Abstract
The memristor, the fourth fundamental electric element, was conceptually proposed by L. Chua in 1971 and was found in laboratory late in 2008. Recently a special type of memristor was considered to be able to mimic the behavior of neural synapses. In particular, attributed to the long-term memory of weight changes, the memristor can reproduce the spike-timing-dependent plasticity (STDP) protocol of a synapse, displaying a synaptic modification related to the time interval of pre- and post-synaptic spikes. Not limited to it, we found that the memristor with adaptive thresholds can even mimic higher-order behavior of synapses, realizing the well-known suppression principle of Froemke. This type of memristor can actually express both long-term and short-term plasticities in synapses, which are responsible for the excitation level and the refractory time, respectively. The corresponding dynamical process is governed by a set of ordinary differential equations. Interestingly, the Froemke’s model and our memristor-like model, based on two completely different mechanisms, are found to be quantitatively equivalent. In this chapter we would like to provide this new perspective of looking at synaptic dynamics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117(4), 500–544 (1952)
Chua, L.O.: Memristor-the missing circuit element. IEEE Trans. Circ. Theor. 18(5), 507–519 (1971)
Chua, L.O., Kang, S.M.:. Memristive devices and systems. Proc. IEEE 64, 209–223 (1976)
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) (2011)
Pérez-Carrasco, J.A., Zamarreño-Ramos, C., Serrano-Gotarredona, T., Linares-Barranco, B.: On neuromorphic spiking architectures for asynchronous STDP mem-ristive systems. In: Proceedings of IEEE ISCAS, pp. 1659–1662 (2010)
Gerstner, W., Kempter, R., van Hemmen, J.L., Wagner, H.: A neuronal learning rule for sub-millisecond temporal coding. Nature 383, 76–78 (1996)
Gerstner, W.: Spiking neurons. In: Maass, W., Bishop, C.M. (eds.) Pulsed Neural Networks. MIT Press, Cambridge (1999)
Kempter, R., Gerstner, W., van Hemmen, J.L.: Hebbian learning and spiking neurons. Phys. Rev. E 59, 4498–4514 (1999)
Dayan, P., Abbott, L.F.: Theoretical Neuroscience. MIT Press, Cambridge (2001)
Hebb, D.O.: The Organization of Behavior: A Neuropsychological Theory. Wiley, New York (1949)
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)
Wang, H.X., Gerkin, R.C., Nauen, D.W., Bi, G.Q.: Coactivation and timing-dependent integration of synaptic potentiation and depression. Nat. Neurosci. 8, 187–193 (2005)
Froemke, R.C., Dan, Y.: Spike-timing-dependent synaptic modification induced by natural spike trains. Nature 416, 433–438 (2002)
Cai, W., Tetzlaff, R.: Neuronal synapse as a memristor: modeling pair- and triplet-based STDP rule. IEEE Trans. Biomed. Circuits Syst. 9(1), 87–95 (2015)
Kandel, E.R., Schwartz, J.H., Jessell, T.M.: Principles of Neural Science, 4th edn. McGraw-Hill, New York (2000)
Pershin, Y.V., Di Ventra, M.: Neuromorphic, digital, and quantum computation with memory circuit elements. Proc. IEEE 100, 2071–80 (2012)
Pershin, Y.V., Di Ventra, M.: Experimental demonstration of associative memory with memristive neural networks. Neural Netw. 23, 881–886 (2010)
Pershin, Y.V., Di Ventra, M.: Practical approach to programmable analog circuits with memristors. IEEE Trans. Circ. Syst. I: Reg. Pap. 57, 1857–1864 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Cai, W., Tetzlaff, R. (2019). Synapse as a Memristor. In: Chua, L., Sirakoulis, G., Adamatzky, A. (eds) Handbook of Memristor Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-76375-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-76375-0_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76374-3
Online ISBN: 978-3-319-76375-0
eBook Packages: Computer ScienceComputer Science (R0)