Tootell, R. B., Reppas, J. B., Dale, A. M., Look, R. B., Sereno, M. I., Malach, R., et al. (1995). Visual motion aftereffect in human cortical area MT revealed by functional magnetic resonance imaging.
Tsao, D. (2014). The macaque face patch system: A window into object representation.
Tsao, D. Y., & Livingstone, M. S. (2008). Mechanisms of face perception.
Tsodyks, M., & Gilbert, C. (2004). Neural networks and perceptual learning.
Turner, M. H., Sanchez Giraldo, L. G., Schwartz, O., & Rieke, F. (2019). Stimulus– and goal-oriented frameworks for understanding natural vision.
Wagner, I. C. (2016). The integration of distributed memory traces.
Wandell, B. A., & Smirnakis, S. M. (2009). Plasticity and stability of visual field maps in adult primary visual cortex.
Wang, H. X., & Movshon, J. A. (2016). Properties of pattern and component direction-selective cells in area MT of the macaque.
Wässle, H. (2002). Brian Blundell Boycott, 10 December 1924–22 April 2000.
Wässle, H., Grunert, U., Rohrenbeck, J., & Boycott, B. B. (1989). Cortical magnification factor and the ganglion cell density of the primate retina.
Wässle, H., Puller, C., Muller, F., & Haverkamp, S. (2009). Cone contacts, mosaics, and territories of bipolar cells in the mouse retina.
Watanabe, T., Nбсez, J. E., & Sasaki, Y. (2001). Perceptual learning without perception.
Wathey, J. C., & Pettigrew, J. D. (1989). Quantitative analysis of the retinal ganglion cell layer and optic nerve of the barn owl
Werner, J. S., & Chalupa, L. M. (Eds.). (2014).
Wiesel, T. N. (1982). Postnatal development of the visual cortex and the influence of environment.
Wong, R. O., Meister, M., & Shatz, C. J. (1993). Transient period of correlated bursting activity during development of the mammalian retina.
Wu, K. J. (2018, December 10). Google’s new A.I. is a master of games, but how does it compare to the human mind? Smithsonian.com. Retrieved from www.smithsonianmag.com/innovation/google-ai-deepminds-alphazero-games-chess-and-go-180970981.
Yamins, D. L., & DiCarlo, J. J. (2016). Using goal-driven deep learning models to understand sensory cortex.
Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex.
Zeng, H., & Sanes, J. R. (2017). Neuronal cell-type classification: Challenges, opportunities and the path forward.