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The output from the preamplifier should be fed to a blanking device in order to eliminate the stimulation artifact. The obtained integrated signal can be used as a trigger (switch), or as an input to a state machine. A state control relying on a multi-threshold detection could be used to allow the user to vary the strength of the grasp or select other modalities of grasping. The alternatives to surface electrodes are implantable electrodes or fully implantable systems. The system comprised several intramuscular electrodes that were positioned by the hypodermal needles. The stimulation patterns were "cloned" from the muscle activities recorded during voluntary grasping movements of able-bodied subjects. The stimulation sequences were triggered with a push button or a pneumatic pressure sensor. The stimulation sequences that are used to generate both palmar and lateral grasps are individually tuned and are preprogrammed in the form of a "muscle contraction map. The position sensor monitors two axes of shoulder motion, protraction/retraction and elevation/depression. Typically, the protraction/retraction motion of the shoulder is used as a proportional signal for hand opening and closing. The shoulder elevation/depression motion is used to generate logic commands that are used to establish a zero level for the protraction/retraction command and to "freeze" the stimulation levels until the next logic command is issued. An additional switch is also provided to allow a user to choose between palmar and lateral grasp strategies. Besides this sensor configuration, the Freehand system also allows one to use either external or implanted transducer mounted on ipsilateral wrist. This transducer measures the dorsal/volar flexion of the wrist and uses this motion to control hand opening and closing in a way similar to the shoulder position sensor [34,35]. The output of the shoulder or the wrist sensor is sent to an external control unit that generates an appropriate stimulation sequence for each stimulation electrode. This sequence is then sent via an inductive link to an implanted stimulator that generates the stimulation trains for each implanted stimulation electrode. More than 250 tetraplegic subjects have received the Freehand neural prosthesis at more than a dozen sites around the world. The system was designed to combine a fully implantable grasping system with some additional channels to control elbow extension, flexion, and shoulder movements. The device is driven and controlled by an external unit that transmits energy and control signals to the implantable unit. The control has been implemented using a fully implantable Freehand grasping system. The walking assist comprises six channels that stimulate hip and knee extensors and withdrawal reflex bilaterally. The Actigait is an implantable foot-drop preventor for poststroke hemiplegic patients. The picture shows the cuff electrode that selectively stimulates the peroneal nerve connected to the implantable two-channel stimulator (left side of the image). The right side of the image shows the external unit with the cooper winding (transmitting antenna). The first demonstrated application of this technique was in hemiplegic patients [52] following the patent and research of Liberson [53]. The stimulation was applied to ankle dorsiflexors so the "foot-drop" can be eliminated. Single- and dual-channel correcting foot-drop is now a regular clinical treatment in some rehabilitation institution [58]. The Actigait uses a cuff electrode to selectively stimulate peroneal nerve; thereby, effectively controlling the foot-drop and assisting the paretic limb during the swing. The heel switch or, potentially, the implantable sensor that records activity from a sensory nerve innervating the lateral side of the foot [59] triggers the dorsiflexion by means of a multichannel cuff electrode (see Figure 70.

