Deep learning—Using machine learning to study biological vision

Article, Preprint English OPEN
Majaj, Najib; Pelli, Denis;

Many vision science studies employ machine learning, especially the version called “deep learning.” Neuroscientists use machine learning to decode neural responses. Perception scientists try to understand how living organisms recognize objects. To them, deep neural netw... View more
  • References (60)
    60 references, page 1 of 6

    Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1), 1-127.

    Bengio, Y., Lee, D. H., Bornschein, J., Mesnard, T., & Lin, Z. (2015). Towards biologically plausible deep learning. arXiv preprint arXiv:1502.04156.

    Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical science, 16(3), 199-231.

    Bryson, A. E., Denham, W. F., & Dreyfus, S. E. (1963). Optimal programming problems with inequality constraints. AIAA journal, 1(11), 2544-2550.

    Caporale, N., & Dan, Y. (2008). Spike timing-dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci., 31, 25-46.

    Cox, D. D., & Dean, T. (2014). Neural networks and neuroscience-inspired computer vision. Current Biology, 24(18), R921-R929.

    Crick, F. (1989). The recent excitement about neural networks. Nature, 337(6203), 129.

    Dechter, R. (1986). Learning while searching in constraint-satisfaction-problems. In Proceedings of the Fifth AAAI National Conference on Artificial Intelligence (pp. 178-183). AAAI Press.

    Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and Trends® in Signal Processing, 7(3-4), 197-387.

    Efron, B., & Hastie, T. (2016). Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Vol. 5). Cambridge University Press.

  • Related Organizations (4)
  • Metrics
Share - Bookmark