51. Langton, C.G.: Computation at the edge of chaos: phase transitions and emergent computation.
Phys. D. 42, 12–37 (1990)
52. Gimzewski, J.K., Möller, R.: Transition from the tunneling regime to point contact studied
using scanning tunneling microscopy. Phys. Rev. B. 36(2), 1284–1287 (1987)
53. Lang, N.D.: Theory of single-atom imaging in the scanning tunneling microscope. Phys. Rev.
Lett. 56, 1164–1167 (1986)
54. van Houton, H., Beenakker, C.: Quantum point contacts. Phys. Today. 49(7), 22–27 (1996)
55. Terabe, K., Nakayama, T., Hasegawa, T., Aono, M.: Formation and disappearance of a
nanoscale silver cluster realized by solid electrochemical reaction. J. Appl. Phys. 91,
10110–10114 (2002)
56. NEC. NEC integrates nanobridge in the Cu interconnects of Si LSI. https://phys.org/news/200912-nec-nanobridge-cu-interconnects-si.html (2009)
57. Ohno, T., Hasegawa, T., Tsuruoka, T., Terabe, K., Gimzewski, J.K., Aono, M.: Short-term
plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10,
591–595 (2011)
58. Hasegawa, T., Nayak, A., Ohno, T., Terabe, K., Tsuruoka, T., Gimzewski, J.K., Aono, M.:
Memristive operations demonstrated by gap-type atomic switches. Appl. Phys. A. 102,
811–815 (2011)
59. Avizienis, A.V., Martin-Olmos, C., Sillin, H.O., Aono, M., Gimzewski, J.K., Stieg, A.Z.:
Morphological transitions from dendrites to nanowires in the electroless deposition of silver.
Cryst. Growth Des. 13(2), 465–469 (2013)
60. Stieg, A.Z., Avizienis, A.V., Sillin, H.O., Martin-Olmos, C., Aono, M., Gimzewski, J.K.:
Emergent criticality in complex turing B-type atomic switch networks. Adv. Mater. 24,
286–293 (2011)
61. Oskoee, E.N., Sahimi, M.: Electric currents in networks of interconnected memristors. Phys.
Rev. E. 83, 031105 (2011)
62. Goudarzi, A., Lakin, M.R., Stefanovic, D., Teuscher, C.: A model for variation-and faulttolerant digital logic using self-assembled nanowire architectures. In: IEEE/ACM International
Symposium on Nanoscale Architectures. ACM, pp. 116–121 (2014)
63. Verstraeten, D.: Reservoir computing: computation with dynamical systems. PhD thesis, Ghent
University (2009)
64. Legenstein, R., Maass, W.: What makes a dynamical system computationally powerful? In:
Haykin, S., Principe, J.C., Sejnowski, T.J., McWhirter, J. (eds.) New Directions in Statistical
Signal Processing: From Systems to Brain. MIT Press, Cambridge, MA (2005)
65. Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network
training. Comput. Sci. Rev. 3, 127–149 (2009)
66. Wyffels, F., Schrauwen, B.: A comparative study of reservoir computing strategies for monthly
time series prediction. Neurocomputing. 73, 1958–1964 (2010)
67. Castro, L.N.D.: Fundamentals of natural computing: an overview. Phys. Life Rev. 4, 1–36
(2007)
68. Modha, D.S., Ananthanarayanan, R., Esser, S.K., Ndirango, A., Sherbondy, A., Singh, R.:
Cognitive computing. Commun. ACM. 54, 62–71 (2011)
69. Yu, S., Kuzum, K., Philip Wong, H. S.: Design considerations of synaptic device for
neuromorphic computing. In: IEEE International Symposium on Circuits and Systems,
Melbourne, VIC. IEEE, pp 1062–1065 (2014)
70. Schrauwen, B., Verstraeten, D., Van Campenhout, J.: An overview of reservoir computing:
theory, applications and implementations. In: 15th European Symposium on Artificial Neural
Networks, pp. 471–482 (2007)
71. Bürger, J., Goudarzi, A., Stefanovic, D., Teuscher, C.: Hierarchical composition of memristive
networks for real-time computing. In: IEEE/ACM International Symposium on Nanoscale
Architectures (NANOARCH). IEEE (2015)
