Prevailing engineering-inspired theories of motor control based on sequential/algorithmic or motor-programming models are difficult to reconcile with what is known about the anatomy and physiology of the motor areas. This is partly because of certain problems with the theories themselves and partly because of features of the cortical and basal ganglionic motor circuits that seem ill-suited for most engineering analyses of motor control. Recent developments in computational neuroscience offer more realistic, that is, connectionist, models of motor processing. The distributed, highly parallel, and nonalgorithmic processes in these models are inherently self-organizing and hence more plausible biologically than their more traditional algorithmic or motor-programming counterparts. The newer models also have the potential to explain some of the unique features of natural, brain-based motor behavior and to avoid some of the computational dilemmas associated with engineering approaches.

The premise explored in this target article is that the function of the cerebellum is best understood in terms of the operation it performs across its structurally homogeneous subdivisions. The functional heterogeneity sometimes ascribed to these different regions reflects the multiplicity of functions subserved by the central targets receiving the outputs of different cerebellar regions. Recent studies from our own laboratory and others suggest that the functional unit of the cerebellum is the sagittal zone. It is hypothesized that the climbing fiber system produces a short-lasting modification in the gain of Purkinje cell responses to its other principal afferent input, the mossy fiber-granule cell-parallel fiber system. Because the climbing fiber inputs to sagittally aligned Purkinje cells can be activated under functionally specific conditions, these afferents could select populations of Purkinje neurons that would be most highly modulated by mossy fiber inputs responding to the same conditions. These operations may be critical for the on-line integration of inputs characterizing external target space with features of the intended movement, proprioceptive and kinesthetic cues, and the body image.

To investigate neural mechanisms of movement, physiologists have analyzed the activity of task-related neurons in behaving animals. The relative onset latencies of neural activity have been scrutinized for evidence of a functional hierarchy of sequentially recruited centers, but experiments reveal that activity changes occur largely in parallel. Neurons whose activity covaries with movement parameters have been sought for evidence of explicit coding of parameters such as active force, limb displacement, and behavioral set. Neurons with recognizable relations to the task are typically selected from a larger population, ignoring those cells with complex relations to the task and unmodulated cells. Selective interpretations are also used to support the notion that different motor regions perform different motor functions; again, current evidence suggests that units with similar properties are distributed over widely different regions.

These coding issues are reexamined for premotoneuronal (PreM) cells, whose correlational links with motoneurons are revealed by spike-triggered averages. PreM cells are recruited over long times relative to their target muscles; they show diverse response patterns relative to the muscle force they produce; functionally disparate PreM cells such as afferent fibers and descending corticomotoneuronal and rubromotoneuronal cells can exhibit similar patterns. Neural mechanisms have been further elucidated by neural network simulations of sensorimotor behavior; the pre-output hidden units typically show diverse response patterns in relation to their target units. Thus, studies in which both the activity and the connectivity of the same units are known reveal that units with both simple and complex relations to the task contribute significantly to the output. This suggests that the search for explicit coding may be diverting us from understanding distributed neural mechanisms that operate without literal representations.

The posterior parietal cortex (PPC) is the most likely site where egocentric spatial relationships are represented in the brain. PPC cells receive visual, auditory, somaesthetic, and vestibular sensory inputs; oculomotor, head, limb, and body motor signals; and strong motivational projections from the limbic system. Their discharge increases not only when an animal moves towards a sensory target, but also when it directs its attention to it. PPC lesions have the opposite effect: sensory inattention and neglect. The PPC does not seem to contain a “map” of the location of objects in space but a distributed neural network for transforming one set of sensory vectors into other sensory reference frames or into various motor coordinate systems. Which set of transformation rules is used probably depends on attention, which selectively enhances the synapses needed for making a particular sensory comparison or aiming a particular movement.