Skip to main content
Log in

Signs of memory in a plastic frustrated Kuramoto model of neurons

  • Original paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

We study the effects brought about by adding frustration to the plastic Kuramoto model of neurons; i.e. we simulate for the first time the plastic Kuramoto–Sakaguchi model, in which the nonlocal coupling matrix for the phases is determined from the spike-timing-dependent plasticity rule. We find that frustration leads to the birhythmicity of the synchronous states. The multistability in the dynamics, with frustration as its crucial element, results in hysteresis, even when the learning rule is symmetric. Fine structure in the hysteresis loop reflects the complexity of the underlying energy landscape. A pacemaker is used to probe this landscape. As the neurons spontaneously self-organise, memory is formed by the configuration of synaptic strengths embodied in such synchronous states. We thus have a simple model of synaptic memory.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Kandel, E.R.: The biology of memory: a forty-year perspective. J. Neurosci. 29, 12748 (2009)

    Google Scholar 

  2. Kandel, E.R.: Cellular Basis of Behavior: An Introduction to Behavioral Neurobiology. W. H. Freeman, San Francisco (1976)

    Google Scholar 

  3. Hebb, D.O.: The Organization of Behavior. Wiley, New York (1949)

    Google Scholar 

  4. 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 (1998)

    Google Scholar 

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

    Google Scholar 

  6. Song, S., Miller, K.D., Abbott, L.F.: Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919 (2000)

    Google Scholar 

  7. Zhigulin, V.P., Rabinovich, M.I.: An important role of spike timing dependent synaptic plasticity in the formation of synchronized neural ensembles. Neurocomputing 58–60, 373 (2004)

    MathSciNet  Google Scholar 

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

    Google Scholar 

  9. Gjorgjieva, J., Clopath, C., Audet, J., Pfister, J.-P.: A triplet spike-timing-dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations. PNAS 108, 19383 (2011)

    Google Scholar 

  10. Lisman, J.E., Zhabotinsky, A.M.: A model of synaptic memory: a CaMKII/PP1 switch that potentiates transmission by organizing an AMPA receptor anchoring assembly. Neuron 2, 191 (2001)

    Google Scholar 

  11. Shouval, H.Z., Bear, M.F., Cooper, L.N.: A unified model of NMDA receptor-dependent bidirectional synaptic plasticity. PNAS 99, 10831 (2002)

    Google Scholar 

  12. Josephson, B.D.: Possible new effects in superconductive tunnelling. Phys. Lett. 1, 251 (1962)

    MATH  Google Scholar 

  13. Kuramoto, Y.: Chemical Oscillations, Waves, and Turbulance. Chemistry Series. Dover, New York (1984)

    MATH  Google Scholar 

  14. Kuramoto, Y.: Self-entrainment of a population of coupled non-linear oscillators. In: Araki, H. (ed.) International Symposium on Mathematical Problems in Theoretical Physics. Lecture Notes in Physics, vol. 39, p. 420. Springer, New York (1975)

    Google Scholar 

  15. Wiesenfeld, K., Colet, P., Strogatz, S.H.: Frequency locking in Josephson arrays: connection with the Kuramoto model. Phys. Rev. E 57, 1563 (1998)

    Google Scholar 

  16. Maistrenko, Yu.L., Lysyansky, B., Hauptmann, C., Burylko, O., Tass, P.A.: Multistability in the Kuramoto model with synaptic plasticity. Phys. Rev. E 75, 066207 (2007)

  17. Li, F., Liu, Q., Guo, H., Zhao, Y., Tang, J., Ma, J.: Simulating the electric activity of FitzHugh–Nagumo neuron by using Josephson junction model. Nonlinear Dyn. 69, 2169–2179 (2012)

    MathSciNet  Google Scholar 

  18. Ma, J., Tang, J.: A review for dynamics in neuron and neuronal network. Nonlinear Dyn. 89, 1569 (2017)

    MathSciNet  Google Scholar 

  19. Ma, J., Zhou, P., Ahmad, B., Ren, G., Wang, C.: Chaos and multi-scroll attractors in RCL-shunted junction coupled Jerk circuit connected by memristor. PLoS ONE 13, e0191120 (2018)

    Google Scholar 

  20. Ma, J., Yang, Z., Yang, L., Tang, J.: A physical view of computational neurodynamics. J. Zhejiang Univ. Sci. A (Appl. Phys. Eng.) 20, 639 (2019)

    Google Scholar 

  21. Sakaguchi, H., Kuramoto, Y.: A soluble active rotater model showing phase transitions via mutual entertainment. Prog. Theor. Phys. 76, 576 (1986)

    Google Scholar 

  22. Kuramoto, Y., Battogtokh, D.: Coexistence of coherence and incoherence in nonlocally coupled phase oscillators. Nonlinear Phenom. Complex Syst. 5, 380 (2002)

    Google Scholar 

  23. Abrams, D.M., Strogatz, S.H.: Chimera states for coupled oscillators. Phys. Rev. Lett. 93, 174102 (2004)

    Google Scholar 

  24. Panaggio, M.J., Abrams, D.M.: Chimera states: coexistence of coherence and incoherence in networks of coupled oscillators. Nonlinearity 28, R67 (2015)

    MathSciNet  MATH  Google Scholar 

  25. Omel’chenko, O.E.: The mathematics behind chimera states. Nonlinearity 31, R121 (2018)

    MathSciNet  MATH  Google Scholar 

  26. Huo, S., Tian, C., Kang, L., Liu, Z.: Chimera states of neuron networks with adaptive coupling. Nonlinear Dyn. 96, 75 (2019)

