Markov, N. T., Vezoli, J., Chameau, P., Arnaud Falchier, Quilodran, R., Huisssoud, C., Lamy, C., Misery, P., Giroud, P., Ullman, S., Barone, P., Dehay, C., Knoblauch, K., and Kennedy, H. (2014). Anatomy of a hierarchy: Feedforward and feedback pathways in macaque visual cortex. Journal of Comparative Neurology, 522(1):225-259.
McDonnell, M. D. and Ward, L. M. (2011). The benefits of noise in neural systems: bridging theory and experiment. Nat. Rev. Neurosci., 12(7):415-426.
Millidge, B., Tschantz, A., and Buckley, C. L. (2020). Predictive coding approximates backprop along arbitrary computation graphs.
Miyasawa, K. (1961). An empirical Bayes estimator of the mean of a normal population. Bull. Inst. Internat. Statist., 38:181-188.
Nayebi, A., Sagastuy-Brena, J., Bear, D. M., Kar, K., Kubilius, J., Ganguli, S., Sussillo, D., DiCarlo, J. J., and Yamins, D. L. K. (2022). Recurrent connections in the primate ventral visual stream mediate a trade-off between task performance and network size during core object recognition. Neural Comput., 34(8):1652-1675.
Nokland, A. (2016). Direct feedback alignment provides learning in deep neural networks.
Pearson, J. (2019). The human imagination: the cognitive neuroscience of visual mental imagery. Nat. Rev. Neurosci., 20(10):624-634.
Pearson, J., Clifford, C. W. G., and Tong, F. (2008). The functional impact of mental imagery on conscious perception. Curr. Biol., 18(13):982-986.
Pearson, J., Naselaris, T., Holmes, E. A., and Kosslyn, S. M. (2015). Mental imagery: Functional mechanisms and clinical applications. Trends Cogn. Sci., 19(10):590-602.
Rao, R. P. N. and Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1):79-87.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning representations by backpropagating errors. Nature, 323(6088):533-536.
Semedo, J. D., Jasper, A. I., Zandvakili, A., Krishna, A., Aschner, A., Machens, C. K., Kohn, A., and Yu, B. M. (2022). Feedforward and feedback interactions between visual cortical areas use different population activity patterns. Nat. Commun., 13(1):1099.
Spoerer, C. J., McClure, P., and Kriegeskorte, N. (2017). Recurrent convolutional neural networks: A better model of biological object recognition. Front. Psychol., 8.
Suzuki, K., Roseboom, W., Schwartzman, D. J., and Seth, A. K. (2017). A deep-dream virtual reality platform for studying altered perceptual phenomenology. Sci. Rep., 7(1):15982.
Suzuki, K., Seth, A. K., and Schwartzman, D. J. (2023). Modelling phenomenological differences in aetiologically distinct visual hallucinations using deep neural networks.
Tang, H., Buia, C., Madhavan, R., Crone, N. E., Madsen, J. R., Anderson, W. S., and Kreiman, G. (2014). Spatiotemporal dynamics underlying object completion in human ventral visual cortex. Neuron, 83(3):736-748.
Tang, H., Schrimpf, M., Lotter, W., Moerman, C., Paredes, A., Ortega Caro, J., Hardesty, W., Cox, D., and Kreiman, G. (2018). Recurrent computations for visual pattern completion. Proc. Natl. Acad. Sci. U. S. A., 115(35):8835-8840.
Tscshantz, A., Millidge, B., Seth, A. K., and Buckley, C. L. (2023). Hybrid predictive coding: Inferring, fast and slow. PLoS Comput. Biol., 19(8):e1011280.
Walsh, K. S., McGovern, D. P., Clark, A., and O'Connell, R. G. (2020). Evaluating the neurophysiological evidence for predictive processing as a model of perception. Ann. N. Y. Acad. Sci., 1464(1):242-268.