G. Indiveri and S. Liu, Memory and information processing in neuromorphic systems, Proceedings of the IEEE, vol.103, issue.8, 2015.

C. S. Thakur, J. L. Molin, G. Cauwenberghs, G. Indiveri, K. Kumar et al., Large-scale neuromorphic spiking array processors: A quest to mimic the brain, Frontiers in Neuroscience, vol.12, 2018.

. Editorial, Big data needs a hardware revolution, Nature, vol.554, issue.7691, 2018.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.521, issue.7553, 2015.

S. Friedmann, J. Schemmel, A. Grübl, A. Hartel, M. Hock et al., Demonstrating hybrid learning in a flexible neuromorphic hardware system, IEEE Transactions on Biomedical Circuits and Systems, vol.11, issue.1, 2017.

S. Moradi, N. Qiao, F. Stefanini, and G. Indiveri, A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (DYNAPs), IEEE Transactions on Biomedical Circuits and Systems, vol.12, issue.1, 2018.

M. Prezioso, M. Mahmoodi, F. M. Bayat, H. Nili, H. Kim et al., Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits, Nature Communications, vol.9, issue.1, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02487839

. Ultra-low, Power Event-Based Camera (ULPEC) H2020 project

C. Posch, T. Serrano-gotarredona, B. Linares-barranco, and T. Delbrück, Retinomorphic event-based vision sensors: Bioinspired cameras with spiking output, Proceedings of the IEEE, vol.102, issue.10, 2014.

G. Orchard, A. Jayawant, G. K. Cohen, and N. Thakor, Converting static image datasets to spiking neuromorphic datasets using saccades, Frontiers in Neuroscience, vol.9, 2015.

G. Lecerf, J. Tomas, S. Boyn, S. Girod, A. Mangalore et al., Silicon neuron dedicated to memristive spiking neural networks, IEEE International Symposium on Circuits and Systems (ISCAS), 2014.
URL : https://hal.archives-ouvertes.fr/hal-01093162

D. Kuzum, S. Yu, and H. P. Wong, Synaptic electronics: materials, devices and applications, Nanotechnology, vol.24, issue.38, 2013.

A. Chanthbouala, V. Garcia, R. O. Cherifi, K. Bouzehouane, S. Fusil et al., A ferroelectric memristor, Nature Materials, vol.11, issue.10, 2012.

D. Querlioz, O. Bichler, P. Dollfus, and C. Gamrat, Immunity to device variations in a spiking neural network with memristive nanodevices, IEEE Transactions on Nanotechnology, vol.12, issue.3, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01826840

T. Serrano-gotarredona, T. Masquelier, T. Prodromakis, G. Indiveri, and B. Linares-barranco, STDP and STDP variations with memristors for spiking neuromorphic learning systems, Frontiers in Neuroscience, vol.7, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01578521

S. Boyn, J. Grollier, G. Lecerf, B. Xu, N. Locatelli et al., Learning through ferroelectric domain dynamics in solid-state synapses, Nature Communications, vol.8, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02288726

B. Nessler, M. Pfeiffer, L. Buesing, and W. Maass, Bayesian computation emerges in generic cortical microcircuits through spike-timingdependent plasticity, PLOS Computational Biology, vol.9, issue.4, 2013.

D. Querlioz, O. Bichler, A. F. Vincent, and C. Gamrat, Bioinspired programming of memory devices for implementing an inference engine, Proceedings of the IEEE, vol.103, issue.8, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01822199

L. R. Iyer and A. Basu, Unsupervised learning of event-based image recordings using spike-timing-dependent plasticity, International Joint Conference on Neural Networks (IJCNN), 2017.