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ArXiv (2013)
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1. Waldrop, M.M.: The chips are down for Moore’s law. Nature. 530, 144–147 (2016)
2. Abbe, E.: Contributions to the Theory of the Microscope and the Microscopic Perception.
Springer (1873)
3. International Technology Roadmap for Semiconductors (2015)
4. Neumann, J.V.: First draft of a report on the EDVAC. IEEE Ann. Hist. Comput. 15, 27–75
(1993)
5. Backus, J.W.: Can programming be liberated from the von Neumann style? A functional style
and its algebra of programs. Comm. ACM. 21, 613–641 (1978)
6. Dongarra, J.: Visit to the National University for Defense Technology Changsha, China.
University of Tennessee (2013)
7. Mead, C.: Neuromorphic electronic systems. In: Proceedings of the IEEE. IEEE (1990)
8. Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500–544 (1952)
9. Haimovici, A., Tagliazucchi, E., Balenzuela, P., Chialvo, D.R.: Brain organization into resting
state networks emerges at criticality on a model of the human connectome. Phys. Rev. Lett. 110
(17), 178101 (2013)
10. Wang, X.F., Chen, G.: Complex networks: small-world, scale-free and beyond. IEEE Circ.
Syst. Mag. 3, 6–20 (2003)
11. Sporns, O.: Small-world connectivity, motif composition, and complexity of fractal neuronal
connections. Biosystems. 85, 55–64 (2006)
12. Abbott, L.F., Nelson, S.B.: Synaptic plasticity: taming the beast. Nat. Neurosci. 3, 1178 (2000)
13. Hebb, D.O.: Organization of Behavior. Wiley, New York (1950)
14. Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization
in the brain. Psychol. Rev. 65, 386–408 (1958)
15. Hopfield, J.J.: Artificial neural networks. IEEE Circ. Dev. Mag. 4, 3–10 (1988)
16. Gomes, L.: Neuromorphic chips are destined for deep learning—or obscurity. IEEE Spectrum
(2017)
17. Schuman, C.D., Potok, T.E., Patton, R.M., Douglas Birdwell, J., Dean, M.E., Rose, G.S., Plank,
J.S.: A survey of neuromorphic computing and neural networks in hardware. arXiv (2017)
18. Christie, P., Stroobandt, D.: The interpretation and application of Rent’s rule. IEEE Trans. VLSI
Syst. 8, 639–648 (2000)
19. Abraham, A.: Artificial neural networks. In: Sydenham, P.H., Thorn, R. (eds.) Handbook of
Measuring System Design. Wiley, New York (2005)
20. Medsker, L., Jain, L.C.: Recurrent Neural Networks: Design and Applications. CRC, Boca
Raton, FL (2001)
21. Graves, A., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks.
ArXiv (2013)
22. Khotanzad, A., Chung, C.: Application of multi-layer perceptron neural networks to vision
problems. Neural Comput. Appl. 7, 249–259 (1998)
23. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature. 521, 436–444 (2015)
24. LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., Jackel, L.
D.: Handwritten digit recognition with a back-propagation network. In: Touretsky, D.S. (ed.)
Advances in Neural Information Processing Systems. Morgan Kaufmann, San Mateo (1990)
25. Hinton, G.E.: Learning multiple layers of representation. Trends Cogn. Sci. 11(10), 428–434
(2007)
26. Büsing, L., Schrauwen, B., Legenstein, R.: Connectivity, dynamics, and memory in reservoir
computing with binary and analog neurons. Neural Comput. 22, 1272–1311 (2010)
27. Sojakka, C. F.: Pattern recognition in a bucket. In: Wolfgang Banzhaf, J. Z. (ed.) European
Conference on Artificial Life: Advances in Artificial Life (2003)
28. Goudarzi, A., Teuscher, C., Gulbahce, N., Rohlf, T.: Emergent criticality through adaptive
information processing in Boolean networks. Phys. Rev. Lett. 108, 128702 (2012)
240
R. Aguilera et al.