4 resultados para Sensory System

em Aston University Research Archive


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Sensory cells usually transmit information to afferent neurons via chemical synapses, in which the level of noise is dependent on an applied stimulus. Taking into account such dependence, we model a sensory system as an array of LIF neurons with a common signal. We show that information transmission is enhanced by a nonzero level of noise. Moreover, we demonstrate a phenomenon similar to suprathreshold stochastic resonance with additive noise. We remark that many properties of information transmission found for the LIF neurons was predicted by us before with simple binary units [Phys. Rev. E 75, 021121 (2007)]. This confirmation of our predictions allows us to point out identical roots of the phenomena found in the simple threshold systems and more complex LIF neurons.

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This thesis described the research carried out on the development of a novel hardwired tactile sensing system tailored for the application of a next generation of surgical robotic and clinical devices, namely a steerable endoscope with tactile feedback, and a surface plate for patient posture and balance. Two case studies are examined. The first is a one-dimensional sensor for the steerable endoscope retrieving shape and ‘touch’ information. The second is a two-dimensional surface which interprets the three-dimensional motion of a contacting moving load. This research can be used to retrieve information from a distributive tactile sensing surface of a different configuration, and can interpret dynamic and static disturbances. This novel approach to sensing has the potential to discriminate contact and palpation in minimal invasive surgery (MIS) tools, and posture and balance in patients. The hardwired technology uses an embedded system based on Field Programmable Gate Arrays (FPGA) as the platform to perform the sensory signal processing part in real time. High speed robust operation is an advantage from this system leading to versatile application involving dynamic real time interpretation as described in this research. In this research the sensory signal processing uses neural networks to derive information from input pattern from the contacting surface. Three neural network architectures namely single, multiple and cascaded were introduced in an attempt to find the optimum solution for discrimination of the contacting outputs. These architectures were modelled and implemented into the FPGA. With the recent introduction of modern digital design flows and synthesis tools that essentially take a high-level sensory processing behaviour specification for a design, fast prototyping of the neural network function can be achieved easily. This thesis outlines the challenge of the implementations and verifications of the performances.

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The 19 channel Neuromagnetometer system in the Clinical Neurophysiology Unit at Aston University is a multi-channel system, unique in the United Kingdom. A bite bar head localisation and MRI co-registration strategy which enabled accurate and reproducible localisation of MEG data into cortical space was developed. This afforded the opportunity to study magnetic fields of the human cortex generated by stimulation of peripheral nerve, by stimulation of visceral sensory receptors and by those evoked through voluntary finger movement. Initially, a study of sensory-motor evoked data was performed in a healthy control population. The techniques developed were then applied to patients who were to undergo neurosurgical intervention for the treatment of epilepsy and I or space occupying lesions. This enabled both validation of the effective accuracy of source localisation using MEG as well as to determine the clinical value of MEG in presurgical assessment of functional localisation in human cortex. The studies in this thesis have demonstrated that MEG can repeatedly and reliably locate sources contained within a single gyrus and thus potentially differentiate between disparate gyral activation. This ability is critical in the clinical application of any functional imaging technique; which is yet to be fully validated by any other 'non-invasive' functional imaging methodology. The technique was also applied to the study of visceral sensory representation in the cortex which yielded important data about the multiple cortical representation of visceral sensory function.

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This article describes a surgical robotic device that is able to discriminate tissue interfaces and other controlling parameters ahead of the drill tip. The advantage in such a surgery is that the tissues at the interfaces can be preserved. A smart tool detects ahead of the tool point and is able to control the interaction with respect to the flexing tissue, to avoid penetration or to control the extent of protrusion with respect to the position of the tissue. For surgical procedures, where precision is required, the tool offers significant benefit. To interpret the drilling conditions and the conditions leading up to breakthrough at a tissue interface, a sensing scheme is used that discriminates between the variety of conditions posed in the drilling environment. The result is a fully autonomous system, which is able to respond to the tissue type, behaviour, and deflection in real-time. The system is also robust in terms of disturbances encountered in the operating theatre. The device is pragmatic. It is intuitive to use, efficient to set up, and uses standard drill bits. The micro-drill, which has been used to prepare cochleostomies in the theatre, was used to remove the bone tissue leaving the endosteal membrane intact. This has enabled the preservation of sterility and the drilling debris to be removed prior to the insertion of the electrode. It is expected that this technique will promote the preservation of hearing and reduce the possibility of complications. The article describes the device (including simulated drill progress and hardware set-up) and the stages leading up to its use in the theatre. © 2010 Authors.