Grujicic, Bojana and Illari, Phyllis (2023) Using deep neural networks and similarity metrics to predict and control brain responses. [Preprint]
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Abstract
In the last ten years there has been an increase in using artificial neural networks to model brain mechanisms, giving rise to a deep learning revolution in neuroscience. This chapter focuses on the ways convolutional deep neural networks (DCNNs) have been used in visual neuroscience. A particular challenge in this developing field is the measurement of similarity between DCNNs and the brain. We survey similarity measures neuroscientists use, and analyse their merit for the goals of causal explanation, prediction and control. In particular, we focus on two recent intervention-based methods of comparing DCNNs and the brain that are based on linear mapping (Bashivan et al., 2019, Sexton and Love, 2022), and analyse whether this is an improvement. While we conclude explanation has not been reached for reasons of underdetermination, progress has been made with regards to prediction and control.
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| Item Type: | Preprint | |||||||||
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| Keywords: | deep neural networks, causality, similarity metric, intervention, explanation, prediction, control | |||||||||
| Subjects: | General Issues > Causation Specific Sciences > Artificial Intelligence General Issues > Evidence General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning Specific Sciences > Neuroscience |
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| Depositing User: | Ms Bojana Grujicic | |||||||||
| Date Deposited: | 25 Nov 2023 16:19 | |||||||||
| Last Modified: | 25 Nov 2023 16:19 | |||||||||
| Item ID: | 22797 | |||||||||
| Subjects: | General Issues > Causation Specific Sciences > Artificial Intelligence General Issues > Evidence General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning Specific Sciences > Neuroscience |
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| Date: | 2023 | |||||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/22797 |
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