sensory cortical areas such as the primary visual cortex, V1. Fifth, a major difference from the cerebral cortex is that each Purkinje cell has its own teacher, its climbing fibre. Instead, there appears to be nothing like this in the cerebral cortex. Instead, the hypothesis developed in this book is that in the neocortex the correct pyramidal cells are selected by which ones win in a competitive network style of computation for any given input pattern. Further, the hypothesis is that in the hippocampus, the selection of which CA3 cells should be activated is performed by random selection, using the mossy fibres, as described in Chapter 24. The difference from neocortex is that the computation performed by the hippocampal CA3 network is to store large numbers of memories separately, and for this, picking neurons at random to be part of each memory is appropriate. In contrast, in the neocortex, the aim of the computation is much more sophisticated, to build useful representations for categorising complex inputs, and for this purpose competitive learning is used to produce neocortical neurons that respond to different non-linear combinations of their inputs (Section B.4, 18.2.1, and 26.5.18).

This chapter focuses on the types and distributions of the main voltage-gated ion channels currently known to exist within the dendrites of CA1 pyramidal neurons, neocortical L5 pyramidal neurons, cerebellar Purkinje cells, and olfactory bulb mitral cells. The focus of this chapter is intentionally limited to these four distinctly different types of central neurons because a substantial amount of high-quality information is available concerning their dendritic voltage-gated channels and because they express a wide range of different types of dendritic electrogenesis. The chapter begins with a short survey of the known dendritic voltage-gated ion channel types and their modulation. This is followed by a look at the distributions of these channels across the entire axis of the four cell types. Lastly, a section is presented to introduce some current ideas on how dendritic voltage-gated channels might impact on the physiological functioning of these neurons.

The language of thought hypothesis is one of Fodor’s seminal contributions to cognitive science. Prominent among the objections to it has been the argument that there is no neurobiological evidence for materially realized symbols in the brain. If memory is materially realized by enduring alterations in synaptic conductances, then this is true, because the synaptic-conductance hypothesis is simply the ancient associative learning hypothesis couched in neurobiological language. Associations are not symbols and cannot readily be made to function as such, thus neurobiologists are unable to say how simple information—for example, the durations of intervals in simple Pavlovian conditioning paradigms—are stored in altered synaptic conductances. Recent results from several laboratories converge, strongly suggesting that memories do not reside in altered synaptic conductances but rather at the molecular level inside neurons. The chapter reviews the experimental evidence for this revolutionary conclusion, as well as the plausibility arguments for it.

This chapter outlines different features of intramembrane charge movements in a variety of experimental systems, comparing steady-state and kinetic properties of the charge with those of known physiological functions. In skeletal muscle, fast charge movements are demonstrated in Vaseline-gap clamp studies of cut amphibian fibre preparations. Their properties are similar to the gating currents in cephalopod giant-axon and amphibian myelinated-nerve membranes. The charge-voltage curves remain similar in form and retain a best fit to a two-state Boltzmann system. The greater part of the charge movement in skeletal muscle exhibits relaxations that extend over substantially more extended time-scales of milliseconds to hundreds of milliseconds. The charge–voltage relation in ventricular myocytes follows a two-state Boltzmann relationship, which constitutes a useful empirical description but need not literally imply a homogeneous population of two-state systems. A number of findings strongly suggest that multiple components of the charge movement exist in ventricular muscle. Gating currents have also been observed in canine cardiac Purkinje cells. They have no transverse tubules, and so there are no large contributions from events related to excitation–contraction coupling. The charge movements differed from those observed in both nerve or muscle in some respects. The dependence of the non-linear charge upon steady-state voltage did not deviate from the expectations for a two-state Boltzmann system. There are closer similarities between charge movements and potassium-channel kinetics. Charging components directly attributable to calcium-channel gating are not observed in canine cardiac Purkinje cells. This is in contrast to the currents observed in ventricular myocytes. Genetic evidence strongly suggests a similarity or identity between the membrane entities involved in calcium-channel gating and those that mediate excitation–contraction coupling.

Marr’s theory of the cerebellum in 1969 came as a great surprise. Since Albus published a similar theory two years later, these network theories have been collectively called the “Marr–Albus” model. A major assumption adopted in this model is that numerous synapses supplied by parallel fibers to each Purkinje cell exhibit plasticity. Long-term potentiation (LTP) or depression (LTD) is induced upon conjunctive synapse activation by climbing fibers that convey a teacher’s signals. Over the past four decades, this assumption has been rigorously tested; as a result, it was found that parallel fiber–climbing fiber conjunctive activation induces LTD whereas parallel fiber stimulation alone induces LTP. Another major assumption in Marr’s model is the “codon” representation in the mossy fiber–granule cell arrangement, which has evolved to the “liquid-state machine” model having broad spatiotemporal representation. This model well explains the behaviors of granule and Purkinje cells in motor learning. Various types of synaptic plasticity have been found at many sites not only within the cerebellar cortex, but also in cerebellar and vestibular nuclei, and their specific roles have been defined partially. Modular units of cerebellar circuits provide two types of internal model, forward and inverse, to assist in the precise control of movements. Although the cerebellum’s roles in movements have been established, evidence suggests that the cerebellum plays roles also in cognition and emotion. Marr’s theory of the cerebellar cortex was the first huge step toward conjoint computational and experimental approaches to brain study.

