# Suggested further readings


### Pytorch - papers: 
Automatic differentiation library; some tutorials [openreview.net/pdf?id=BJJsrmfCZ](https://openreview.net/pdf?id=BJJsrmfCZ).

### Recommended review papers:

Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., ... and Kording, K. P. (2019). A deep learning framework for neuroscience. Nature neuroscience 22(11): 1761-1770. doi: [10.1038/s41593-019-0520-2](https://doi.org/10.1038/s41593-019-0520-2) {{ closed_access }} (postprint: [www.ncbi.nlm.nih.gov/pmc/articles/PMC7115933](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115933) {{ open_access }}).

Lindsay, G. W. (2021). Convolutional neural networks as a model of the visual system: Past, present, and future. Journal of cognitive neuroscience 33(10): 2017-2031. doi: [10.1162/jocn_a_01544](https://doi.org/10.1162/jocn_a_01544) {{ closed_access }} (preprint: [arxiv:2001.07092](http://arxiv.org/abs/2001.07092) {{ open_access }}).

### Intro:

Large list of papers comparing DNNs and the brain:

Cichy, R. M., Khosla, A., Pantazis, D., Torralba, A., and Oliva, A. (2016). Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Scientific reports 6(1): 1-13. doi: [10.1038/srep27755](https://doi.org/10.1038/srep27755) {{ open_access }}.

Hasson, U., Nastase, S. A., & Goldstein, A. (2020). Direct fit to nature: an evolutionary perspective on biological and artificial neural networks. Neuron 105(3): 416-434. doi: [10.1016/j.neuron.2019.12.002](https://doi.org/10.1016/j.neuron.2019.12.002) {{ open_access }}.

Heuer, K., Gulban, O. F., Bazin, P. L., Osoianu, A., Valabregue, R., Santin, M., ... and Toro, R. (2019). Evolution of neocortical folding: A phylogenetic comparative analysis of MRI from 34 primate species. Cortex 118: 275-291. doi: [10.1016/j.cortex.2019.04.011](https://doi.org/10.1016/j.cortex.2019.04.011) {{ open_access }}.

Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2015). Object detectors emerge in deep scene cnns. In ICLR, San Diego, CA, USA. [arXiv:1412.6856](https://arxiv.org/abs/1412.6856).

Zhou, B., Bau, D., Oliva, A., and Torralba, A. (2018). Interpreting deep visual representations via network dissection. IEEE transactions on pattern analysis and machine intelligence 41(9): 2131-2145. doi: [10.1109/TPAMI.2018.2858759](https://doi.org/10.1109/TPAMI.2018.2858759) {{ open_access }}.

### Tutorials:

#### Dataset:
Stringer, C., Michaelos, M., Tsyboulski, D., Lindo, S. E., and Pachitariu, M. (2021). High-precision coding in visual cortex. Cell 184(10): 2767–2778.e15. doi: [10.1016/j.cell.2021.03.042](https://doi.org/10.1016/j.cell.2021.03.042) {{ closed_access }} (preprint: [www.biorxiv.org/content/biorxiv/early/2019/11/04/679324.full.pdf](https://www.biorxiv.org/content/biorxiv/early/2019/11/04/679324.full.pdf) {{ open_access }}).

#### Deep learning used for encoding models:
Batty, E., Merel, J., Brackbill, N., Heitman, A., Sher, A., Litke, A., ... and Paninski, L. (2017). Multilayer recurrent network models of primate retinal ganglion cell responses. ICLR 2017, Toulon, France. url: [openreview.net/forum?id=HkEI22jeg](https://openreview.net/forum?id=HkEI22jeg)

Cadena, S. A., Denfield, G. H., Walker, E. Y., Gatys, L. A., Tolias, A. S., Bethge, M., and Ecker, A. S. (2019). Deep convolutional models improve predictions of macaque V1 responses to natural images. PLoS computational biology 15(4): e1006897. doi: [10.1371/journal.pcbi.1006897](https://doi.org/10.1371/journal.pcbi.1006897) {{ open_access }}.

