Atomically Thin Optomemristive Feedback Neurons

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Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).

Article CAS Google Scholar

M, D., N, S., T, L. & G, C. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 82–99 (2018).

Article Google Scholar

Tuma, T., Pantazi, A., Le Gallo, M., Sebastian, A. & Eleftheriou, E. Stochastic phase-change neurons. Nat. Nanotechnol. 11, 693–699 (2016).

Article CAS Google Scholar

Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H. & Pernice, W. H. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208–214 (2019).

Article CAS Google Scholar

Kumar, S., Williams, R. S. & Wang, Z. Third-order nanocircuit elements for neuromorphic engineering. Nature 585, 518–523 (2020).

Article CAS Google Scholar

Yi, W. et al. Biological plausibility and stochasticity in scalable VO₂ active memristor neurons. Nat. Commun. 9, 4661 (2018).

Article Google Scholar

Hassan, N. et al. Magnetic domain wall neuron with lateral inhibition. J. Appl. Phys. 124, 152127 (2018).

Article Google Scholar

Kandel, E. R. et al. Principles of Neural Science Vol. 4 (McGraw-Hill, 2000).

Yin, H. in Computational Intelligence: a Compendium (eds Fulcher, J. & Jain, L. C.) 715–762 (Springer, 2008).

Maass, W. On the computational power of winner-take-all. Neural Comput. 12, 2519–2535 (2000).

Article CAS Google Scholar

Maass, W. Neural computation with winner-take-all as the only nonlinear operation. Adv. Neural Inf. Process. Syst. 12, 293–299 (2000).

Google Scholar

Lazzaro, J., Ryckebusch, S., Mahowald, M. A. & Mead, C. A. Winner-take-all networks of O(n) complexity. Adv. Neural Inf. Process. Syst. 1, 703–711 (1988).

Kaski, S. & Kohonen, T. Winner-take-all networks for physiological models of competitive learning. Neural Netw. 7, 973–984 (1994).

Article Google Scholar

Gerstner, W., Lehmann, M., Liakoni, V., Corneil, D. & Brea, J. Eligibility traces and plasticity on behavioral time scales: experimental support of neohebbian three-factor learning rules. Front. Neural Circuits 12, 53 (2018).

Article Google Scholar

Ferguson, K. A. & Cardin, J. A. Mechanisms underlying gain modulation in the cortex. Nat. Rev. Neurosci. 21, 80–92 (2020).

Article CAS Google Scholar

Kreiser, R., Moraitis, T., Sandamirskaya, Y. & Indiveri, G. On-chip unsupervised learning in winner-take-all networks of spiking neurons. In 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) 1–4 (IEEE, 2017).

Hsu, D., Figueroa, M. & Diorio, C. Competitive learning with floating-gate circuits. IEEE Trans. Neural Netw. 13, 732–744 (2002).

Article CAS Google Scholar

Diehl, P. U. & Cook, M. Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci. 9, 99 (2015).

Article Google Scholar

Ebong, I. E. & Mazumder, P. CMOS and memristor-based neural network design for position detection. Proc. IEEE 100, 2050–2060 (2011).

Article Google Scholar

Srivastava, R. K., Masci, J., Kazerounian, S., Gomez, F. J. & Schmidhuber, J. Compete to compute. In Advances in Neural Information Processing Systems 26 (NIPS 2013), 2310–2318 (Citeseer, 2013).

Oster, M., Douglas, R. & Liu, S.-C. Computation with spikes in a winner-take-all network. Neural Comput. 21, 2437–2465 (2009).

Article Google Scholar

Fenno, L., Yizhar, O. & Deisseroth, K. The development and application of optogenetics. Ann. Rev. Neurosci. 34, 389–412 (2011).

Article CAS Google Scholar

Gradinaru, V. et al. Molecular and cellular approaches for diversifying and extending optogenetics. Cell 141, 154–165 (2010).

Article CAS Google Scholar

Sarwat, S. G., Moraitis, T., Wright, C. D. & Bhaskaran, H. Chalcogenide optomemristors for multi-factor neuromorphic computation. Nat. Commun. 13, 2247 (2022).

Article CAS Google Scholar

Wang, Q. et al. Nonvolatile infrared memory in MoS₂/PbS van der Waals heterostructures. Sci. Adv. 4, eaap7916 (2018).

Article Google Scholar

Xiang, D. et al. Two-dimensional multibit optoelectronic memory with broadband spectrum distinction. Natu. Commun. 9, 2966 (2018).

Article Google Scholar

Tran, M. D. et al. Two-terminal multibit optical memory via van der Waals heterostructure. Adv. Mater. 31, 1807075 (2019).

Article Google Scholar

Lee, J. et al. Monolayer optical memory cells based on artificial trap-mediated charge storage and release. Nat. Commun. 8, 14734 (2017).

Article CAS Google Scholar

Sze, S. M., Li, Y. & Ng, K. K. Physics of Semiconductor Devices (John Wiley & Sons, 2021).

Amit, I. et al. Role of charge traps in the performance of atomically thin transistors. Adv. Mater. 29, 1605598 (2017).

Article Google Scholar

Kim, S. Y., Yang, H. I. & Choi, W. Photoluminescence quenching in monolayer transition metal dichalcogenides by Al₂O₃ encapsulation. Appl. Phys. Lett. 113, 133104 (2018).

Article Google Scholar

Li, Z., Wang, W., Greenham, N. C. & McNeill, C. R. Influence of nanoparticle shape on charge transport and recombination in polymer/nanocrystal solar cells. Phys. Chem. Chem. Phys. 16, 25684–25693 (2014).

Article CAS Google Scholar

Carpenter, G. A. & Grossberg, S. A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput. Vis. Graph. Image Process. 37, 54–115 (1987).

Article Google Scholar

Kohonen, T. Essentials of the self-organizing map. Neural Netw. 37, 52–65 (2013).

Article Google Scholar

Kohonen, T. The self-organizing map. Proc. IEEE 78, 1464–1480 (1990).

Article Google Scholar

Oh, S. et al. Energy-efficient Mott activation neuron for full-hardware implementation of neural networks. Nat. Nanotechnol. 16, 680–687 (2021).

Article CAS Google Scholar

Meng, W. et al. Three-dimensional monolithic micro-led display driven by atomically thin transistor matrix. Nat. Nanotechnol. 16, 1231–1236 (2021).

Article CAS Google Scholar

Hwangbo, S., Hu, L., Hoang, A. T., Choi, J. Y. & Ahn, J.-H. Wafer-scale monolithic integration of full-colour micro-led display using MoS₂ transistor. Nat. Nanotechnol. 17, 500–506 (2022).

Article CAS Google Scholar