Designing artificial sodium ion reservoirs to emulate biological synapses


  • 1.

    Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).

    Article 

    Google Scholar
     

  • 2.

    Abbott, L. F. & Regehr, W. G. Synaptic computation. Nature 431, 796–803 (2004).

    Article 

    Google Scholar
     

  • 3.

    Navarrete, A., van Schaik, C. P. & Isler, K. Energetics and the evolution of human brain size. Nature 480, 91–93 (2011).

    Article 

    Google Scholar
     

  • 4.

    López, J. C. A fresh look at paired-pulse facilitation. Nat. Rev. Neurosci. 2, 307 (2001).

    Article 

    Google Scholar
     

  • 5.

    Xu, W., Min, S.-Y., Hwang, H. & Lee, T.-W. Organic core-sheath nanowire artificial synapses with femtojoule energy consumption. Sci. Adv. 2, e1501326 (2016).

    Article 

    Google Scholar
     

  • 6.

    Wang, Y. et al. Emerging perovskite materials for high density data storage and artificial synapses. J. Mater. Chem. C. 6, 1600–1617 (2018).

    Article 

    Google Scholar
     

  • 7.

    Saxena, V., Wu, X., Srivastava, I. & Zhu, K. Towards neuromorphic learning machines using emerging memory devices with brain-like energy efficiency. J. Low Power Electron. Appl. 8, 34 (2018).

    Article 

    Google Scholar
     

  • 8.

    Kim, Y. et al. A bioinspired flexible organic artificial afferent nerve. Science 360, 998–1003 (2018).

    Article 

    Google Scholar
     

  • 9.

    Shi, J., Ha, S. D., Zhou, Y., Schoofs, F. & Ramanathan, S. A correlated nickelate synaptic transistor. Nat. Commun. 4, 2676 (2013).

    Article 

    Google Scholar
     

  • 10.

    Wang, Y. et al. Photonic synapses based on inorganic perovskite quantum dots for neuromorphic computing. Adv. Mater. 30, 1802883 (2018).

    Article 

    Google Scholar
     

  • 11.

    Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 8, 13–24 (2013).

    Article 

    Google Scholar
     

  • 12.

    Nazari, M. H., Mazhab-Jafari, H., Leng, L., Guenther, A. & Genov, R. CMOS neurotransmitter microarray: 96-channel integrated potentiostat with on-die microsensors. IEEE Trans. Biomed. Circuits Syst. 7, 338–348 (2013).

    Article 

    Google Scholar
     

  • 13.

    Pan, F., Gao, S., Chen, C., Song, C. & Zeng, F. Recent progress in resistive random access memories: Materials, switching mechanisms, and performance. Mater. Sci. Eng. R 83, 1–59 (2014).

    Article 

    Google Scholar
     

  • 14.

    Yin, J. et al. Adaptive crystallite kinetics in homogenous bilayer oxide memristor for emulating diverse synaptic plasticity. Adv. Funct. Mater. 28, 1706927 (2018).

    Article 

    Google Scholar
     

  • 15.

    Zhou, L. et al. Biological spiking synapse constructed from solution processed bimetal core-shell nanoparticle based composites. Small 14, 1800288 (2018).

    Article 

    Google Scholar
     

  • 16.

    Choi, S. et al. SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations. Nat. Mater. 17, 335–340 (2018).

    Article 

    Google Scholar
     

  • 17.

    Ohno, T. et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10, 591–595 (2011).

    Article 

    Google Scholar
     

  • 18.

    Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101–108 (2017).

    Article 

    Google Scholar
     

  • 19.

    Kumar, S., Strachan, J. P. & Williams, R. S. Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing. Nature 548, 318–321 (2017).

    Article 

    Google Scholar
     

  • 20.

    Ding, G. L. et al. 2D Metal-organic framework nanosheets with time-dependent and multilevel memristive switching. Adv. Funct. Mater. 29, 1806637 (2019).

    Article 

    Google Scholar
     

  • 21.

    Eryilmaz, S. B. et al. Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array. Front. Neurosci. 8, 205 (2014).

    Article 

    Google Scholar
     

  • 22.

    Torrejon, J. et al. Neuromorphic computing with nanoscale spintronic oscillators. Nature 547, 428–431 (2017).