But what is pertinent here is that both agree on this: A goal of perception is to match or approximate true properties of an objective physical environment. This hypothesis is widespread and rarely questioned in the scientific study of perception. For instance, Stoffregen and Bardy (2001) state: We analyze three hypotheses about relations between ambient arrays and physical reality: (1) that there is an ambiguous relation between ambient energy arrays and physical reality, (2) that there is a unique relation between individual energy arrays and physical reality, and (3) that there is a redundant but unambiguous relation, within or across arrays, between energy arrays and physical reality. The first hypothesis is endorsed by indirect theories, and the second by some direct theories. They conclude in favor of the third hypothesis, viewing it as an extension of standard direct theories. Nowhere do they question the assumption of faithful depiction that is shared by all three; nor do any of the more than 30 commentaries on their article. They reject, for instance, "the seemingly sensible idea that the purpose of vision Conscious Realism and the Mind-Body Problem 93 is to perceive the world as it is. They simply recommend rejecting a version of the hypothesis that focuses exclusively on the present stimulus and the present state of the physical world. The purpose of vision is to perceive the world, not just as it is, but as it has been. Our perceptual systems do not try to approximate properties of an objective physical world. I propose that perception is like a multimodal user interface (Hoffman 1998, 2003). A successful user interface does not, in general, resemble 94 Hoffman what it represents. Because it simplifies, rather than resembles, a user interface usefully and swiftly informs the actions of the user. The features in an interface usually differ from those in the represented domain, with no loss of effectiveness. A perceptual user interface, simplifying and reformatting for the niche of an organism, gives that organism an adaptive advantage over one encumbered with constructing a complex approximation to the objective world. The race is to the swift; a user interface makes one swift by not resembling the world. This is not what textbooks or most perceptual experts say and therefore invites spelling out. You find the icon for the file, click on it with your mouse, drag it to the recycle-bin icon, and release. Of course what goes on behind the icons is quite complex: A central processor containing millions of transistors executes binary commands encoded as voltages in megabytes of memory, and directs the head on a hard drive to change the magnetic structure of a disk revolving thousands of times per minute. Fortunately, to delete a file you do not need to know anything about this complexity. The icons, and the entire graphical-windows interface, are designed to help the user by hiding the complexity of the computer (see. The windows interface and its contents are designed not to resemble the actual complexity of the computer and its inner workings, but instead to present needed information to the user in a format that is friendly, i. Although the actual file in the computer is a complex array of voltages and magnetic fields with no simple geometry, the file icon is a rectangle because this is a simple symbol easily interpreted by human users. Few souls delight to search the guts of a computer with voltmeter and magnetometer to find a file. But nothing about the file itself, the voltages and magnetic fields inside the computer, is blue. The color of the icon is not intended to resemble anything about the file but simply to indicate, say, what kind of file it Conscious Realism and the Mind-Body Problem 95 is or how recently it was modified. But this does not mean that the file itself is in the upper right of the computer. The location of an icon on the display is, in part, simply a convenient way to keep track of it.

Reflections on the treatment of brain injured patients suffering from problem-solving disorders. Dense amnesia in a professional musician following Herpes Simplex Virus encephalitis. Reducing everyday memory and planning problems by means of a paging system: a randomised control crossover study. The Oliver Zangwill Centre for Neuropsychological Rehabilitation: a partnership between health care and rehabilitation research. A practical framework for understanding compensatory behaviour in people with organic memory impairment. Previous to that, scaled-down versions of adult assessments were often the only available measures, or tests specifically constructed for research purposes, both of which were often unsuitable for use with a clinical population of children. Observations of how children complete (or fail) memory and learning tasks, and perform generally. A second important point to remember is that young children rarely complain of memory problems themselves if the problem is acquired in infancy, unlike older children (and adults) who have an acquired memory problem, or adults who may know that their memory is not what it used to be. While normal children will be able to discuss memory from a young age (for further discussion, see Joyner & Kurtz-Costes, 1997) their metamemory or understanding that they have memory, that they are able to forget and that they are able to know that they have forgotten what they have known at one time, does not develop until later childhood. Indeed, even older children whose memory may have been poor for a number of years may not know that they have a problem, never having been consciously aware that they did not have a fully functioning memory. Generally it is parents or teachers who first raise concerns about problems of memory in children. This is again a major difference with adults, who will have had the experience of a normal memory for many years. Even if they have lost insight into their problem, their family and associates will notice a loss or difference in them which gives rise to concern. With young children there may be little against which to compare their present memory capacity or processing, so there may not be a sense of loss of memory function, but rather a failure to make progress as expected over time. In older children the loss of memory may be a presenting problem, depending on the aetiology. Consequently, it is sufficient to say here that damage to the temporal