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This chapter discusses the problem of spatial transformation in the multisensory stabilization of gaze. Stabilization of gaze is a goal achieved by the cooperation of several sensory inputs, each providing complementary information on the movement of self with respect to the external environment. The neuronal processing of multisensory inputs underlying gaze stabilization could be regarded essentially as a problem of coordinate transformation. Head motion is initially coded in the reference frames proper of each sensory modality. For instance, head angular velocity is measured by the vestibular system in a three-dimensional, nearly orthogonal reference frame, defined by the axes orthogonal to the planes on which each pair of semicircular canals, with a good degree of approximation, can be considered to lie. In order to produce compensatory rotations of the eyes, this sensory representation of head movement must ultimately be transformed into an oculomotor coordinate frame. Among the many different reference frames that can be chosen to represent eye rotations, the natural reference frame defined by the spatial orientation of the pulling action of the extraocular muscles looks a very favorable one for describing the sensorimotor integration underlying gaze stabilization.

This chapter examines the mechanisms underlying sensorimotor integration by discussing three cases where anatomical and physiological studies of circuits are co-evolving with computer models of the circuit: the first focuses on the dorsal bending reflex in the leech, the second deals with the vestibulo-ocular reflex (VOR) in mammals, and the third is concerned with rhythmic behaviors generated by the spinal cord. The chapter explains each case in detail, beginning with the computational model for the local bending reflex in the leech and proceeding with a discussion of a model network incorporating known pathways, connections, and physiology of the VOR. It also considers how time is represented in nervous systems and describes the segmental swimming oscillator before concluding with an overview of modeling of neurons.

This book introduces a conceptual framework for brain function based on large populations of neurons. It advances the hypothesis that emergent properties are high-level effects that depend on lower-level phenomena in some systematic way, drawing on the idea that brains are computational in nature. Areas and topics related to computational neuroscience covered in this book include computational mechanisms in neurons, analysis of signal processing in neural circuits, representation of sensory information, systems models of sensorimotor integration, and computational approaches to plasticity. The book emphasizes the importance of single neuron models as the foundation into which network models must eventually fit. It also provides a background discussion on neuroscience and the science of computation.