Implementation of TSTDP with a Parallel Synaptic Circuit Composed of Nanoparticle Memory-TFTs and Memristors

Document Type : Original Article

Authors

Faculty of Engineering, Razi University, Kermanshah, Iran

Abstract

Triplet-based Spike Timing Dependent Plasticity (TSTDP) is an advanced synaptic plasticity rule that results in improved learning capability compared to the conventional pair-based STDP (PSTDP). The TSTDP rule can reproduce the results of many electrophysiological experiments, where the PSTDP fails, and can be implemented using new nano-scale technologies. Nanocrystalline-silicon thin film transistors (TFT) and memristors are of these nano-scale devices which can be integrated into three-dimensions using low-temperature processing. This paper proposes a new hybrid TFT-memristive circuit that implements the TSTDP. The proposed circuit is designed using current/charge driven memristors and nanoparticle memory-TFTs, as the synapse. Our simulation results demonstrate that the proposed hybrid TFT-memristor circuit induces synaptic weight changes that arise due to the timing differences among pairs and triplets of spikes with a close match to realistic biological measurements. The presented memristive design is an initial step towards developing asynchronous TSTDP learning architectures using memristive devices. These architectures may facilitate the implementation of advanced large-scale neuromorphic systems with applications in real world engineering tasks such as pattern classification. be prepared in one or two paragraphs with less than 200 word and figures, tables, equations, and references should be avoided.

