| Paper: | PS-2B.74 | ||
| Session: | Poster Session 2B | ||
| Location: | H Fläche 1.OG | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | An overview of functional alignment in artificial and biological neural networks: Current recommendations and open questions | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1187-0 | ||
| Authors: | Elizabeth DuPre, Jean-Baptiste Poline, McGill University, Canada | ||
| Abstract: | Functional alignment is a method for finding similarity in functional representations of both biological and artificial neural networks. Although it is actively developed in cognitive neuroscience and deep learning, each field prefers its own terminology for and variants of this method. There is, therefore, relatively little cross talk between the two spaces. In this brief review, we highlight three functional alignment methods successfully used in both fields: canonical correlation analysis, Procrustes analysis, and shared response modelling. We consider the relative strengths of each method and highlight situations in which each may be most appropriate. We conclude with open questions in functional alignment that may serve as collaborative opportunities for cognitive neuroscience and deep learning. | ||