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Simulation experiments in bionics: A regulative methodological perspective

Edoardo Datteri

Biology and Philosophy 24 (3):301-324 (2009)

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The Epistemic Value of Brain–Machine Systems for the Study of the Brain.Edoardo Datteri - 2017 - Minds and Machines 27 (2):287-313.details Bionic systems, connecting biological tissues with computer or robotic devices through brain–machine interfaces, can be used in various ways to discover biological mechanisms. In this article I outline and discuss a “stimulation-connection” bionics-supported methodology for the study of the brain, and compare it with other epistemic uses of bionic systems described in the literature. This methododology differs from the “synthetic”, simulative method often followed in theoretically driven Artificial Intelligence and cognitive science, even though it involves machine models of biological systems. (...) Download Export citation Bookmark 3 citations
Extending, changing, and explaining the brain.Mazviita Chirimuuta - 2013 - Biology and Philosophy 28 (4):613-638.details This paper addresses concerns raised recently by Datteri (Biol Philos 24:301–324, 2009) and Craver (Philos Sci 77(5):840–851, 2010) about the use of brain-extending prosthetics in experimental neuroscience. Since the operation of the implant induces plastic changes in neural circuits, it is reasonable to worry that operational knowledge of the hybrid system will not be an accurate basis for generalisation when modelling the unextended brain. I argue, however, that Datteri’s no-plasticity constraint unwittingly rules out numerous experimental paradigms in behavioural and systems (...) Download Export citation Bookmark 5 citations
Analyzing the Explanatory Power of Bionic Systems With the Minimal Cognitive Grid.Antonio Lieto - 2022 - Frontiers in Robotics and AI 9.details 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 (...) Download Export citation Bookmark
Prediction versus understanding in computationally enhanced neuroscience.Mazviita Chirimuuta - 2020 - Synthese 199 (1-2):767-790.details 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 (...) Download Export citation Bookmark 9 citations

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