[1] A. Clark, “Whatever next? predictive brains, situated
agents, and the future of cognitive science,” Behavioral
and Brain Sciences, vol. 36, p. 181–204, May 2013.
[2] S. Lodato and P. Arlotta, “Generating neuronal diversity
in the mammalian cerebral cortex,” Annual Review
of Cell and Developmental Biology, vol. 31, p. 699–720,
November 2015.
[3] V. B. Mountcastle, “Modality and topographic properties
of single neurons of cat’s somatic sensory cortex,” Journal
of Neurophysiology, vol. 20, p. 408–434, July 1957.
[4] M. Awad and R. Khanna, Cortical Algorithms. Apress,
2015.
[5] J. Hawkins, A Thousand Brains: A new theory of intelligence.
London, England: Basic Books, mar 2021.
[6] T. Parr, G. Pezzulo, and K. J. Friston, Active Inference:
The Free Energy Principle in Mind, Brain, and Behavior.
The MIT Press, March 2022.
[7] A. M. Bastos, W. M. Usrey, R. A. Adams, G. R. Mangun,
P. Fries, and K. J. Friston, “Canonical microcircuits for
predictive coding,” Neuron, vol. 76, no. 4, pp. 695–711,
2012.
[8] K. Friston, “Learning and inference in the brain,” Neural
Networks, vol. 16, p. 1325–1352, November 2003.
[9] R. P. N. Rao and D. H. Ballard, “Predictive coding in the
visual cortex: a functional interpretation of some extraclassical
receptive-field effects,” Nature Neuroscience,
vol. 2, no. 1, pp. 79–87, 1999.
[10] M. A. Kramer, “Nonlinear principal component analysis
using autoassociative neural networks,” AIChE Journal,
vol. 37, p. 233–243, February 1991.
[11] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit,
L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin,
“Attention is all you need,” in Advances in Neural Information
Processing Systems (I. Guyon, U. V. Luxburg,
S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and
R. Garnett, eds.), vol. 30, Curran Associates, Inc., 2017.
[12] W. Lotter, G. Kreiman, and D. Cox, “Deep predictive
coding networks for video prediction and unsupervised
learning,” ArXiv, vol. abs/1605.08104, 2016.
[13] S. B. L. Mandyam Veerambudi Srinivasan and A. Dubs,
“Predictive coding: a fresh view of inhibition in the
retina,” Proceedings of the Royal Society of London.
Series B. Biological Sciences, vol. 216, p. 427–459,
November 1982.
[14] A. v. d. Oord, Y. Li, and O. Vinyals, “Representation
learning with contrastive predictive coding,” ArXiv,
vol. abs/1807.03748, 2018.
[15] J. Orchard and W. Sun, “Making predictive coding networks
generative,” arXiv, vol. abs/1910.12151, 2019.
[16] A. Ororbia, “Spiking neural predictive coding
for continual learning from data streams,” arXiv,
vol. abs/1908.08655, 2019.
[17] M. Boerlin, C. K. Machens, and S. Denève, “Predictive
coding of dynamical variables in balanced spiking
networks,” PLoS Computational Biology, vol. 9,
p. e1003258, November 2013.
[18] A. W. N’Dri, T. Barbier, C. Teulière, and J. Triesch,
“Predictive coding light: learning compact visual codes
by combining excitatory and inhibitory spike timingdependent
plasticity,” in 2023 IEEE/CVF Conference
on Computer Vision and Pattern Recognition Workshops
(CVPRW), IEEE, June 2023.
[19] S. Yoo, Y. Park, Z. Wang, Y. Wu, S. Medepalli, W. Thio,
and W. D. Lu, “Columnar learning networks for multisensory
spatiotemporal learning,” Advanced Intelligent
Systems, vol. 4, October 2022.
[20] L. Abbott, “Lapicque’s introduction of the integrateand-
fire model neuron (1907),” Brain Research Bulletin,
vol. 50, p. 303–304, November 1999.
[21] G.-q. Bi and M.-m. Poo, “Synaptic modifications in cultured
hippocampal neurons: Dependence on spike timing,
synaptic strength, and postsynaptic cell type,” The
Journal of Neuroscience, vol. 18, p. 10464–10472, December
1998.
[22] M. E. Larkum, “Are dendrites conceptually useful?,”
Neuroscience, vol. 489, p. 4–14, May 2022.
[23] B. Willmore, R. J. Prenger, M. C.-K. Wu, and J. L. Gallant,
“The berkeley wavelet transform: A biologically inspired
orthogonal wavelet transform,” Neural Computation,
vol. 20, p. 1537–1564, June 2008.
[24] Y. LeCun, C. Cortes, and C. Burges, “Mnist handwritten
digit database,” AT&T Labs [Online]. Available:
http://yann.lecun.com/exdb/mnist, vol. 2, 2010.
[25] M. Koperski, T. Konopczynski, R. Nowak, P. Semberecki,
and T. Trzcinski, “Plugin networks for inference
under partial evidence,” in 2020 IEEE Winter Conference
on Applications of Computer Vision (WACV), IEEE,
March 2020.