Compression-enabled interpretability of voxelwise encoding models.
Voxelwise encoding models based on convolutional neural networks (CNNs) are widely used as predictive models of brain activity evoked by natural movies. Despite their superior predictive performance, the huge number of parameters in CNN-based models have made them difficult to interpret. Here, we in...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-02-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012822 |
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| author | Fatemeh Kamali Amir Abolfazl Suratgar Mohammadbagher Menhaj Reza Abbasi-Asl |
| author_facet | Fatemeh Kamali Amir Abolfazl Suratgar Mohammadbagher Menhaj Reza Abbasi-Asl |
| author_sort | Fatemeh Kamali |
| collection | DOAJ |
| description | Voxelwise encoding models based on convolutional neural networks (CNNs) are widely used as predictive models of brain activity evoked by natural movies. Despite their superior predictive performance, the huge number of parameters in CNN-based models have made them difficult to interpret. Here, we investigate whether model compression can build more interpretable and more stable CNN-based voxelwise models while maintaining accuracy. We used multiple compression techniques to prune less important CNN filters and connections, a receptive field compression method to select receptive fields with optimal center and size, and principal component analysis to reduce dimensionality. We demonstrate that the model compression improves the accuracy of identifying visual stimuli in a hold-out test set. Additionally, compressed models offer a more stable interpretation of voxelwise pattern selectivity than uncompressed models. Finally, the receptive field-compressed models reveal that the optimal model-based population receptive fields become larger and more centralized along the ventral visual pathway. Overall, our findings support using model compression to build more interpretable voxelwise models. |
| format | Article |
| id | doaj-art-41c1d635d8f34dc8a6ad2c0609ab782a |
| institution | DOAJ |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-41c1d635d8f34dc8a6ad2c0609ab782a2025-08-20T02:56:20ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-02-01212e101282210.1371/journal.pcbi.1012822Compression-enabled interpretability of voxelwise encoding models.Fatemeh KamaliAmir Abolfazl SuratgarMohammadbagher MenhajReza Abbasi-AslVoxelwise encoding models based on convolutional neural networks (CNNs) are widely used as predictive models of brain activity evoked by natural movies. Despite their superior predictive performance, the huge number of parameters in CNN-based models have made them difficult to interpret. Here, we investigate whether model compression can build more interpretable and more stable CNN-based voxelwise models while maintaining accuracy. We used multiple compression techniques to prune less important CNN filters and connections, a receptive field compression method to select receptive fields with optimal center and size, and principal component analysis to reduce dimensionality. We demonstrate that the model compression improves the accuracy of identifying visual stimuli in a hold-out test set. Additionally, compressed models offer a more stable interpretation of voxelwise pattern selectivity than uncompressed models. Finally, the receptive field-compressed models reveal that the optimal model-based population receptive fields become larger and more centralized along the ventral visual pathway. Overall, our findings support using model compression to build more interpretable voxelwise models.https://doi.org/10.1371/journal.pcbi.1012822 |
| spellingShingle | Fatemeh Kamali Amir Abolfazl Suratgar Mohammadbagher Menhaj Reza Abbasi-Asl Compression-enabled interpretability of voxelwise encoding models. PLoS Computational Biology |
| title | Compression-enabled interpretability of voxelwise encoding models. |
| title_full | Compression-enabled interpretability of voxelwise encoding models. |
| title_fullStr | Compression-enabled interpretability of voxelwise encoding models. |
| title_full_unstemmed | Compression-enabled interpretability of voxelwise encoding models. |
| title_short | Compression-enabled interpretability of voxelwise encoding models. |
| title_sort | compression enabled interpretability of voxelwise encoding models |
| url | https://doi.org/10.1371/journal.pcbi.1012822 |
| work_keys_str_mv | AT fatemehkamali compressionenabledinterpretabilityofvoxelwiseencodingmodels AT amirabolfazlsuratgar compressionenabledinterpretabilityofvoxelwiseencodingmodels AT mohammadbaghermenhaj compressionenabledinterpretabilityofvoxelwiseencodingmodels AT rezaabbasiasl compressionenabledinterpretabilityofvoxelwiseencodingmodels |