lobes, hippocampus, amygdala, mammillary bodies, thalamus and frontal lobes, those parts of the brain mostly commonly associated with memory problems, may broadly arise from necrosis, injury, inflammation (meningitis and encephalitis), atrophy (following febrile convulsions in childhood), oedema, cerebral haemorrhage, tumours (whether malignant or benign), calcification [e. In addition, there may be developmental disorders where the brain is malformed and consequently memory and learning are compromised. However, a number of case histories highlight that children may present with specific memory problems in the absence of any definite neuropathology. As part of a good clinical assessment, it is important to consider whether there may be psychological causes. Children who are anxious and depressed may be preoccupied with their own concerns and present with complaints about their distractedness, failure to learn and frequent forgetting, both at home and in school. Careful clinical interviews of the child and family are needed to exclude such issues. Lack of motivation and cooperation in the assessment may also be the cause of test failure. As the chapter on the development of memory will have illustrated (Chapter 24, this volume), infants are not born with a fully developed memory system or memory capacity, and will not have the same complexity for encoding, storing and retrieving information at birth that is present in adults. In addition, the means that we have to assess memory in preverbal children and in children with developing but immature language systems is consequently compromised and different paradigms need to be employed. Comparisons between assessments at different ages may give unexpected results if their memory capacity, speed of processing and age-appropriate strategies are not understood. Younger children are more vulnerable than older children, however the injury is acquired. Fletcher & Taylor (1984) propose a developmental model of assessment whereby the starting point is exploration of the presenting or manifest behaviour, rather than the neuropathology. They support a model in which assessment of the specific processing difficulties is based on the manifest problems within a wider assessment of ability. Delayed language development will mean that test instructions and test content may not be understood, even in nonverbal memory tests. Failure to focus attention on test materials, distractibility and short attention span may also result in deviant memory test scores.

Currently, neural network control systems lack the type of theoretical foundation upon which linear control systems are based, but recently 12-8 Biomedical Engineering Fundamentals there have been some promising theoretical developments. In addition, there are numerous examples of successful engineering applications of neural networks to attest to the utility of these techniques. Defining Terms Backpropagation: A technique used to determine the training signal used for adjusting the weights of a given neuron in a neural network. Learning algorithm: An algorithm used to update the synaptic weights in a neural network. Neural network: A term used to refer to a broad class of computational algorithms that are loosely based on models of the nervous system. Reinforcement learning algorithms: Learning algorithms that utilize a system performance measure (that may or may not have a direct, known relationship to output error of the neural network) as a training signal for the neural network. Supervised learning algorithms: Learning algorithms often used in neural networks that use the output error of the neural network as a training signal. Synaptic weight: A scaling factor on the signal from one neuron in a network to another. Experimental evaluation of an adaptive feedforward controller for use in functional neuromuscular stimulation systems. Intelligence as Adaptive Behavior: An Experiment in Computational Neuroethology, Academic Press, Boston. Biological Neural Networks in Invertebrate Neuroethology and Robotics, Academic Press, New York. Adaptive control of arterial blood pressure with a learning controller based on multilayer neural networks. Comparative nonlinear modeling of renal autoregulation in rats: volterra approach versus artificial neural networks. Use of evolutionary programming in the design of neural networks for artifact detection. An adaptive sensorimotor network inspired by the anatomy and physiology of the cerebellum. A hierarchical neural-network model for control and learning of voluntary movement. Neural network generation of muscle stimulation patterns for control of arm movements. Real-time dynamic control of an industrial manipulator using a neural-network-based learning controller. Feedback-error-learning neural network for trajectory control of a robotic manipulator. Neural networks and nonlinear adaptive filtering: unifying concepts and new algorithms. An analytical comparison of a neural network and a model-based adaptive controller. Recurrent backpropagation and the dynamical approach to adaptive neural computation. Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches, Van Nostrand Reinhold, New York. Further Information Very good introductions to the operation of neural networks are given in Neural Network Design by Hagan, Demuth, and Beale and in Artificial Neural Systems by J. Detailed descriptions of the neural network control systems described in this chapter are provided in Neural Network Control Systems edited by Miller, Sutton, and Werbos and the Handbook of Intelligent Control edited by White and Sofge. Marmarelis University of Southern California the problem of system identification in physiology derives its importance from the need to acquire quantitative models of physiologic function (from the subcellular to the organ system level) by use of experimental observations (data). Quantitative models can be viewed as summaries of experimental observations that allow scientific inference and organize our knowledge regarding the functional properties of physiologic systems. System identification is the process by which the system model is determined from data. This modeling and identification problem is rather challenging in the general use of a physiologic system, where insufficient knowledge about the internal workings of the system or its usually confounding complexity prevents the development of an explicit model.

