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Material Memristive Device Circuits with Synaptic Plasticity: Learning and Memory

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Abstract

An important endeavor in modern materials science is the synthesis of adaptive assemblies with information processing capabilities similar to those of biological neural systems. Recent developments concern materials functionally similar to the memristor, a notional electrical circuit whose conductivity is dependent on past activity. This feature is analogous to synaptic plasticity: the ability of neurons to modify their synaptic connections as a result of accumulated experience—the basis of learning and the formation of memory. In this paper, we present the first evidence that memristive device-based organic materials show adaptive behavior similar to biological cognitive systems, using learning in the feeding neural network of the pond snail, Lymnaea stagnalis, as a specific biological reference. The synthetic reproduction of synaptic plasticity reported here can create new paradigms for novel computing systems and give impetus to the search for bio-inspired nanoscale molecular architectures capable of learning and decision making.

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References

  1. Strukov, D. B., Snider, G. S., Stewart, D. R., Williams, R. S. (2008). The missing memristor found. Nature, 453, 80–83.

    Article  Google Scholar 

  2. Berzina, T., Erokhina, S., Camorani, P., Konovalov, O., Erokhin, V., Fontana, M. P. (2009). Electrochemical control of conductivity in an organic memristor: a time-resolved X-ray fluorescence study of ionic drift as a function of applied voltage. ACS Appl Mater, 1, 2115–2118.

    Article  Google Scholar 

  3. Pershin, Y. V., & Di Ventra, M. (2011). Memory effects in complex materials and nanoscale systems. Advances in Physics, 60, 145–227.

    Article  Google Scholar 

  4. Chua, L. (1971). Memristor—the missing circuit element. IEEE Transactions on Circuit Theory, 18, 507–519.

    Article  Google Scholar 

  5. Benjamin, P. R., Staras, K., Kemenes, G. (2000). A systems approach to the cellular analysis of associative learning in the pond snail Lymnaea. Learning & Memory, 7, 124–131.

    Article  Google Scholar 

  6. Ben Jamaa, M. H., Carrara, S., Georgiou, J., Archontas, N., De Micheli, G. (2009). Fabrication of memristors with poly-crystalline silicon nanowires. In: Proc. IEEE Conference on Nanotechnology, 2009, IEEE-Nano 2009. pp 152–154

  7. Sacchetto, D., Ben Jamaa, M.H., Carrara, S., De Micheli, G., Leblebici, Y. (2010). Memristive devices fabricated with silicon nanowire Schottky barrier transistors. Proc. IEEE Int. Symp. Circuits and Systems (ISCAS): 9–12.

  8. Erokhin, V., Berzina, T., Fontana, M. P. (2005). Hybrid electronic device based on polyaniline-polyethylenoxide junction. Journal of Applied Physics, 97, 064501.

    Article  Google Scholar 

  9. Widrow, B., Pierce, W.H., Angell, J.B. (1961). Birth, life, and death in microelectronic systems. Technical report 1552-2/1851-1, Office of Naval Research

  10. Bondar, A. O., Dedosha, L. A., Reznik, O. M., Stepanenkov, A. F. (1968). Simulation of the plasticity of synapses using memistors. Soviet Automatic Control, 13, 47–51.

    Google Scholar 

  11. Thakoor, S., Moopenn, A., Daud, T., Thakoor, A. P. (1990). Solid-state thin-film memistor for electronic neural networks. Journal of Applied Physics, 67, 3132–3135.

    Article  Google Scholar 

  12. Erokhin, V. (2007). Polymer-based adaptive networks. In V. Erokhin, M. K. Ram, & O. Yavuz (Eds.), The new frontiers of organic and composite nanotechnologies (pp. 287–353). Oxford: Elsevier.

    Google Scholar 

  13. Staras, K., Kemenes, G., Benjamin, P. R. (1998). Pattern-generating role for motoneurons in a rhythmically active neuronal network. The Journal of Neuroscience, 18, 3669–3688.

