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Vibrissa reso- nance tuning curves (gray lines purchase 6.5mg nitroglycerin otc, middle row of boxes) and corresponding neural frequency tuning curves (black lines, top row) are shown for peripheral and cortical recordings. The bottom row shows the corresponding spike traces for trigeminal (NV), fast spiking unit (FSU), regular spiking unit (RSU) and multi-unit activity (MUA). Green horizontal lines indicate the spontaneous firing rate; yellow lines indicate the threshold for significant evoked activity. Note that off-resonance stimuli were unable to evoke a significant increase in neural activity, demonstrating the potential importance of resonance for the amplification of sensory infor- mation. Left and Center Boxes Average neural tuning curves are shown for all four types of neural recording. In the graph on the left, average neural activity was normalized to peak firing rate and centered on the best frequency (BF), the frequency that drove the greatest increase in mean firing rate. On the right, average neural activity was normalized to peak firing rate and centered on the fundamental resonance frequency (FRF), the frequency that drove the greatest increase in the amplitude of vibrissa motion. Right The quality of neural frequency tuning (Qneural) normalized by the quality of vibrissa frequency tuning (Qvibrissa) for all four neural recording types. As seen in the BF- and FRF-centered average tuning curves, RSUs (red curve) and trigeminal neurons (green) demonstrated more refined tuning than FSUs (blue) and MUA (purple) for both averaging approaches. A consistent difference was observed in the specificity of tuning between recorded cell classes. The excitatory neuron classes in the periphery and SI, trigem- inal neurons and RSUs, showed significantly more precise tuning than the FSUs (Figures 2. The greater precision observed in the frequency tuning of RSU responses parallels the tuning precision observed for other features in SI, as RSUs also tend to have more precise spatial tuning and more precise direction-of- deflection representations. Complex Stimuli We also recorded the neural response in a subset of neurons evoked by natural and complex stimuli. As the speed of sandpaper motion was increased, the relative frequency of vibration of the vibrissa tip was correspondingly increased. Larger vibrissa oscillations were observed when the speed of the wheel combined with the prominent spatial frequencies of the sandpaper to drive the vibrissa at its resonance. In accordance, a greater mean firing rate was also observed in this recording when the vibrissa was driven at its resonance.

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Although this diagram is fairly complex discount nitroglycerin 2.5mg without prescription, it is only a small, imperfect and incomplete representation of the entire clinical decision support landscape. The first section focuses on “library-type” applications that enable a clinician to look up information in an electronic document. The second section describes a myriad of “real-time clinical decision support systems”. These systems generally deliver clinical guidance to clini- cians at the point of care within the CIS. The third section describes several “hybrid” systems, which combine aspects of knowledge-based clinical decision support systems with library-type information. Finally, section four looks at various attempts to bring clinical knowledge in the form of computable guidelines to the point of care. Library-Type Applications: Front-Ends to Applications That Directly Interact with Clinicians Bibliographic Databases (DBs) Biomedical bibliographic databases contain on the order of millions of records, each representing a unique scientific journal article that has been published. Each record typically contains the title of the article, the authors, their affiliation(s), and the abstract of the article. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. An Overview of Efforts to Bring Clinical Knowledge to the Point of Care 289 For the last 30 years, the National Library of Medicine has maintained the MedLine database, the most common bibliographic database used in clinical medicine. During that period, various attempts have been made to develop easy to use and reliable interfaces to this vast resource including Grateful Med (Cahan, 1989) and COACH (Kingsland, 1993). PubMed relies on a sophisticated free-text query processor to map freetext user queries to MeSH terms, when appropriate, and returns a highly relevant set of documents. For example, Ovid has developed an interesting MeSH mapper and query expander that has gathered outstanding reviews from highly trained librarians. Knowl- edge Finder has developed a fuzzy mapping algorithm that has also generated some good reviews. Unfortunately, none of these systems consistently enables clinicians to retrieve more than half of all the relevant articles on any particular topic (Hersh, 1998).