Error detection and representation in the olivo-cerebellar system

Shortly after John Eccles completed his studies of synaptic inhibition in the spinal cord, for which he was awarded the 1963 Nobel Prize in physiology/medicine, he opened another chapter of neuroscience with his work on the cerebellum. From 1963 to 1967, Eccles and his colleagues in Canberra successfully dissected the complex neuronal circuitry in the cerebellar cortex. In the 1967 monograph, "The Cerebellum as a Neuronal Machine", he, in collaboration with Masao Ito and Janos Szentágothai, presented blue-print-like wiring diagrams of the cerebellar neuronal circuitry. These stimulated worldwide discussions and experimentation on the potential operational mechanisms of the circuitry and spurred theoreticians to develop relevant network models of the machinelike function of the cerebellum. In following decades, the neuronal machine concept of the cerebellum was strengthened by additional knowledge of the modular organization of its structure and memory mechanism, the latter in the form of synaptic plasticity, in particular, long-term depression. Moreover, several types of motor control were established as model systems representing learning mechanisms of the cerebellum. More recently, both the quantitative preciseness of cerebellar analyses and overall knowledge about the cerebellum have advanced considerably at the cellular and molecular levels of analysis. Cerebellar circuitry now includes Lugaro cells and unipolar brush cells as additional unique elements. Other new revelations include the operation of the complex glomerulus structure, intricate signal transduction for synaptic plasticity, silent synapses, irregularity of spike discharges, temporal fidelity of synaptic activation, rhythm generators, a Golgi cell clock circuit, and sensory or motor representation by mossy fibers and climbing fibers. Furthermore, it has become evident that the cerebellum has cognitive functions, and probably also emotion, as well as better-known motor and autonomic functions. Further cerebellar research is required for full understanding of the cerebellum as a broad learning machine for neural control of these functions.

A coordinated movement is easy to recognize, but we know little about how it is achieved. In search of the neural basis of coordination, we present a model of spinocerebellar interactions in which the structure-functional organizing principle is a division of the cerebellum into discrete microcomplexes. Each microcomplex is the recipient of a specific motor error signal - that is, a signal that conveys information about an inappropriate movement. These signals are encoded by spinal reflex circuits and conveyed to the cerebellar cortex through climbing fibre afferents. This organization reveals salient features of cerebellar information processing, but also highlights the importance of systems level analysis for a fuller understanding of the neural mechanisms that underlie behaviour.

In recent years, it has become clear that motor learning, as revealed by associative eyelid conditioning and adaptation of the vestibulo-ocular reflex, contributes to the well-established cerebellar functions of sensorimotor integration and control. Long-term depression of the parallel fiber-Purkinje cell synapse (which is often called 'cerebellar LTD') is a cellular phenomenon that has been suggested to underlie these forms of learning. However, it is clear that parallel fiber LTD, by itself, cannot account for all the properties of cerebellar motor learning. Here we review recent electrophysiological experiments that have described a rich variety of use-dependent plasticity in cerebellum, including long-term potentiation (LTP) and LTD of excitatory and inhibitory synapses, and persistent modulation of intrinsic neuronal excitability. Finally, using associative eyelid conditioning as an example, we propose some ideas about how these cellular phenomena might function and interact to endow the cerebellar circuit with particular computational and mnemonic properties.