System Identification of Neural Systems: If We Got It Right, Would We Know?

Yena Han, Tomaso A Poggio, Brian Cheung
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:12430-12444, 2023.

Abstract

Artificial neural networks are being proposed as models of parts of the brain. The networks are compared to recordings of biological neurons, and good performance in reproducing neural responses is considered to support the model’s validity. A key question is how much this system identification approach tells us about brain computation. Does it validate one model architecture over another? We evaluate the most commonly used comparison techniques, such as a linear encoding model and centered kernel alignment, to correctly identify a model by replacing brain recordings with known ground truth models. System identification performance is quite variable; it also depends significantly on factors independent of the ground truth architecture, such as stimuli images. In addition, we show the limitations of using functional similarity scores in identifying higher-level architectural motifs.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-han23d, title = {System Identification of Neural Systems: If We Got It Right, Would We Know?}, author = {Han, Yena and Poggio, Tomaso A and Cheung, Brian}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {12430--12444}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/han23d/han23d.pdf}, url = {https://proceedings.mlr.press/v202/han23d.html}, abstract = {Artificial neural networks are being proposed as models of parts of the brain. The networks are compared to recordings of biological neurons, and good performance in reproducing neural responses is considered to support the model’s validity. A key question is how much this system identification approach tells us about brain computation. Does it validate one model architecture over another? We evaluate the most commonly used comparison techniques, such as a linear encoding model and centered kernel alignment, to correctly identify a model by replacing brain recordings with known ground truth models. System identification performance is quite variable; it also depends significantly on factors independent of the ground truth architecture, such as stimuli images. In addition, we show the limitations of using functional similarity scores in identifying higher-level architectural motifs.} }
Endnote
%0 Conference Paper %T System Identification of Neural Systems: If We Got It Right, Would We Know? %A Yena Han %A Tomaso A Poggio %A Brian Cheung %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-han23d %I PMLR %P 12430--12444 %U https://proceedings.mlr.press/v202/han23d.html %V 202 %X Artificial neural networks are being proposed as models of parts of the brain. The networks are compared to recordings of biological neurons, and good performance in reproducing neural responses is considered to support the model’s validity. A key question is how much this system identification approach tells us about brain computation. Does it validate one model architecture over another? We evaluate the most commonly used comparison techniques, such as a linear encoding model and centered kernel alignment, to correctly identify a model by replacing brain recordings with known ground truth models. System identification performance is quite variable; it also depends significantly on factors independent of the ground truth architecture, such as stimuli images. In addition, we show the limitations of using functional similarity scores in identifying higher-level architectural motifs.
APA
Han, Y., Poggio, T.A. & Cheung, B.. (2023). System Identification of Neural Systems: If We Got It Right, Would We Know?. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:12430-12444 Available from https://proceedings.mlr.press/v202/han23d.html.

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