A solution to the tag-assignment problem for neural 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.

Analyzing vision at the complexity level

The general problem of visual search can be shown to be computationally intractable in a formal, complexity-theoretic sense, yet visual search is extensively involved in everyday perception, and biological systems manage to perform it remarkably well. Complexity level analysis may resolve this contradiction. Visual search can be reshaped into tractability through approximations and by optimizing the resources devoted to visual processing. Architectural constraints can be derived using the minimum cost principle to rule out a large class of potential solutions. The evidence speaks strongly against bottom-up approaches to vision. In particular, the constraints suggest an attentional mechanism that exploits knowledge of the specific problem being solved. This analysis of visual search performance in terms of attentional influences on visual information processing and complexity satisfaction allows a large body of neurophysiological and psychological evidence to be tied together.

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.

Functional specialization in the lower and upper visual fields in humans: Its ecological origins and neurophysiological implications

Functional specialization in the lower and upper visual fields in humans is analyzed in relation to the origins of the primate visual system. Processing differences between the vertical hemifields are related to the distinction between near (peripersonal) and far (extrapersonal) space, which are biased toward the lower and upper visual fields, respectively. Nonlinear/global processing is required in the lower visual field in order to pergeive the optically degraded and diplopic images in near vision, whereas objects in far vision are searched for and recognized primarily using linear/local perceptual mechanisms. The functional differences between near and far visual space are correlated with their disproportionate representations in the dorsal and ventral divisions of visual association cortex, respectively, and in the magnocellular and parvocellular pathways that project to them. Advances in far visual capabilities and forelimb manipulatory skills may have led to a significant enhancement of these functional specializations.

What connectionist models learn: Learning and representation in connectionist networks

Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models of memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities (including “distributed representations”) or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks.

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.

Is there a role for extraretinal factors in the maintenance of stability in a structured environment?

The calibration solution to the stability of the world despite eye movements depends, according to Bridgeman et al., upon a combination of three factors which presumably all need to operate to achieve the goal of stability. Although the authors admit (sect. 4.3, para. 5) that the relative contributions of retinal and extraretinal factors will depend on the particular viewing situation, Figure 5 (sect. 4.3) makes it clear in its representation that the role of perceptual factors is relatively minor compared to extraretinal ones. It is with this representation that this commentary wishes to take issue, believing that it occurs as a result of some assumptions about terminology that may be ambiguous, as well as some misconceptions about the circumstances in which there is a need for stability.

Temporal representation in the control of movement

Theories of the representation of specific kinetic and spatiotem-poral features of movement range from the explicit assertion that temporal aspects of movement are not represented (Kugler et al. 1980) to the idea that they are represented and that they have neurophysiological correlates (Ivry & Corcos 1993; Ivry & Keele 1989). Jeannerod's thesis is that mental and visual images have common mechanisms and that there is a link between the image to move and the mechanisms involved with movement. The target article takes the position that certain parameters are coded in motor representations (sect. 4) but that the duration of an action is not one of them. This position is based on the work of Gottlieb et al. (1989b) and of Decety et al. (1989). Both these studies are worth considering in detail. In Note 1, Jeannerod suggests that: “in time-constrained tasks subjects control the amplitude parameter of force impulses, whereas in spatially constrained tasks the duration of the force impulse is affected by accuracy demands.” This is not exactly correct. Excitation pulse intensity (amplitude) is modulated both in tasks that require spatial and those that require temporal accuracy. Excitation pulse duration is modulated for changes in movement distance and inertial load. If subjects are required to be very accurate spatially, they will move at less than maximum speed for a given distance and this is achieved by lower levels of excitation intensity (Gottlieb et al. 1990).