A Neural Model of How the Brain Represents and Compares Multi-Digit Numbers: Spatial and Categorical Processes

Both animals and humans are capable of representing and comparing numerical quantities, but only humans seem to have evolved multi-digit place-value number systems. This article develops a neural model, called the Spatial Number Network, or SpaN model, which predicts how these shared numerical capabilities are computed using a spatial representation of number quantities in the Where cortical processing stream, notably the Inferior Parietal Cortex. Multi-digit numerical representations that obey a place-value principle are proposed to arise through learned interactions between categorical language representations in the What cortical processing stream and the Where spatial representation. It is proposed that learned semantic categories that symbolize separate digits, as well as place markers like "tens," "hundreds," "thousands," etc., are associated through learning with the corresponding spatial locations of the Where representation, leading to a place-value number system as an emergent property of What-Where information fusion. The model quantitatively simulates error rates in quantification and numerical comparison tasks, and reaction times for number priming and numerical assessment and comparison tasks. In the Where cortical process, it is proposed that transient responses to inputs are integrated before they activate an ordered spatial map that selectively responds to the number of events in a sequence. Neural mechanisms are defined which give rise to an ordered spatial numerical map ordering and Weber law characteristics as emergent properties. The dynamics of numerical comparison are encoded in activity pattern changes within this spatial map. Such changes cause a "directional comparison wave" whose properties mimic data about numerical comparison. These model mechanisms are variants of neural mechanisms that have elsewhere been used to explain data about motion perception, attention shifts, and target tracking. Thus, the present model suggests how numerical representations may have emerged as specializations of more primitive mechanisms in the cortical Where processing stream. The model's What-Where interactions can explain human psychophysical data, such as error rates and reaction times, about multi-digit (base 10) numerical stimuli, and describe how such a competence can develop through learning. The SpaN model and its explanatory range arc compared with other models of numerical representation.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333

Inertial Load Compensation by a Model Spinal Circuit During Single Joint Movement

Office of Naval Research (N00014-92-J-1309); CONACYT (Mexico) (63462

A Vector-Integration-to-Endpoint Model for Performance of Viapoint Movements

Viapoint (VP) movements are movements to a desired point that are constrained to pass through an intermediate point. Studies have shown that VP movements possess properties, such as smooth curvature around the VP, that are not explicable by treating VP movements as strict concatenations of simpler point-to-point (PTP) movements. Such properties have led some theorists to propose whole-trajectory optimization models, which imply that the entire trajectory is pre-computed before movement initiation. This paper reports new experiments conducted to systematically compare VP with PTP trajectories. Analyses revealed a statistically significant early directional deviation in VP movements but no associated curvature change. An explanation of this effect is offered by extending the Vector-Integration-To-Endpoint (VITE) model (Bullock and Grossberg, 1988), which postulates that voluntary movement trajectories emerge as internal gating signals control the integration of continuously computed vector commands based on the evolving, perceptible difference between desired and actual position variables. The model explains the observed trajectories of VP and PTP movements as emergent properties of a dynamical system that does not precompute entire trajectories before movement initiation. The new model includes a working memory and a stage sensitive to time-to-contact information. These cooperate to control serial performance. The structural and functional relationships proposed in the model are consistent with available data on forebrain physiology and anatomy.Office of Naval Research (N00014-92-J-1309, N00014-93-1-1364, N0014-95-1-0409

Vision Science and Technology at NASA: Results of a Workshop

A broad review is given of vision science and technology within NASA. The subject is defined and its applications in both NASA and the nation at large are noted. A survey of current NASA efforts is given, noting strengths and weaknesses of the NASA program

A Self-Organizing Neural System for Learning to Recognize Textured Scenes

A self-organizing ARTEX model is developed to categorize and classify textured image regions. ARTEX specializes the FACADE model of how the visual cortex sees, and the ART model of how temporal and prefrontal cortices interact with the hippocampal system to learn visual recognition categories and their names. FACADE processing generates a vector of boundary and surface properties, notably texture and brightness properties, by utilizing multi-scale filtering, competition, and diffusive filling-in. Its context-sensitive local measures of textured scenes can be used to recognize scenic properties that gradually change across space, as well a.s abrupt texture boundaries. ART incrementally learns recognition categories that classify FACADE output vectors, class names of these categories, and their probabilities. Top-down expectations within ART encode learned prototypes that pay attention to expected visual features. When novel visual information creates a poor match with the best existing category prototype, a memory search selects a new category with which classify the novel data. ARTEX is compared with psychophysical data, and is benchmarked on classification of natural textures and synthetic aperture radar images. It outperforms state-of-the-art systems that use rule-based, backpropagation, and K-nearest neighbor classifiers.Defense Advanced Research Projects Agency; Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

