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date: 22 September 2017

Abstract and Keywords

The sense of touch and related haptic capabilities are essential for skilled interaction with our environment. More generally, the study of these capabilities offers a unique window into key features of brain function. Paradoxically, such capabilities have been much less studied than other senses such as vision and hearing. The computational modeling of haptic capabilities is also a little-explored area. Here a computational approach is presented to model basic aspects of important haptic capabilities. It is proposed that neural spiking signals following a Poisson process can be used to learn the dynamics of a simulated, nonlinear body. More generally, this Poisson learning approach can be extended to situations involving multiple sensory signals (haptic, visual, etc.). This approach also allows linking millisecond-level neural computations to complex behaviors happening at larger time scales. Finally, the realism and virtuality of an environment are discussed in terms of Poisson learning computations.

Keywords: haptics, internal model, computational modeling, neural computation, Poisson learning

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