Mohamed N. Abdelghani

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BACKGROUND To learn, a motor system needs to know its sensitivity derivatives, which quantify how its neural commands affect motor error. But are these derivatives themselves learned, or are they known solely innately? Here we test a recent theory that the brain's estimates of sensitivity derivatives are revisable based on sensory feedback. In its simplest(More)
OBJECTIVES Brain stimulation techniques are non-pharmacologic strategies which offer additional therapeutic options for treatment-resistant depression (TRD). The purpose of this paper is to review the current literature regarding the use of brain stimulation in resistant bipolar disorder (BD), with particular reference to hypomanic/manic symptoms. METHODS(More)
The predictive nature of the primate sensorimotor systems, for example the smooth pursuit system and their ability to compensate for long delays have been proven by many physiological experiments. However, few theoretical models have tried to explain these facts comprehensively. Here, we propose a sensorimotor learning and control model that can be used to(More)
Citation: Abdelghani M and Melnikov A (2015) On macrohedging problem in semimartingale markets. Macrohedging is a hedging technique commonly used in practice. It allows one to find a hedging policy that offsets several underlying risk factors of a portfolio of assets as a whole. Here, we develop a macrohedging methodology in a general semimartingale market.(More)
Decoding motor intent from recorded neural signals is essential for the development of effective neural-controlled prostheses. To facilitate the development of online decoding algorithms we have developed a software platform to simulate neural motor signals recorded with peripheral nerve electrodes, such as longitudinal intrafascicular electrodes (LIFEs).(More)
Signals recorded from peripheral nerves may provide an effective and reliable means of controlling powered prosthetic limbs. Longitudinal intrafascicular electrodes (LIFEs) have been used to record extracellular motor activity from peripheral nerves in upper-limb amputees for periods up to several weeks and the ability to decode the activity and use it for(More)
In control theory, variables called sensitivity derivatives quantify how a system's performance depends on the commands from its controller. Knowledge of these derivatives is a prerequisite for adaptive control, including sen-sorimotor learning in the brain, but no one has explained how the derivatives themselves could be learned by real neural networks,(More)
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