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How neural is a neural net? Bio-inspired computational models and their impact on the multiple realization debate

Hardalupas, Mahi (2021) How neural is a neural net? Bio-inspired computational models and their impact on the multiple realization debate. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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My dissertation introduces a new account of multiple realization called ‘engineered multiple realization’ and applies it to cases of artificial intelligence research in computational neuroscience. Multiple realization has had an illustrious philosophical history, where multiple realization is when a higher-level (psychological) kind can be realized by several different lower-level (physical) kinds. There are two threads in the multiple realization literature: one situated in philosophy of mind and the other in philosophy of science. In philosophy of mind, multiple realization is typically seen as arbitrating a debate between metaphysical accounts of the mind, namely functionalism and identity theory. Philosophers of science look to how multiple realization is connected to scientific practice, but many question what it is useful for outside of philosophy of mind.

My dissertation addresses this gap by drawing on cases from machine learning and computational neuroscience to show there is a useful form of multiple realization based on engineering practice. It differs from previous accounts in three ways. First, it reintroduces the link between engineering and multiple realization, which has been mostly neglected in current debates. Second, it is explicitly perspectival, where what counts as multiple realization depends on your perspective. Third, it locates the utility of engineered multiple realization in its ability to support constraint-based reasoning in science. This provides an answer to concerns about the utility of multiple realization in the philosophy of science literature and explains how deep neural networks can provide understanding of the brain. The first half of this dissertation proposes my account of Engineered Multiple Realization and applies it to scientific cases. The second half considers implications and connections to the modelling literature.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Hardalupas, Mahimchardalupas@gmail.commch640000-0002-4745-6799
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee CoChairAllen,
Committee CoChairChirimuuta,
Committee MemberMitchell,
Committee MemberDanks,
Committee MemberPlaut,
Date: 3 May 2021
Date Type: Publication
Defense Date: 6 April 2021
Approval Date: 3 May 2021
Submission Date: 8 April 2021
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 140
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > History and Philosophy of Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: history and philosophy of science, multiple realization, bio-inspiration, deep learning, computational neuroscience
Date Deposited: 03 May 2021 15:12
Last Modified: 03 May 2021 15:12

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