Cerebellar long-term depression: characterization, signal transduction, and functional roles

Cerebellar Purkinje cells exhibit a unique type of synaptic plasticity, namely, long-term depression (LTD). When two inputs to a Purkinje cell, one from a climbing fiber and the other from a set of granule cell axons, are repeatedly associated, the input efficacy of the granule cell axons in exciting the Purkinje cell is persistently depressed. Section I of this review briefly describes the history of research around LTD, and section II specifies physiological characteristics of LTD. Sections III and IV then review the massive data accumulated during the past two decades, which have revealed complex networks of signal transduction underlying LTD. Section III deals with a variety of first messengers, receptors, ion channels, transporters, G proteins, and phospholipases. Section IV covers second messengers, protein kinases, phosphatases and other elements, eventually leading to inactivation of DL-alpha-amino-3-hydroxy-5-methyl-4-isoxazolone-propionate-selective glutamate receptors that mediate granule cell-to-Purkinje cell transmission. Section V defines roles of LTD in the light of the microcomplex concept of the cerebellum as functionally eliminating those synaptic connections associated with errors during repeated exercises, while preserving other connections leading to the successful execution of movements. Section VI examines the validity of this microcomplex concept based on the data collected from recent numerous studies of various forms of motor learning in ocular reflexes, eye-blink conditioning, posture, locomotion, and hand/arm movements. Section VII emphasizes the importance of integrating studies on LTD and learning and raises future possibilities of extending cerebellar research to reveal memory mechanisms of implicit learning in general.

Is the cerebellum a smith predictor?

The motor system may use internal predictive models of the motor apparatus to achieve better control than would be possible by negative feedback. Several theories have proposed that the cerebellum may form these predictive representations. In this article, we review these theories and try to unify them by reference to an engineering control model known as a Smith Predictor. We suggest that the cerebellum forms two types of internal model. One model is a forward predictive model of the motor apparatus (e.g., limb and muscle), providing a rapid prediction of the sensory consequences of each movement. The second model is of the time delays in the control loop (due to receptor and effector delays, axonal conductances, and cognitive processing delays). This model delays a copy of the rapid prediction so that it can be compared in temporal register with actual sensory feedback from the movement. The result of this comparison is used both to correct for errors in performance and as a training signal to learn the first model. We discuss evidence that the cerebellum could form both of these models and suggest that the cerebellum may hold at least two separate Smith Predictors. One, in the lateral cerebellum, would predict the movement outcome in visual, egocentric, or peripersonal coordinates. Another, in the intermediate cerebellum, would predict the consequences in motor coordinates. Generalization of the Smith Predictor theory is discussed in light of cerebellar involvement in nonmotor control systems, including autonomic functions and cognition.

Neuronal assemblies

This paper examines the concept of neuronal assembly as it has appeared in selected portions of the literature. The context is experimental access to real neuronal assemblies in working brains, as made possible by recent technological progress. One current measure of assembly organization is based on correlation of firing among neurons; recent observations show that such correlations can vary rapidly. In this paper, we demonstrate that dynamic firing correlation can be caused either by dynamic changes in neuronal connection strengths or, alternatively, by the effects of an unobserved (large) pool of other neurons. The static connectivity within the pool appears to be important in determining these effects.

Neural nets

The brain is one of the most highly organized structures in the known universe. It is a biological computer which has evolved over a billion years to program, monitor and control all bodily functions. It is also the organ of knowing, feeling and thinking. To understand how the brain works is perhaps the most difficult of all scientific problems. A scientific theory of the brain would provide a comprehensive understanding of a substantial body of facts on the basis of a few fundamental principles. In this sense, there is no theory of the brain.

