Tensorial aspects of the multidimensional massively parallel sensorimotor function of neuronal networks

The representation of egocentric space in the posterior parietal cortex

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.

Early stages in a sensorimotor transformation

We present a model for several early stages of the sensorimotor transformations involved in targeted arm movement. In psychophysical experiments, human subjects pointed to the remembered locations of randomly placed targets in three-dimensional space. They made consistent errors in distance, and from these errors stages in the sensorimotor transformation were deduced. When subjects attempted to move the right index finger to a virtual target they consistently undershot the distance of the more distal targets. Other experiments indicated that the error was in the sensorimotor transformation rather than in the perception of distance. The error was most consistent when evaluated using a spherical coordinate system based at the right shoulder, indicating that the neural representation of target parameters is transformed from a retinocentric representation to a shoulder-centered representation. According to the model, the error in distance results from the neural implementation of a linear approximation in the algorithm to transform shoulder-centered target parameters into a set of arm orientations appropriate for placing the finger on the target. The transformation to final arm orientations places visually derived information into a frame of reference where it can readily be combined with kinesthetically derived information about initial arm orientations. The combination of these representations of initial and final arm orientations could give rise to the representation of movement direction recorded in the motor cortex by Georgopoulos and his colleagues. Later stages, such as the transformation from kinematic (position) to dynamic (force) parameters, or to levels of muscle activation, are beyond the scope of the present model.

Does the nervous system depend on kinesthetic information to control natural limb movements?

This target article draws together two groups of experimental studies on the control of human movement through peripheral feedback and centrally generated signals of motor commands. First, during natural movement, feedback from muscle, joint, and cutaneous afferents changes; in human subjects these changes have reflex and kinesthetic consequences. Recent psychophysical and microneurographic evidence suggests that joint and even cutaneous afferents may have a proprioceptive role. Second, the role of centrally generated motor commands in the control of normal movements and movements following acute and chronic deafferentation is reviewed. There is increasing evidence that subjects can perceive their motor commands under various conditions, but that this is inadequate for normal movement; deficits in motor performance arise when the reliance on proprioceptive feedback is abolished either experimentally or because of pathology. During natural movement, the CNS appears to have access to functionally useful input from a range of peripheral receptors as well as from internally generated command signals. The unanswered questions that remain suggest a number of avenues for further research.

Implications of neural networks for how we think about brain function

Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On the other hand, knowing a system's function and describing it with elegant mathematics tell one very little about what to expect of interneuronal behavior. Examples of simple networks based on neurophysiology are taken from the oculomotor literature to suggest how single-unit interpretability might decrease with increasing task complexity. It is argued that trying to explain how any real neural network works on a cell-by-cell, reductionist basis is futile and we may have to be content with trying to understand the brain at higher levels of organization.

Inconvenient truths about neural processing in primary motor cortex

Primary motor cortex (MI) plays an important role in voluntary motor behaviour, yet considerable debate remains on how neural processing within this brain region contributes to motor function. This article provides a brief review of the dominant conceptual frameworks used to interpret MI activity, notably servo-control during the 1970s and early 1980s, and sensorimotor transformations since that time. The former emphasized the use of feedback, but was abandoned because delays in sensory feedback could not permit sufficient feedback gains to generate observed patterns of limb movement. The latter framework focuses attention on identifying what coordinate frames, or representations, best describe neural processing in MI. However, studies have shown that MI activity correlates with a broad range of parameters of motor performance from spatial target location, hand or joint motion, joint torque and muscle activation patterns. Further, these representations can change across behaviours, such as from posture to movement. What do heterogeneous, labile neural representations mean and how do they help us understand how MI is involved in volitional motor control? Perhaps what is required is a new conceptual framework that re-focuses the experimental problem back on processes of control. Specifically, optimal feedback control has been proposed as a theory of the volitional motor system and it is argued here that it provides a rich, new perspective for addressing the role of MI and other brain regions in volitional motor control.

Current approaches and future directions to understanding control of head movement

This chapter reviews four key issues that must be addressed to advance our knowledge of control of head movement by the central nervous system (CNS). (1) Researchers must consider how the CNS utilizes the multiple muscle patterns that can produce the same head movement in carrying out tasks in an optimal way. (2) More attention must be paid to the dynamics of neck muscle activation that are required to implement head movements and show they are produced by CNS circuits. (3) Research is required to determine how the multiple pathways that impinge upon neck motor centers are utilized in a variety of tasks including eye-head gaze shifts, smooth head tracking, head stabilization and manipulating objects with the head. These pathways include corticospinal, vestibulospinal, reticulospinal (three subdivisions), fastigiospinal, tectospinal and interstitiospinal tracts. (4) Further analysis is needed to understand how vestibular signals are modulated during each of the above-mentioned tasks. This ambitious agenda is justified by the fact that the head-neck motor system is an ideal model for understanding issues of complex motor control.

Cerebral cortical mechanisms of reaching movements

Because reaching movements have a clear objective--to bring the hand to the spatial location of an object--they are well suited to study how the central nervous system plans a purposeful act from sensory input to motor output. Most models of movement planning propose a serial hierarchy of analytic steps. However, the central nervous system is organized into densely interconnected populations of neurons. This paradox between the apparent serial order of central nervous system function and its complex internal organization is strikingly demonstrated by recent behavioral, modeling, and neurophysiological studies of reaching movements.