Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Modeling triplet spike-timing-dependent plasticity using memristive devices

  • Published:
Journal of Computational Electronics Aims and scope Submit manuscript

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. This paper proposes a novel memristive circuit that implements the TSTDP rule. The proposed circuit is designed using three voltage (flux)-driven memristors. Simulation results demonstrate that our memristive circuit induces synaptic weight changes that arise due to the timing differences among pairs and triplets of spikes. The presented memristive design is an initial step toward 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Chua, L.O.: Memristor—the missing circuit element. IEEE Trans. Circuit Theory 18(5), 507–519 (1971)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Azghadi, M.R., Al-Sarawi, S., Abbott, D., Iannella, N.: A neuromorphic VLSI design for spike timing and rate based synaptic plasticity. Neural Netw. 45, 70–82 (2013)

    Article  Google Scholar 

  4. Wijekoon, J., Dudek, P.: Compact silicon neuron circuit with spiking and bursting behavior. Neural Netw. 21, 524–534 (2008)

    Article  Google Scholar 

  5. Azghadi, M.R., Moradi, S., Fasnacht, D.B., Ozdas, M.S., Indiveri, G.: Programmable spike-timing-dependent plasticity learning circuits in neuromorphic VLSI architectures. ACM J. Emerg. Technol. Comput. Syst. 12(2) (2015). Article 17

  6. Gerstner, W., Ritz, R., van Hemmen, J.L.: Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns. Biol. Cybernet. 69, 503–515 (1993)

    Article  MATH  Google Scholar 

  7. Gerstner, W., Kempter, R., van Hemmen, J.L., Wagner, H.: A neuronal learning rule for sub-millisecond temporal coding. Nature 383, 76–78 (1996)

    Article  Google Scholar 

  8. Azghadi, M.R., Iannella, N., Al-Sarawi, S., Abbott, D.: Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity. PLoS ONE 9(2), e88326 (2014)

    Article  Google Scholar 

  9. Zamarreño-Ramos, C., Camuñas-Mesa, L.A., Pérez-Carrasco, J.A., Masquelier, T., Serrano-Gotarredona, T., Linares-Barranco, B.: On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex. Front. Neurosci. 5(26), 1–22 (2011)

  10. Pérez-Carrasco, J.A., Zamarreño-Ramos, C., Serrano-Gotarredona, T., Linares-Barranco, B.: On neuromorphic spiking architectures for asynchronous STDP memristive systems. In: Proceedings of IEEE International Symposium on Circuits and Systems, pp. 1659–1662 (2010)

  11. Froemke, R.C., Dan, Y.: Spike-timing-dependent synaptic modification induced by natural spike trains. Nature 416, 433–438 (2002)

    Article  Google Scholar 

  12. Pfister, J.P., Gerstner, W.: Triplets of spikes in a model of spike timing-dependent plasticity. J. Neurosci. 26, 9673–9682 (2006)

    Article  Google Scholar 

  13. Hart, M., Taylor, N., Taylor, J.: Understanding spike time-dependent plasticity: a biologically motivated computational model. Neurocomputing 69, 2005–2016 (2006)

    Article  Google Scholar 

  14. Indiveri, G., Chicca, E., Douglas, R.: A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Trans. Neural Netw. 17(1), 211–221 (2006)

    Article  Google Scholar 

  15. Meng, Y., Zhou, K., Monzon, J., Poon, C.: Iono-neuromorphic implementation of spike-timing-dependent synaptic plasticity. In: 2011 Annual international conference of the IEEE engineering in medicine and biology society, EMBC, pp. 7274–7277 (2011)

  16. Azghadi, M.R., Iannella, N., Al-Sarawi, S.F., Indiveri, G., Abbott, D.: Spike-based synaptic plasticity in silicon: design, implementation, application, and challenges. Proc. IEEE 102(5), 717–737 (2014)

    Article  Google Scholar 

  17. Sjöström, P., Turrigiano, G., Nelson, S.: Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron 32(6), 1149–1164 (2001)

    Article  Google Scholar 

  18. Wang, H., Gerkin, R., Nauen, D., Bi, G.: Coactivation and timing-dependent integration of synaptic potentiation and depression. Nat. Neurosci. 8(2), 187–193 (2005)

    Article  Google Scholar 

  19. Chua, L.: If it’s pinched it’s a memristor. Semicond. Sci. Technol. 29, 104001 (2014)

    Article  Google Scholar 

  20. Saïghi, S., Mayr, C.G., Serrano-Gotarredona, T., Schmidt, H., Lecerf, G., Tomas, J., et al.: Plasticity in memristive devices for spiking neural networks. Front. Neurosci. 9(51), 1–16 (2015)

  21. 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)

    Google Scholar 

  22. Azghadi, M.R., Al-Sarawi, S., Iannella, N., Abbott, D.: Efficient design of triplet based spike-timing dependent plasticity. In: The 2012 international joint conference on neural networks, IJCNN, pp. 1–7, IEEE (2012)

  23. Mead, C.: Analog VLSI and Neural Systems. Addison-Wesley, Boston (1989)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran

    Soraya Aghnout & Gholamreza Karimi

  2. School of Electrical and Information Engineering, The University of Sydney, Sydney, Australia

    Mostafa Rahimi Azghadi

  3. College of Science and Engineering, James Cook University, Townsville, Australia

    Mostafa Rahimi Azghadi

Authors
  1. Soraya Aghnout
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gholamreza Karimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mostafa Rahimi Azghadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gholamreza Karimi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aghnout, S., Karimi, G. & Azghadi, M.R. Modeling triplet spike-timing-dependent plasticity using memristive devices. J Comput Electron 16, 401–410 (2017). https://doi.org/10.1007/s10825-017-0972-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10825-017-0972-0

Keywords

Search

Navigation