Illari, Phyllis Kirstin;
Grujičić, Bojana;
(2025)
Using Deep Neural Networks and Similarity Metrics to Predict and Control Brain Responses.
In: Illari, Phyllis and Russo, Federica, (eds.)
Routledge Handbook of Causality and Causal Methods.
Routledge
(In press).
![[thumbnail of Chapter 28 [Part V]]](https://discovery.ucl.ac.uk/style/images/fileicons/text.png) |
Text (Chapter 28 [Part V])
Illari_Using deep neural networks and similarity metrics to predict and control brain responses_AAM_chapter.pdf Access restricted to UCL open access staff Download (272kB) |
| Type: | Book chapter |
|---|---|
| Title: | Using Deep Neural Networks and Similarity Metrics to Predict and Control Brain Responses |
| ISBN-13: | 9781032260198 |
| Publisher version: | https://www.routledge.com/The-Routledge-Handbook-o... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Science and Technology Studies |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10188092 |
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