Simulation of learning processes in neuronal networks of the cerebellum

A dynamic model of learning that is based on the specific neuronal system of the cerebellum, including some of its structural-functional peculiarities, is proposed. It allows to simulate modification processes of the parallel fiber synapse that influences the Purkinje cell. regularities of synaptic modifications are obtained by extrapolating well-known experimental data bout changes of synaptic efficiency as resulting from release, refilling and mobilization of the mediator. It is shown that a mathematical description of synaptic processes corresponds to experimental data on the changes of synaptic efficiency under rhythmical stimulation and gives objective quantitative estimates for long-term (refilling of the mediator) and short-term (mobilization of the mediator) effects which are caused by presynaptic stimulation. Computer simulations have been conducted to investigate the characteristics of learning for different values of the following parameters: intensity of unconditioned stimulus (US, activity of the climbing fibre), intensity of conditioned stimulus (CS, activity of the parallel fibre), temporal shift between US and CS, temporal interval between reinforcements. It is shown that the temporal shift between CS and US is one of the major factors that influence the learning procedures. Analysis of the data obtained shows that the model enables us to simulate the main regularities of establishment and extinction of conditioned reflexes.

Neural modeling of human behavior: a proposal for a new type of modifiable synapse

Presently published neural models for the stimulation of human behavior have reached a limit in terms of their ability to account for the complexity and magnitude of such behavior. In this paper, these limitations are explored, and a new model is presented which overcomes them. The model employs a new type of modifiable synapse which allows for the integration of three separate bits of information rather than the conventional two bits.

The recall of events through the learning of associations between their parts

A form of recall is discussed in which some elements of an event serve to initiate recall of the whole set of elements which comprised an event. The conditions which enable learned associations between pairs of elements to be used for such recall are quantitatively assessed for recall procedures which use only direct associations from the initial elements (‘simple recall’) and which use both direct and indirect associations (‘progressive recall’). The theory is equivalent to the analysis of a neural model in which randomly arranged excitatory connections between a set of cells can be strengthened by simultaneous activity in their presynaptic and postsynaptic elements. The problems involved in a direct neural implementation of the model are discussed, and it is seen that some of the mechanisms resemble known physiological mechanisms in the cerebral cortex. The relationship between recall and recognition learning is discussed and the evidence relating these functions to the hippocampal formation is assessed.

Neuronal circuits involved in learning: the use of modifiable synapses in the simulation of behavior

Our present knowledge of brain structure and function requires that any model of neuronal circuitry designed to account for learning must satisfy three conditions. It must (1) meet economy restrictions on the number of cells in the brain, (2) use the same set of cells to account for a number of different behaviors, and (3) not require a detailed embryological specification of its connections. Previously published models have failed to meet one or more of these conditions. In this paper, a model is presented which does satisfy them, and in doing so accounts in detail for classical conditioning, operant conditioning, and other learning tasks. The model employs the types of synapses proposed by Burke and Hebb in simple modular circuits as a means of providing independent storage of information at each modifiable synapse.

Psychiatric illnesses: some inferences from symptomatology

In an earlier paper (Maxwell, 1972) the difficulties met in a dimensional approach to the description of symptomatology were discussed. In that study a representative sample of patients was considered under the three broad diagnostic categories, neurotics, affective psychotics and schizophrenics, and it was seen that all had a basic core of symptoms of the type generally referred to as ‘neurotic’, which lent themselves fairly well to a dimensional description and which indeed accounted for the greater part of the covariability of the symptoms in all three categories. But in addition the schizophrenics, and to some extent the affective psychotics, showed further symptoms of the type generally referred to as ‘psychotic’, though these occurred in a patchy and less general way than did the ‘neurotic’ symptoms. As a consequence these symptoms were not amenable to a dimensional description, nor did they lead to clear-cut typologies, as Everitt, Gourlay and Kendell (1971) have already shown.

Nerve net models of plausible size that perform many simple learning tasks

Nerve net models are designed that can learn up to γ of α n possible sequences of stimuli, where γ is large but α n much larger still. The models proposed store information in modifiable synapses. Their connexions need to be specified only in a general way, a large part being random. They resist destruction of a good many cells. When built with Hebb synapses (or any other class B or C synapses whose modification depends on the conjunction of activities in two cells) they demand a number of inputs to each cell that agrees well with known anatomy. The number of cells required, for performing tasks of the kind considered as well as the human brain can perform them, is only a small fraction of the number of cells in the brain. It is suggested that the models proposed are likely to be the most economical possible for their tasks, components and constructional constraints, and that any others that approach them in economy must share with them certain observable features, in particular an abundance of cells with many independent inputs and low thresholds.