    Article 

    Google Scholar
     

  • 23.

    van de Burgt, Y. et al. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414–418 (2017).

    Article 

    Google Scholar
     

  • 24.

    Yang, J. J. & Xia, Q. Organic electronics: battery-like artificial synapses. Nat. Mater. 16, 396–397 (2017).

    Article 

    Google Scholar
     

  • 25.

    Fu, Y. M. et al. Hodgkin-Huxley artificial synaptic membrane based on protonic/electronic hybrid neuromorphic transistors. Adv. Biosyst. 2, 1700198 (2018).

    Article 

    Google Scholar
     

  • 26.

    Gkoupidenis, P., Schaefer, N., Garlan, B. & Malliaras, G. G. Neuromorphic functions in PEDOT:PSS organic electrochemical transistors. Adv. Mater. 27, 7176–7180 (2015).

    Article 

    Google Scholar
     

  • 27.

    Lai, Q. et al. Ionic/electronic hybrid materials integrated in a synaptic transistor with signal processing and learning functions. Adv. Mater. 22, 2448–2453 (2010).

    Article 

    Google Scholar
     

  • 28.

    Rivnay, J. et al. Organic electrochemical transistors. Nat. Rev. Mater. 3, 17086 (2018).

    Article 

    Google Scholar
     

  • 29.

    Steriade, M., Nunez, A. & Amzica, F. A novel slow (<1 Hz) oscillation of neocortical neurons in vivo: depolarizing and hyperpolarizing components. J. Neurosci. 13, 3252–3265 (1993).

    Article 

    Google Scholar
     

  • 30.

    Wexler, E. M. & Stanton, P. K. Priming of homosynaptic long-term depression in hippocampus by previous synaptic activity. Neuroreport 4, 591–594 (1993).

    Article 

    Google Scholar
     

  • 31.

    Koneshan, S., Rasaiah, J. C., Lynden-Bell, R. M. & Lee, S. H. Solvent structure, dynamics, and ion mobility in aqueous solutions at 25 °C. J. Phys. Chem. B 102, 4193–4204 (1998).

    Article 

    Google Scholar
     

  • 32.

    Kushmerick, M. J. & Podolsky, R. J. Ionic mobility in muscle cells. Science 166, 1297–1298 (1969).

    Article 

    Google Scholar
     

  • 33.

    Messerschmidt, B., Hsieh, C. H., McIntyre, B. L. & HoudeWalter, S. N. Ionic mobility in an ion exchanged silver-sodium boroaluminosilicate glass for micro-optics applications. J. Non-Crystalline Solids 217, 264–271 (1997).

    Article 

    Google Scholar
     

  • 34.

    Kim, D. & Lee, J.-S. Liquid-based memory and artificial synapse. Nanoscale 11, 9726–9732 (2019).

    Article 

    Google Scholar
     

  • 35.

    Levi, T. & Fujii, T. Microfluidic neurons, a new way in neuromorphic engineering? Micromachines 7, 146 (2016).

    Article 

    Google Scholar
     

  • 36.

    Blitz, D. M., Foster, K. A. & Regehr, W. G. Short-term synaptic plasticity: a comparison of two synapses. Nat. Rev. Neurosci. 5, 630–640 (2004).

    Article 

    Google Scholar
     

  • 37.

    Ellingboe, J. L. & Runnels, J. H. Solubilities of disodium terephthalate in aqueous solutions of sodium carbonate and sodium bicarbonate. J. Chem. Eng. Data 11, 185–187 (1966).

    Article 

    Google Scholar
     

  • 38.

    Cao, C. Y., Wang, H. B., Liu, W. W., Liao, X. Z. & Li, L. Nafion membranes as electrolyte and separator for sodium-ion battery. Int. J. Hydrog. Energ. 39, 16110–16115 (2014).

    Article 

    Google Scholar
     

  • 39.

    Sk, M. A. & Manzhos, S. Exploring the sodium storage mechanism in disodium terephthalate as anode for organic battery using density-functional theory calculations. J. Power Sources 324, 572–581 (2016).

    Article 

    Google Scholar
     

  • 40.