    Google Scholar 

  14. Straub, V. A., & Benjamin, P. R. (2001). Extrinsic modulation and motor pattern generation in a feeding network: a cellular study. The Journal of Neuroscience, 21, 1767–1778.

    Google Scholar 

  15. Yeoman, M. S., Pieneman, A. W., Ferguson, G. P., Ter Maat, A., Benjamin, P. R. (1994). Modulatory role for the serotonergic cerebral giant cells in the feeding system of the snail, Lymnaea. I. Fine wire recording in the intact animal and pharmacology. Journal of Neurophysiology, 72, 1357–1371.

    Google Scholar 

  16. Vavoulis, D. V., Straub, V. A., Kemenes, I., Kemenes, G., Feng, J., Benjamin, P. R. (2007). Dynamic control of a central pattern generator circuit: a computational model of the snail feeding network. The European Journal of Neuroscience, 25, 2805–2818.

    Article  Google Scholar 

  17. Nikitin, E. S., Vavoulis, D. V., Kemenes, I., Marra, V., Pirger, Z., Michel, M., et al. (2008). Persistent sodium current is a nonsynaptic substrate for long-term associative memory. Current Biology, 18, 1221–1226.

    Article  Google Scholar 

  18. Vavoulis, D. V., Nikitin, E. S., Kemenes, I., Marra, V., Feng, J., Benjamin, P. R., et al. (2010). Balanced plasticity and stability of the electrical properties of a molluscan modulatory interneuron after classical conditioning: a computational study. Front Behav Neurosci, 4, 19.

    Google Scholar 

  19. Snider, G. S. (2008). Spike-timimg-dependent learning in memristive nanodevices. In: Proc. IEEE Int. Symp. Nanoscale Architectures, NANOARCH 2008. pp 85–92

  20. Corinto, F., Ascoli, A., Gilli, M. (2010). Memristive based oscillatory associative and dynamic memories. In: Proc. Int. Workshop on cellular nanoscience networks and their applications (CNNA). pp 1–6

  21. Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., Lu, W. (2010). Nanoscale memristor device as synapse in neuromorphic systems. Nano Letters, 10, 1297–1301.

    Article  Google Scholar 

  22. Sharifi, M. J., & Banadaki, Y. M. (2010). General SPICE models for memristor and application to circuit simulation of memristor-based synapses and memory cells. Journal of Circuits Systems and Computers, 19, 407–424.

    Article  Google Scholar 

  23. Alibart, F., Pleutin, S., Guerin, D., Novembre, C., Lefant, S., Lmimount, K., et al. (2010). An organic nanoparticle transistor behaving as a biological synapse. Advanced Functional Materials, 20, 330–337.

    Article  Google Scholar 

  24. Smerieri, A., Berzina, T., Erokhin, V., Fontana, M. P. (2008). A functional polymeric material based on hybrid electrochemically controlled junctions. Materials Science & Engineering C, 28, 18–22.

    Article  Google Scholar 

  25. Berzina, T., Erokhin, V., Fontana, M. P. (2007). Spectroscopic investigation of an electrochemically controlled conducting polymer-solid electrolyte junction. Journal of Applied Physics, 101, 024501.

    Article  Google Scholar 

  26. Camorani, P., Berzina, T., Erokhin, V., Fontana, M. P. (2011). Adaptive polymeric system for Hebbian-type learning. Philosophical Magazine, 91, 2021–2027.

    Article  Google Scholar 

  27. Smerieri, A., Erokhin, V., Fontana, M. P. (2008). Origin of current oscillations in a polymeric electrochemically controlled element. Journal of Applied Physics, 103, 094517.

    Article  Google Scholar 

  28. Erokhin, V., Berzina, T., Camorani, P., Fontana, M. P. (2007). Non-equilibrium electrical behaviour of polymeric electrochemical junctions. Journal of Physics: Condensed Matter, 19, 205111.