McIntosh, L., Maheswaranathan, N., Nayebi, A., Ganguli, S., and Baccus, S. (2016). Deep learning models of the retinal response to natural scenes. Advances in neural information processing systems, 29. url: [papers.nips.cc/paper/2016/hash/a1d33d0dfec820b41b54430b50e96b5c-Abstract.html](https://papers.nips.cc/paper/2016/hash/a1d33d0dfec820b41b54430b50e96b5c-Abstract.html)

Walker, E. Y., Sinz, F. H., Cobos, E., Muhammad, T., Froudarakis, E., Fahey, P. G., ... and Tolias, A. S. (2019). Inception loops discover what excites neurons most using deep predictive models. Nature neuroscience 22(12): 2060-2065. doi: [10.1038/s41593-019-0517-x](https://doi.org/10.1038/s41593-019-0517-x) {{ closed_access }}.

#### Comparing deep networks and the brain:

Guclu, U., and van Gerven, M. A. (2015). Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. Journal of Neuroscience 35(27): 10005-10014. doi: [10.1523/JNEUROSCI.5023-14.2015](https://doi.org/10.1523/JNEUROSCI.5023-14.2015) {{ open_access }}.

Khaligh-Razavi, S. M., and Kriegeskorte, N. (2014). Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS computational biology 10(11):e1003915. doi: [10.1371/journal.pcbi.1003915](https://doi.org/10.1371/journal.pcbi.1003915) {{ open_access }}.

Kriegeskorte, N., Mur, M., and Bandettini, P. A. (2008). Representational similarity analysis-connecting the branches of systems neuroscience. Frontiers in systems neuroscience 2:4. doi: [10.3389/neuro.06.004.2008](https://doi.org/10.3389/neuro.06.004.2008) {{ open_access }}.

Mohsenzadeh, Y., Mullin, C., Lahner, B., and Oliva, A. (2020). Emergence of Visual center-periphery Spatial organization in Deep convolutional neural networks. Scientific Reports 10(1): 1-8. doi: [10.1038/s41598-020-61409-0](https://doi.org/10.1038/s41598-020-61409-0) {{ open_access }}.

Yamins, D. L., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., and DiCarlo, J. J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the national academy of sciences 111(23): 8619-8624. doi: [10.1073/pnas.1403112111](https://doi.org/10.1073/pnas.1403112111) {{ closed_access }} (postprint: [europepmc.org/articles/pmc4060707?pdf=render](https://europepmc.org/articles/pmc4060707?pdf=render) {{ open_access }}).

#### Deep learning:
Goh, G. (2017). Why momentum really works. Distill 2(4): e6. doi: [10.23915/distill.00006](https://doi.org/10.23915/distill.00006) {{ open_access }}.

He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). url: [https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7780459](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7780459)

Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448-456). PMLR. url: [proceedings.mlr.press/v37/ioffe15.html](https://proceedings.mlr.press/v37/ioffe15.html)

Li, H., Xu, Z., Taylor, G., Studer, C., and Goldstein, T. (2018). Visualizing the loss landscape of neural nets. Advances in neural information processing systems, 31. url: [papers.nips.cc/paper/2018/hash/a41b3bb3e6b050b6c9067c67f663b915-Abstract.html](https://papers.nips.cc/paper/2018/hash/a41b3bb3e6b050b6c9067c67f663b915-Abstract.html)

Nielsen, M. (2016). A visual proof that neural nets can compute any function. url: [neuralnetworksanddeeplearning.com/chap4.html](http://neuralnetworksanddeeplearning.com/chap4.html).

Olah, C. (2014). Conv nets: A modular perspective. url: [colah.github.io/posts/2014-07-Conv-Nets-Modular](http://colah.github.io/posts/2014-07-Conv-Nets-Modular).

### Outro 1

Jozwik, K. M., Kriegeskorte, N., Storrs, K. R., and Mur, M. (2017). Deep convolutional neural networks outperform feature-based but not categorical models in explaining object similarity judgments. Frontiers in psychology 8: 1726. doi: [10.3389/fpsyg.2017.01726](https://doi.org/10.3389/fpsyg.2017.01726) {{ open_access }}.

Kriegeskorte, N., and Douglas, P. K. (2018). Cognitive computational neuroscience. Nature neuroscience 21(9): 1148-1160. doi: [10.1038/s41593-018-0210-5](https://doi.org/10.1038/s41593-018-0210-5) {{ closed_access }} (postprint: [europepmc.org/articles/pmc6706072?pdf=render](https://europepmc.org/articles/pmc6706072?pdf=render) {{ open_access }}).