Keywords


[1]        P. Livi and G. Indiveri, “A current-mode conductance-based silicon neuron for address-event neuromorphic systems,” International Symposium on Circuits and Systems, (ISCAS), IEEE, pp. 2898–2901, May 2009.
[2]        M. R. Azghadi, S. Al-Sarawi, D. Abbott and N. Iannella, “A neuromorphic vlsi design for spike timing and rate based synaptic plasticity,” Neural Networks, vol. 45, pp. 70-82, September 2013.
[3]        W. Gerstner, R. Ritz and J. L. Hemmen, “Why spikes? Hebbian learning and retrieval of timeresolved excitation patterns,” Biological Cybernetics, vol. 69, issue 5-6, pp. 503-515, September 1993.
[4]        W. Gerstner, R. Kempter, J. Leo van Hemmen and H. Wagner, “A neuronal learning rule for sub-millisecond temporal coding,” Letters to Nature, vol. 383, pp. 76-78, September 1996.
[5]        L. A. Finelli, S. Haney, M. Bazhenov, M. Stopfer and T. J. Sejnowski, “Synaptic learning rules and sparse coding in a model sensory system,” PLoS Computational Biology, vol. 4, no. 4, April 2008.
[6]        G. Indiveri, E. Chicca and R. Douglas, “A vlsi array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity,” IEEE Transactions on Neural Networks, vol. 17, pp. 211-221, 2006.
[7]        J. Fieres, J. Schemmel and K. Meier, “Realizing biological spiking network models in a configurable wafer-scale hardware system,” IEEE International Joint Conferance on Neural Networks, pp. 969- 976, 2008.
[8]        M. Khan, D. Lester, L. Plana, A. Rast, X. Jin, E. Painkras and S. Furber, “Spinnaker: mapping neural networks onto a massively-parallel chip multiprocessor,” IEEE International Joint Conferance on Neural Networks, pp. 2849-2856, 2008.
[9]      مهسا مهراد و میثم زارعی، «ارائه ساختار نوین ترانزیستور اثرمیدان سیلیسیم روی عایق دو گیتی با پنجره اکسید در درین گسترده‌شده به منظور کاربرد در تکنولوژی نانو»، مجله مهندسی برق دانشگاه تبریز، جلد 47، شماره 2، صفحات 733-727، 1396.
[10]   مهسا مهراد و میثم زارعی، «ارائه ساختاری جدید از ترانزیستورهای اثرمیدان در مقیاس نانو به منظور بالا بردن قابلیت اطمینان»، مجله مهندسی برق دانشگاه تبریز، جلد 48، شماره 3، صفحات 1404-1399، 1397.
[11]     D. B. Strukov, G. S. Snider, D. R. Stewart and R. S. Williams, “The missing memristor found,” Nature, vol. 453, pp. 80-83, 2008.
[12]     C, Zamarreño-Ramos, L. A. Camuñas-Mesa, J. A. Pérez-Carrasco, T. Masquelier, T. SerranoGotarredona and B. Linares-Barranco, “On spike-timing-dependent plasticity, memristive devices, and building a self-learning visual cortex,” Frontiers in Neuroscience, vol. 5, 2011.
[13]     R. C. Froemke and Y. Dan, “Spike-timing-dependent synaptic modification induced by natural spike trains,” Nature, vol. 416, pp. 433–438, 2002.
[14]     J. P. Pfister and W. Gerstner, “Triplets of spikes in a model of spike timing-dependent plasticity,” Journal of Neuroscience, vol. 26, pp. 9673–9682, 2006.
[15]     M. R. Azghadi, S. Moradi, D. B. Fasnacht, M. S. Ozdas and G. Indiveri, “Programmable spike-timing-dependent plasticity learning circuits in neuromorphic vlsi architectures,” ACM Journal on Emerging Technologies in Computing Systems, vol. 12, no. 2, Article 17, August 2015.
[16]     M. R. Azghadi, N. Iannella, S. Al-Sarawi and D. Abbott, “Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity,” PLOSONE, vol. 9, no. 2, pp. 1–14, 2014.
[17]     M. R. Azghadi, B. Linares-Barranco, D. Abbott and P. H. W. Leong, “A hybrid cmos-memristor neuromorphic synapse,” IEEE Transactions on Biomedical Circuits and Systems, vol. 11, no. 2, pp. 434-445, 2017.
[18]     S. Aghnout, G. Karimi and M. R. Azghadi, “Modeling triplet spike-timing-dependent plasticity using memristive devices,” Journal of Computational Electronics, vol. 16, issue 2, pp. 401–410, 2017. 
[19]     S. Aghnout and G. Karimi, “Modeling triplet spike timing dependent plasticity using a hybrid tft-memristor neuromorphic synapse,” Integration, the VLSI Journal, vol. 64, pp. 184-191, January 2019.
[20]     M. R. Azghadi, N. Iannella, S. F. Al-Sarawi, G. Indiveri and D. Abbott, “Spike-based synaptic plasticity in silicon: design, implementation, application, and challenges,” Proceedings of IEEE, vol. 102, no. 5, pp. 717-737, May 2014.
[21]     P. Sjöström, G. Turrigiano and S. Nelson, “Rate, timing, and cooperativity jointly determine cortical synaptic plasticity,” Neuron, vol. 32, no. 6, pp. 1149–1164, 2001.
[22]     H. Wang, R. Gerkin, D. Nauen and G. Bi, “Coactivation and timing-dependent integration of synaptic potentiation and depression,” Nature Neuroscience, vol. 8, no. 2, pp. 187–193, 2005.
[23]     Y. Huang, Novel Approaches to Amorphous Silicon Thin Film Transistors for Large Area Electronics, Ph.D. Thesis, Princeton University, 2011.
[24]     Subramaniam, K. D. Cantley, R. A. Chapman, H. Stiegler and E. M.Vogel, “Submicron ambipolar  nanocrystalline-silicon thin-film transistors and inverters,” IEEE Transactions on Electron Device, vol. 59, no. 2, pp. 359–366, 2012.
[25]     Subramaniam, K. D. Cantley, G. Bersuker, D. C. Gilmer and E. M. Vogel, “Spike-timing-dependent plasticity using biologically realistic action potentials and low-temperature materials,” IEEE Transactions on Nanotechnology, vol. 12, no. 3, pp. 450-459, 2013.
[26]     K. D. Cantley, A. Subramaniam, H. J. Stiegler, R. A. Chapman and E. M. Vogel, “Spike-timing-dependent synaptic plasticity using memristors and nanocrystalline silicon tft memories,” Proceedings of IEEE 11th Conference of Nanotechnology, pp. 421–425, 2011.
[27]     S. D. Brotherton, Introduction to Thin Film Transistors Physics and Technology of TFTs, Springer Publishing, 2013.
[28]     K. D. Cantley, A. Subramaniam, H. J. Stiegler, R. A. Chapman, and E. M.Vogel, “Spice simulation of nanoscale non-crystalline silicon tfts in spiking neuron circuits,” Proceedings of 53rd IEEE International Midwest Symposium on Circuits and Systems, pp. 1202–1205, 2010.
[29]     K. D. Cantley, A. Subramaniam and E. M. Vogel, “Spike timing-dependent plasticity using memristors and nano-crystalline silicon tft memories,” Nanoelectronic Device Applications Handbook, J. E. Morris and K. Iniewski, Editors, CRC Press, Taylor and Francis Group, 2013.
[30]     L. Chua and S. M. Kang, “Memristive devices and systems,” Proceedings of the IEEE, vol. 64, no. 2, pp. 209–223, 1976.
[31]     M. R. Azghadi, N. Iannella, S. Al-Sarawi, G. Indiveri, and D. Abbott, “Spike-based synaptic plasticity in silicon: design, implementation, application, and challenges,” Proceedings of the IEEE, vol. 102, no. 5, pp. 717-737, 2014.
[32]     G. Bi and M. Poo, “Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type,” Journal of Neuroscience, vol. 18, no. 24, pp. 10464–10472, 1998.
[33]     H. Markram, J. Lubke, M. Frotscher and B. Sakmann, “Regulation of synaptic efficacy by coincidence of postsynaptic aps and epsps,” Science (80), vol. 275, no. 5297, pp. 213–215, 1997.
[34]     M. R. Azghadi, S. Al-Sarawi, N. Iannella and D. Abbott, “Efficient design of triplet based spike-timing dependent plasticity,” International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–7, 2012.
[35]     C. Mead, Analog VLSI and Neural Systems, Addison-Wesley Publishing, 1989.