    Article  Google Scholar 

  29. Erokhin, V., & Fontana, M. P. (2011). Thin film electrochemical memristive systems for bio-inspired computation. Journal of Computational and Theoretical Nanoscience, 8, 313–330.

    Article  Google Scholar 

  30. Di Ventra, M., Pershin, Y. V., Chua, L. O. (2009). Circuit elements with memory. Memristors, memcapacitors, and meminductors. Proceedings of the IEEE, 97, 1717–1724.

    Article  Google Scholar 

  31. Kemenes, I., Straub, V. A., Nikitin, E. S., Staras, K., O’Shea, M., Kemenes, G., et al. (2006). Role of delayed nonsynaptic neuronal plasticity in long-term associative memory. Current Biology, 16, 1269–1279.

    Article  Google Scholar 

  32. Hebb, D. O. (1961). The organization of behavior. A neurophychological theory (2nd ed.). New York: Wiley.

    Google Scholar 

  33. Zhang, W., & Linden, D. J. (2003). The other side of the engram: experience-driven changes in neuronal intrinsic excitability. Nature Reviews. Neuroscience, 4, 885–900.

    Article  Google Scholar 

  34. Benjamin, P. R., Kemenes, G., Kemenes, I. (2008). Non-synaptic neuronal mechanisms of learning and memory in gastropod mollusks. Frontiers in Bioscience, 13, 4051–4057.

    Article  Google Scholar 

  35. Bailey, C. H., Giustetto, M., Huang, Y. Y., Hawkins, R. D., Kandel, E. R. (2000). Is heterosynaptic modulation essential for stabilizing Hebbian plasticity and memory? Nature Reviews. Neuroscience, 1, 11–20.

    Article  Google Scholar 

  36. Ebong, I., & Mazumder, P. (2010). Memristor based STDP learning network for position detection. In: Proc. Int. Conf. Microelectronics (ICM). pp 292–295.

  37. Erokhin, V., Schüz, A., Fontana, M. P. (2010). Organic memristor and bio-inspired information processing. International Journal of Unconventional Computing, 6, 15–32.

    Google Scholar 

  38. Prins, L. J., Rheinhoudt, D. N., Timmerman, P. (2001). Noncovalent synthesis using hydrogen bonding. Angewandte Chemie. International Edition, 40, 2382–2426.

    Article  Google Scholar 

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Acknowledgments

We acknowledge the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under the FET-OPEN grant agreement BION, number 213219. The authors are grateful to Prof. Almut Schuez and Prof. Valentino Braitenberg for critical reading of the manuscript and useful discussion, and to Mr. Yuri Gunaza for help in the preparation of figures.

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Authors and Affiliations

  1. CNR-IPCF, Rome, 43100, Italy

    Victor Erokhin & Tatiana Berzina

  2. Department of Physics, University of Parma, Viale Usberti 7A, Parma, 43100, Italy

    Victor Erokhin, Tatiana Berzina, Paolo Camorani, Anteo Smerieri & Marco P. Fontana

  3. Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK

    Dimitris Vavoulis & Jianfeng Feng

  4. Centre for Computational Systems Biology, Fudan University, Shanghai, People’s Republic of China

    Jianfeng Feng

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  1. Victor Erokhin
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  2. Tatiana Berzina
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  3. Paolo Camorani
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  4. Anteo Smerieri
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  5. Dimitris Vavoulis
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  6. Jianfeng Feng
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  7. Marco P. Fontana
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Correspondence to Victor Erokhin.

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Erokhin, V., Berzina, T., Camorani, P. et al. Material Memristive Device Circuits with Synaptic Plasticity: Learning and Memory. BioNanoSci. 1, 24–30 (2011). https://doi.org/10.1007/s12668-011-0004-7

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  • DOI: https://doi.org/10.1007/s12668-011-0004-7

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