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• W2895556287 abstract "The surface of the body is invested with populations of mechanosensory neurons and receptors which balance performance and efficiency to provide relevant input on stimuli such as touch and movement. While it might be biologically possible to generate dense arrays of sensors all over the body and take in vast amounts of information, the mechanosensory system has adapted with function, resulting in more sensors where they are needed and in sensors that extract key features of stimulus input. Such adaptations can be illustrated with touch sensation and the two-point discrimination test. In areas of the body that need fine discrimination ability—our fingertips, for example—two points touching the skin can be perceived as distinct at a very small separation distance due to a high number of mechanosensory neurons innervating the region. In areas of our skin that are not generally used to resolve fine surface features, such as the outer area of the upper arm, we may perceive similarly spaced touches as one stimulus due to lower numbers of mechanosensory neurons in the region. The touch stimulus itself is also filtered and coded as trains of action potentials that reflect relevant characteristics of the stimulus. Inspired by the biological instrumentation of mechanosensory surfaces, Mohren et al. (1) developed and implemented computational approaches that provide insight on the biology and engineering of sparse sensing from wings.Engineered, sensation-enabled structures, like evolved biological ones, have sensory systems that strike a balance between performance and cost. The choice of density and placement of sensors, the type and resolution of information captured from them, and the extent of processing of those data will impact functionality and expense of the device. The ability to obtain the requisite information for function from the minimal number of sensors optimizes the efficiency of design without sacrificing performance.To understand … [↵][1]1Email: mhale{at}uchicago.edu. [1]: #xref-corresp-1-1" @default.
• W2895556287 created "2018-10-12" @default.
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• W2895556287 date "2018-10-02" @default.
• W2895556287 modified "2023-10-17" @default.
• W2895556287 title "Making sense of sparse data with neural encoding strategies" @default.
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• W2895556287 doi "https://doi.org/10.1073/pnas.1814761115" @default.
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