Motor learning induced neuroplasticity in minimally invasive surgery

Technical skills in surgery have become more complex and challenging to acquire since the introduction of technological aids, particularly in the arena of Minimally Invasive Surgery. Additional challenges posed by reforms to surgical careers and increased public scrutiny, have propelled identification of methods to assess and acquire MIS technical skills. Although validated objective assessments have been developed to assess motor skills requisite for MIS, they poorly understand the development of expertise. Motor skills learning, is indirectly observable, an internal process leading to relative permanent changes in the central nervous system. Advances in functional neuroimaging permit direct interrogation of evolving patterns of brain function associated with motor learning due to the property of neuroplasticity and has been used on surgeons to identify the neural correlates for technical skills acquisition and the impact of new technology. However significant gaps exist in understanding neuroplasticity underlying learning complex bimanual MIS skills. In this thesis the available evidence on applying functional neuroimaging towards assessment and enhancing operative performance in the field of surgery has been synthesized. The purpose of this thesis was to evaluate frontal lobe neuroplasticity associated with learning a complex bimanual MIS skill using functional near-infrared spectroscopy an indirect neuroimaging technique. Laparoscopic suturing and knot-tying a technically challenging bimanual skill is selected to demonstrate learning related reorganisation of cortical behaviour within the frontal lobe by shifts in activation from the prefrontal cortex (PFC) subserving attention to primary and secondary motor centres (premotor cortex, supplementary motor area and primary motor cortex) in which motor sequences are encoded and executed. In the cross-sectional study, participants of varying expertise demonstrate frontal lobe neuroplasticity commensurate with motor learning. The longitudinal study involves tracking evolution in cortical behaviour of novices in response to receipt of eight hours distributed training over a fortnight. Despite novices achieving expert like performance and stabilisation on the technical task, this study demonstrates that novices displayed persistent PFC activity. This study establishes for complex bimanual tasks, that improvements in technical performance do not accompany a reduced reliance in attention to support performance. Finally, least-squares support vector machine is used to classify expertise based on frontal lobe functional connectivity. Findings of this thesis demonstrate the value of interrogating cortical behaviour towards assessing MIS skills development and credentialing.Open Acces

MEART: The Semi-Living Artist

Here, we and others describe an unusual neurorobotic project, a merging of art and science called MEART, the semi-living artist. We built a pneumatically actuated robotic arm to create drawings, as controlled by a living network of neurons from rat cortex grown on a multi-electrode array (MEA). Such embodied cultured networks formed a real-time closed-loop system which could now behave and receive electrical stimulation as feedback on its behavior. We used MEART and simulated embodiments, or animats, to study the network mechanisms that produce adaptive, goal-directed behavior. This approach to neural interfacing will help instruct the design of other hybrid neural-robotic systems we call hybrots. The interfacing technologies and algorithms developed have potential applications in responsive deep brain stimulation systems and for motor prosthetics using sensory components. In a broader context, MEART educates the public about neuroscience, neural interfaces, and robotics. It has paved the way for critical discussions on the future of bio-art and of biotechnology

Evolutionary robotics in high altitude wind energy applications

Recent years have seen the development of wind energy conversion systems that can exploit the superior wind resource that exists at altitudes above current wind turbine technology. One class of these systems incorporates a flying wing tethered to the ground which drives a winch at ground level. The wings often resemble sports kites, being composed of a combination of fabric and stiffening elements. Such wings are subject to load dependent deformation which makes them particularly difficult to model and control. Here we apply the techniques of evolutionary robotics i.e. evolution of neural network controllers using genetic algorithms, to the task of controlling a steerable kite. We introduce a multibody kite simulation that is used in an evolutionary process in which the kite is subject to deformation. We demonstrate how discrete time recurrent neural networks that are evolved to maximise line tension fly the kite in repeated looping trajectories similar to those seen using other methods. We show that these controllers are robust to limited environmental variation but show poor generalisation and occasional failure even after extended evolution. We show that continuous time recurrent neural networks (CTRNNs) can be evolved that are capable of flying appropriate repeated trajectories even when the length of the flying lines are changing. We also show that CTRNNs can be evolved that stabilise kites with a wide range of physical attributes at a given position in the sky, and systematically add noise to the simulated task in order to maximise the transferability of the behaviour to a real world system. We demonstrate how the difficulty of the task must be increased during the evolutionary process to deal with this extreme variability in small increments. We describe the development of a real world testing platform on which the evolved neurocontrollers can be tested

Neuromorphic electronics for real-time biomedical image processing

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