Kietzmann, T. C., Spoerer, C. J., Sörensen, L. K., Cichy, R. M., Hauk, O., and Kriegeskorte, N. (2019). Recurrence is required to capture the representational dynamics of the human visual system. Proceedings of the National Academy of Sciences 116(43): 21854-21863. doi: [10.1073/pnas.1905544116](https://doi.org/10.1073/pnas.1905544116) {{ open_access }}.

Kubilius, J., Schrimpf, M., Kar, K., Rajalingham, R., Hong, H., Majaj, N., ... and DiCarlo, J. J. (2019). Brain-like object recognition with high-performing shallow recurrent ANNs. Advances in neural information processing systems, 32. url: [NIPS2019](https://papers.nips.cc/paper/2019/hash/7813d1590d28a7dd372ad54b5d29d033-Abstract.html)

Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., and Hinton, G. (2020). Backpropagation and the brain. Nature reviews. Neuroscience 21(6): 335–346. doi: [10.1038/s41583-020-0277-3](https://doi.org/10.1038/s41583-020-0277-3) {{ closed_access }} (preprint: [ora.ox.ac.uk/objects/uuid:862189c1-0088-4f78-b17a-2748c2019209/download_file?safe_filename=Lillicrap_v6_2020.pdf&file_format=pdf&type_of_work=Journal+article](https://ora.ox.ac.uk/objects/uuid:862189c1-0088-4f78-b17a-2748c2019209/download_file?safe_filename=Lillicrap_v6_2020.pdf&file_format=pdf&type_of_work=Journal+article) {{ open_access }}).

Nili, H., Wingfield, C., Walther, A., Su, L., Marslen-Wilson, W., and Kriegeskorte, N. (2014). A toolbox for representational similarity analysis. PLoS computational biology 10(4): e1003553. doi: [10.1371/journal.pcbi.1003553](https://doi.org/10.1371/journal.pcbi.1003553) {{ open_access }}.

Schrimpf, M., Kubilius, J., Hong, H., Majaj, N. J., Rajalingham, R., Issa, E. B., ..., and DiCarlo, J. J. (2020). Brain-score: Which artificial neural network for object recognition is most brain-like?. bioRxiv 407007. doi: [10.1101/407007](https://doi.org/10.1101/407007) {{ open_access }}.

Spoerer, C. J., Kietzmann, T. C., Mehrer, J., Charest, I., and Kriegeskorte, N. (2020). Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision. PLoS computational biology 16(10): e1008215. doi: [10.1371/journal.pcbi.1008215](https://doi.org/10.1371/journal.pcbi.1008215) {{ open_access }}.

Storrs, K. R., Kietzmann, T. C., Walther, A., Mehrer, J., and Kriegeskorte, N. (2021). Diverse deep neural networks all predict human inferior temporal cortex well, after training and fitting. Journal of Cognitive Neuroscience 33(10): 2044-2064. doi: [10.1162/jocn_a_01755](https://doi.org/10.1162/jocn_a_01755) {{ closed_access }} (postprint: [repository.ubn.ru.nl/bitstream/handle/2066/237374/1/237374.pdf](https://repository.ubn.ru.nl/bitstream/handle/2066/237374/1/237374.pdf) {{ open_access }}).

Tang, H., Schrimpf, M., Lotter, W., Moerman, C., Paredes, A., Caro, J. O., ... and Kreiman, G. (2018). Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences 115(35): 8835-8840. doi: [10.1073/pnas.1719397115](https://doi.org/10.1073/pnas.1719397115) {{ open_access }}.

### Outro 2

Chambers, C., Seethapathi, N., Saluja, R., Loeb, H., Pierce, S. R., Bogen, D. K., ... and Kording, K. P. (2020). Computer vision to automatically assess infant neuromotor risk. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(11): 2431-2442. doi: [10.1109/TNSRE.2020.3029121](https://doi.org/10.1109/TNSRE.2020.3029121) {{ closed_access }} (postprint: [www.ncbi.nlm.nih.gov/pmc/articles/PMC8011647](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011647) {{ open_access }}).