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  1. Analyzing the Explanatory Power of Bionic Systems With the Minimal Cognitive Grid.Antonio Lieto - 2022 - Frontiers in Robotics and AI 9.
    In this article, I argue that the artificial components of hybrid bionic systems do not play a direct explanatory role, i.e., in simulative terms, in the overall context of the systems in which they are embedded in. More precisely, I claim that the internal procedures determining the output of such artificial devices, replacing biological tissues and connected to other biological tissues, cannot be used to directly explain the corresponding mechanisms of the biological component(s) they substitute (and therefore cannot be used (...)
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  • Prediction versus understanding in computationally enhanced neuroscience.Mazviita Chirimuuta - 2020 - Synthese 199 (1-2):767-790.
    The use of machine learning instead of traditional models in neuroscience raises significant questions about the epistemic benefits of the newer methods. I draw on the literature on model intelligibility in the philosophy of science to offer some benchmarks for the interpretability of artificial neural networks used as a predictive tool in neuroscience. Following two case studies on the use of ANN’s to model motor cortex and the visual system, I argue that the benefit of providing the scientist with understanding (...)
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  • Robotic Simulations, Simulations of Robots.Edoardo Datteri & Viola Schiaffonati - 2019 - Minds and Machines 29 (1):109-125.
    Simulation studies have been carried out in robotics for a variety of epistemic and practical purposes. Here it is argued that two broad classes of simulation studies can be identified in robotics research. The first one is exemplified by the use of robotic systems to acquire knowledge on living systems in so-called biorobotics, while the second class of studies is more distinctively connected to cases in which artificial systems are used to acquire knowledge about the behaviour of autonomous mobile robots. (...)
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