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The epistemic value of brain-machine systems for the study of the brain

Datteri, Edoardo (2016) The epistemic value of brain-machine systems for the study of the brain. [Preprint]

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Abstract

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 (neuro)science, even though it involves machine models of biological systems. I also bring the previous analysis to bear on some claims on the epistemic value of bionic technologies made in the recent philosophical literature. I believe that the methodological reflections proposed here may contribute to the piecewise understanding of the many ways bionic technologies can be deployed not only to restore lost sensory-motor functions, but also to discover brain mechanisms.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Datteri, Edoardoedoardo.datteri@unimib.it
Additional Information: Accepted for publication in "Minds and Machine".
Keywords: Prosthetic systems, Bionic systems, Methodology of Artificial Intelligence, Methodology of biorobotics, Philosophy of Cognitive Science
Subjects: Specific Sciences > Cognitive Science
Specific Sciences > Computer Science > Artificial Intelligence
General Issues > Experimentation
Specific Sciences > Biology > Neuroscience
General Issues > Technology
Depositing User: Dr. Edoardo Datteri
Date Deposited: 05 Nov 2016 19:45
Last Modified: 05 Nov 2016 19:45
Item ID: 12598
Subjects: Specific Sciences > Cognitive Science
Specific Sciences > Computer Science > Artificial Intelligence
General Issues > Experimentation
Specific Sciences > Biology > Neuroscience
General Issues > Technology
Date: 2016
URI: http://philsci-archive.pitt.edu/id/eprint/12598

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