This chapter presents a discussion on glutamate receptor delta2 subunit (GluRδ2) in cerebellar wiring. It suggests that GluRδ2 is involved in stabilization and the strengthening of synaptic connectivity between parallel fibers (PFs) and Purkinje cells (PCs). It illustrates that GluRδ2 expression in nonneuronal cells triggers the formation of contacts with granule cell axons and interactions with an unknown presynaptic protein regulate vesicle clustering. The presence of the GluRδ2 is enough to induce the formation of new PF contacts also in cells different from the PCs. This chapter also shows that GluRδ2 has the potential of inducing plastic events in cerebellar circuitry.

This chapter attempted to summarize the common morphological features of spines. Data for this chapter were derived from the literature on pyramidal and Purkinje cells. It shows that spines are morphologically heterogeneous, although at the same time, they are also conspicuously small. The findings imply a functional interpretation of the spine structure, as if they were made to optimize input integration.

Cervical spinoreticular (CSR) neurons and the linked medullary reticular neurons respond to proprioceptive afferent volleys stemming not only from the forelimb but also from the dorsal neck musculature. These neurons may also respond to vestibular afferent volleys stemming from macular labyrinthine receptors. The potential that CSR neurons are involved in the proprioceptive cervical and labyrinthine control of posture is supported by anatomic observations, manifesting that both uncrossed as well as crossed CSR neurons terminate in the precerebellar lateral reticular nucleus (LRN), which inhibits the discharge of excitatory vestibulospinal (VS) neurons by acting through Purkinje cells of the cerebellar hermivervis. Physiologic researches have shown that populations of both MRF and LRN neurons respond to neck and macular labyrinthine stimulations.

Now that we have quantified the behavior of the cell in response to current pulses and current steps as delivered by the physiologist's microelectrode, let us study the behavior of the cell responding to a more physiological input. For instance, a visual stimulus in the environment will activate cells in the retina and its target, neurons in the lateral geniculate nucleus. These, in turn, make on the order of 50 excitatory synapses onto the apical tree of a layer 5 pyramidal cell in primary visual cortex such as the one we use throughout the book, and about 100-150 synapses onto a layer 4 spiny stellate cell (Peters and Payne, 1993; Ahmed et al., 1994, 1996; Peters, Payne, and Rudd, 1994). All of these synapses will be triggered within a fraction of a millisecond (Alonso, Usrey, and Reid, 1996). Thus, any sensory input to a neuron is likely to activate on the order of 102 synapses, rather than one or two very specific synapses as envisioned in Chap. 5 in the discussion of synaptic AND-NOT logic. This chapter will reexamine the effect of synaptic input to a realistic dendritic tree. We will commence by considering a single synaptic input as a sort of baseline condition. This represents a rather artificial condition; but because the excitatory postsynaptic potential and current at the soma are frequently experimentally recorded and provide important insights into the situation prevailing in the presence of massive synaptic input, we will discuss them in detail. Next we will treat the case of many temporally dispersed synaptic inputs to a leaky integrate-and-fire model and to the passive dendritic tree of the pyramidal cell. In particular, we are interested in uncovering the exact relationship between the temporal input jitter and the output jitter. The bulk of this chapter deals with the effect of massive synaptic input onto the firing behavior of the cell, by making use of the convenient fiction that the detailed temporal arrangement of action potentials is irrelevant for neuronal information processing. This allows us to derive an analytical expression relating the synaptic input to the somatic current and ultimately to the output frequency of the cell.

This chapter presents animal experiments that study maternal infection as a risk factor for schizophrenia. Exposure of pregnant mice to a strain of human influenza virus results in offspring with several histologic abnormalities in the hippocampus and cortex. These offspring also display a spatially restricted deficit in Purkinje cells, which are commonly found in autism and also occur in schizophrenia, as well as smaller and more densely packed pyramidal cells, a finding also reminiscent of schizophrenia pathology. Moreover, rodent models of viral and bacterial maternal infection yield offspring with a series of abnormal behaviors and neuropathology consistent with those found in schizophrenia. These models are being used to investigate the molecular and cellular pathways that mediate the effects of maternal infection on fetal brain development.

This chapter narrates the discovery of dendritic spines. It begins with Santiago Ramón y Cajal, a Spanish professor of Pathology and Histology, and his work entitled Estractura delos centros nerviosos de la aves (Structure of the Nervous Centers in Birds) and how he discovered the spines using the Golgi technique. Cajal defined “spine” on the spines of a rose bush, as it resembled one when he first investigated it in Purkinje cells.