242
R. Aguilera et al.
Phys. D. 42, 12–37 (1990)
52. Gimzewski, J.K., Möller, R.: Transition from the tunneling regime to point contact studied
using scanning tunneling microscopy. Phys. Rev. B. 36(2), 1284–1287 (1987)
53. Lang, N.D.: Theory of single-atom imaging in the scanning tunneling microscope. Phys. Rev.
Lett. 56, 1164–1167 (1986)
54. van Houton, H., Beenakker, C.: Quantum point contacts. Phys. Today. 49(7), 22–27 (1996)
55. Terabe, K., Nakayama, T., Hasegawa, T., Aono, M.: Formation and disappearance of a
nanoscale silver cluster realized by solid electrochemical reaction. J. Appl. Phys. 91,
10110–10114 (2002)
56. NEC. NEC integrates nanobridge in the Cu interconnects of Si LSI. https://phys.org/news/200912-nec-nanobridge-cu-interconnects-si.html (2009)
57. Ohno, T., Hasegawa, T., Tsuruoka, T., Terabe, K., Gimzewski, J.K., Aono, M.: Short-term
plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10,
591–595 (2011)
58. Hasegawa, T., Nayak, A., Ohno, T., Terabe, K., Tsuruoka, T., Gimzewski, J.K., Aono, M.:
Memristive operations demonstrated by gap-type atomic switches. Appl. Phys. A. 102,
811–815 (2011)
59. Avizienis, A.V., Martin-Olmos, C., Sillin, H.O., Aono, M., Gimzewski, J.K., Stieg, A.Z.:
Morphological transitions from dendrites to nanowires in the electroless deposition of silver.
Cryst. Growth Des. 13(2), 465–469 (2013)
60. Stieg, A.Z., Avizienis, A.V., Sillin, H.O., Martin-Olmos, C., Aono, M., Gimzewski, J.K.:
Emergent criticality in complex turing B-type atomic switch networks. Adv. Mater. 24,
286–293 (2011)
61. Oskoee, E.N., Sahimi, M.: Electric currents in networks of interconnected memristors. Phys.
Rev. E. 83, 031105 (2011)
62. Goudarzi, A., Lakin, M.R., Stefanovic, D., Teuscher, C.: A model for variation-and faulttolerant digital logic using self-assembled nanowire architectures. In: IEEE/ACM International
Symposium on Nanoscale Architectures. ACM, pp. 116–121 (2014)
63. Verstraeten, D.: Reservoir computing: computation with dynamical systems. PhD thesis, Ghent
University (2009)
64. Legenstein, R., Maass, W.: What makes a dynamical system computationally powerful? In:
Haykin, S., Principe, J.C., Sejnowski, T.J., McWhirter, J. (eds.) New Directions in Statistical
Signal Processing: From Systems to Brain. MIT Press, Cambridge, MA (2005)
65. Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network
training. Comput. Sci. Rev. 3, 127–149 (2009)
66. Wyffels, F., Schrauwen, B.: A comparative study of reservoir computing strategies for monthly
time series prediction. Neurocomputing. 73, 1958–1964 (2010)
67. Castro, L.N.D.: Fundamentals of natural computing: an overview. Phys. Life Rev. 4, 1–36
(2007)
68. Modha, D.S., Ananthanarayanan, R., Esser, S.K., Ndirango, A., Sherbondy, A., Singh, R.:
Cognitive computing. Commun. ACM. 54, 62–71 (2011)
69. Yu, S., Kuzum, K., Philip Wong, H. S.: Design considerations of synaptic device for
neuromorphic computing. In: IEEE International Symposium on Circuits and Systems,
Melbourne, VIC. IEEE, pp 1062–1065 (2014)
70. Schrauwen, B., Verstraeten, D., Van Campenhout, J.: An overview of reservoir computing:
theory, applications and implementations. In: 15th European Symposium on Artificial Neural
Networks, pp. 471–482 (2007)
71. Bürger, J., Goudarzi, A., Stefanovic, D., Teuscher, C.: Hierarchical composition of memristive
networks for real-time computing. In: IEEE/ACM International Symposium on Nanoscale
Architectures (NANOARCH). IEEE (2015)
242
R. Aguilera et al.