    Park, Y. et al. Sodium terephthalate as an organic anode material for sodium ion batteries. Adv. Mater. 24, 3562–3567 (2012).

    Article 

    Google Scholar
     

  • 41.

    Zhao, L. et al. Disodium terephthalate (Na2C8H4O4) as high performance anode material for low-cost room-temperature sodium-ion battery. Adv. Energy Mater. 2, 962–965 (2012).

    Article 

    Google Scholar
     

  • 42.

    Sah, P., Hestrin, S. & Nicoll, R. A. Properties of excitatory postsynaptic currents recorded in vitro from rat hippocampal interneurones. J. Physiol. 430, 605–616 (1990).

    Article 

    Google Scholar
     

  • 43.

    Schulz, P. E., Cook, E. P. & Johnston, D. Changes in paired-pulse facilitation suggest presynaptic involvement in long-term potentiation. J. Neurosci. 14, 5325–5337 (1994).

    Article 

    Google Scholar
     

  • 44.

    Debanne, D., Guerineau, N. C., Gahwiler, B. H. & Thompson, S. M. Paired-pulse facilitation and depression at unitary synapses in rat hippocampus: quantal fluctuation affects subsequent release. J. Physiol. 491, 163–176 (1996).

    Article 

    Google Scholar
     

  • 45.

    Malenka, R. C. Postsynaptic factors control the duration of synaptic enhancement in area CA1 of the hippocampus. Neuron 6, 53–60 (1991).

    Article 

    Google Scholar
     

  • 46.

    Luo, C. H. & Rudy, Y. A dynamic model of the cardiac ventricular action potential. I. Simulations of ionic currents and concentration changes. Circ. Res. 74, 1071–1096 (1994).

    Article 

    Google Scholar
     

  • 47.

    Bliss, T. V. P. & Collingridge, G. L. A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361, 31–39 (1993).

    Article 

    Google Scholar
     

  • 48.

    Caporale, N. & Dan, Y. Spike timing–dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci. 31, 25–46 (2008).

    Article 

    Google Scholar
     

  • 49.

    Bi, G.-Q. & Poo, M.-M. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18, 10464–10472 (1998).

    Article 

    Google Scholar
     

  • 50.

    Kaeser, P. S. & Sudhof, T. C. RIM function in short- and long-term synaptic plasticity. Biochem. Soc. Trans. 33, 1345–1349 (2005).

    Article 

    Google Scholar
     

  • 51.

    Lisman, J., Cooper, K., Sehgal, M. & Silva, A. J. Memory formation depends on both synapse-specific modifications of synaptic strength and cell-specific increases in excitability. Nat. Neurosci. 21, 309–314 (2018).

    Article 

    Google Scholar
     

  • 52.

    Chang, T., Jo, S. H. & Lu, W. Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano 5, 7669–7676 (2011).

    Article 

    Google Scholar
     

  • 53.

    Kim, M. K. & Lee, J.-S. Short-term plasticity and long-term potentiation in artificial biosynapses with diffusive dynamics. ACS Nano 12, 1680–1687 (2018).

    Article 

    Google Scholar
     

  • 54.

    Park, Y. & Lee, J.-S. Artificial synapses with short- and long-term memory for spiking neural networks based on renewable materials. ACS Nano 11, 8962–8969 (2017).

    Article 

    Google Scholar
     

  • 55.

    Barsoukov, E., et al. in Impedance Spectroscopy: Theory, Experiment, and Applications 2nd edn (eds Barsoukov E. & Macdonald J. R.) (John Wiley & Sons, Inc., New Jersey, NJ, USA, 2018).

  • 56.

    Lück, J. & Latz, A. The electrochemical double layer and its impedance behavior in lithium-ion batteries. Phys. Chem. Chem. Phys. 21, 14753–14765 (2019).

    Article 

    Google Scholar
     

  • 57.

    Cueto-Gómez, L. F., Garcia-Gómez, N. A., Mosqueda, H. A. & Sánchez, E. M. Electrochemical study of TiO2 modified with silver nanoparticles upon CO2 reduction. J. Appl. Electrochem. 44, 675–682 (2014).

    Article 

    Google Scholar
     



  • Source link

    Leave a Reply

    Your email address will not be published. Required fields are marked *