Stimulus S1, the largest, caused the transmembrane potential to rise from its baseline value to about -65 mV and initiated an action potential (ap1), which followed a few milliseconds after the stimulus. Stimulus S2 had only 80% of the intensity of S1, and the stimulus itself caused a transmembrane voltage change that was proportionally smaller. As did S1, stimulus S2 initiated an action potential (ap2), although there was a delay of about 200 msec before excitation occurred, and the action potential itself was somewhat diminished in amplitude. Stimulus S3 had 80% of the magnitude of S2 and caused a transmembrane voltage change that again was proportionally smaller. That is, stimuli producing transmembrane voltages above a threshold value initiate action potentials, while those below do not. The threshold value for a particular stimulus varies depending on factors such as the location of the electrodes relative to the tissue that is excited, the stimulus duration, and the amount of membrane affected. In different trials, three stimuli (S1, S2, S3) of decreasing magnitude were applied. Subsequently, action potentials may be observed at sites progressively further away. Propagating current loops are of fundamental importance to nerve or muscle function, as they allow for the transmission of information in nerves or initiation of contraction through the release of calcium, in muscle. The deterioration of propagation with disease may be in the form of a failure to propagate, or propagation with an abnormally slow velocity. In the heart, propagation along a closed path ("re-entrant propagation") is one source of cardiac disrhythmias and even death. The theory of the electric telegraph as put forward by Kelvin was perhaps the origin of the core-conductor model, which forms much of the analytical basis for the present understanding of propagation. While experimentally based, such models have become increasingly precise, increasingly specialized, and much more detailed so as to reflect accurately the properties of particular tissues [e. Such numerical models allow a consistent picture to be obtained of action potentials at different sites along a nerve or muscle structure. They are based on analyzing the interaction of spatially distributed events at one time, so as to see the consequences at a short time later. The fiber is surrounded by an extracellular volume extending 3a beyond the membrane. To establish a symmetric reference potential, the outside edge of the extracellular region was connected via resistances to a junction where e = 0. Solutions for intracellular, extracellular, and transmembrane potentials were obtained through a process of numerical simulation. Waveforms are shown following a transmembrane stimulus at z = 0, where the first action potential originates, and also for sites 3a 2a Extracellular Intracellular 0. These nodes are portrayed by the columns of circles drawn at four axial positions, although actually there were 101 columns. The innermost (lower) row of nodes, row 0, were sites where intracellular potentials were determined; four surrounding rows (rows 1 to 4), at increasing distances, were sites for extracellular potentials. Vm (t) at z = 10 has a larger peak-to-peak rise because excitation terminates there, at the end of the fiber. All the action potentials have similar wave shapes, differing mainly in their timing. Note also that the waveform at z = 0 has progressed through excitation, but not into the plateau phase, by the time of excitation at z = 1 cm. Secondary changes of action potential wave shape as a function of z, other than the stimulus artefact, can be discerned mainly in the baseline as it rises into the upstroke, a region called the foot. Differences in the duration of the foot reflect the time that other tissue has been excited, prior to the start of excitation at a particular site; that is, the foot is longer at sites further down the fiber. In the former categories, energy used at the source creates a disturbance that propagates to the observer through an intervening passive medium. In bioelectric propagation, what is meant is that the source itself is moving down an excitable medium, the membrane, in a process more similar to setting off a chain of firecrackers. The velocities associated with bioelectric propagation relate to the magnitudes of transmembrane current passing through one site on the membrane and flowing to another and how fast the active responses of the membrane occur at the downstream sites.