    Google Scholar 

  27. Mihara, A., Medrano-T, R.O.: Stability in the Kuramoto–Sakaguchi model for finite networks of identical oscillators. Nonlinear Dyn. 98, 539 (2019)

    MATH  Google Scholar 

  28. Huang, X., Gao, J., Sun, Y.-T., Zheng, Z.-G., Xu, C.: Effects of frustration on explosive synchronization. Front. Phys. 11, 110504 (2016)

    Google Scholar 

  29. Kundu, P., Khanra, P., Hens, C., Pal, P.: Transition to synchrony in degree-frequency correlated Sakaguchi–Kuramoto model. Phys. Rev. E 96, 052216 (2017)

    MathSciNet  Google Scholar 

  30. Zhu, L.: Synchronization dynamics in the Sakaguchi–Kuramoto oscillator network with frequency mismatch rules. J. Appl. Math. Phys. 8, 259 (2020)

    Google Scholar 

  31. Boaretto, B.R.R., Budzinski, R.C., Prado, T.L., Lopes, S.R.: Mechanism for explosive synchronization of neural networks. Phys. Rev. E 100, 052301 (2019)

    Google Scholar 

  32. Yeung, M.K.S., Strogatz, S.H.: Time delay in the Kuramoto model of coupled oscillators. Phys. Rev. Lett. 82, 648 (1999)

    Google Scholar 

  33. Jeong, S.-O., Ko, T.-W., Moon, H.-T.: Time-delayed spatial patterns in a two-dimensional array of coupled oscillators. Phys. Rev. Lett. 89, 154104 (2002)

    Google Scholar 

  34. Breakspear, M., Heitmann, S., Daffertshofer, A.: Generative models of cortical oscillations: neurobiological implications of the Kuramoto model. Front. Hum. Neurosci. 4, 190 (2010)

    Google Scholar 

  35. Ratas, I., Pyragas, K.: Eliminating synchronization in bistable networks. Nonlinear Dyn. 83, 1137 (2016)

    MathSciNet  Google Scholar 

  36. Aoki, T., Aoyagi, T.: Self-organized network of phase oscillators coupled by activity-dependent interactions. Phys. Rev. E 84, 066109 (2011)

    Google Scholar 

  37. Masuda, N., Kori, H.: Formation of feedforward networks and frequency synchrony by spike-timing-dependent plasticity. J. Comput. Neurosci. 22, 327 (2007)

    MathSciNet  Google Scholar 

  38. Seliger, P., Young, S.C., Tsimring, L.S.: Plasticity and learning in a network of coupled phase oscillators. Phys. Rev. E 65, 041906 (2002)

    MathSciNet  MATH  Google Scholar 

  39. Aguirre, L.A., Freitas, L.: Control and observability aspects of phase synchronization. Nonlinear Dyn. 91, 2203 (2018)

    Google Scholar 

  40. Wang, C., Tang, J., Ma, J.: Minireview on signal exchange between nonlinear circuits and neurons via field coupling. Eur. Phys. J. Spec. Top. 228, 1907 (2009)

    Google Scholar 

  41. Le Bon-Jego, M., Yuste, R.: Persistently active, pacemaker-like neurons in neocortex. Front. Neurosci. 1, 123 (2007)

    Google Scholar 

  42. Brocard, F., et al.: Activity-dependent changes in extracellular Ca\(^{2+}\) and K\(^{+}\) reveal pacemakers in the spinal locomotor-related network. Neuron 77, 1047 (2013)

    Google Scholar 

  43. Penn, Y., Segal, M., Moses, E.: Network synchronization in hippocampal neurons. PNAS 113, 3341 (2016)

    Google Scholar 

  44. Paladini, C.A., et al.: Dopamine controls the firing pattern of dopamine neurons via a network feedback mechanism. Proc. Natl. Acad. Sci. 100, 2866 (2003)

    Google Scholar 

  45. Alving, B.O.: Spontaneous activity in isolated somata of Aplysia pacemaker neurons. J. Gen. Physiol. 51, 29 (1968)

    Google Scholar 

  46. Takahashi, Y.K., Kori, H., Masuda, K.: Self-organization of feed-forward structure and entrainment in excitatory neural networks with spike-timing-dependent plasticity. Phys. Rev. E 79, 051904 (2009)

    MathSciNet  Google Scholar 

  47. Teitel, S., Jayaprakash, C.: Phase transitions in frustrated two-dimensional XY models. Phys. Rev. B 27, 598 (1983)

    Google Scholar 

  48. Watanabe, S., Strogatz, S.H.: Integrability of a globally coupled oscillator array. Phys. Rev. Lett. 70, 2391 (1993)

    MathSciNet  MATH  Google Scholar 

  49. Watanabe, S., Strogatz, S.H.: Constants of motion for superconducting Josephson arrays. Physica D 74, 197 (1994)

    MATH  Google Scholar 

  50. Shukrinov, Yu.M., Botha, A.E., Medvedeva, S.Yu., Kolahchi, M.R., Irie, A.: Structured chaos in a devil’s staircase of the Josephson junction. Chaos 24, 033115 (2014)

  51. Kori, H., Mikhailov, A.S.: Entrainment of randomly coupled oscillator networks by a pacemaker. Phys. Rev. Lett. 93, 254101 (2004)

    Google Scholar 

  52. Angeli, D., Ferrell Jr., J.E., Sontag, E.D.: Detection of multistability, bifurcations, and hysteresis in a large class of biological positive-feedback systems. PNAS 101, 1822 (2004)

    Google Scholar 

  53. Lisman, J.E.: A mechanism for memory storage insensitive to molecular turnover: a bistable autophosphorylating kinase. Proc. Natl. Acad. Sci. 82, 3055 (1985)

    Google Scholar 

  54. Michalski, P.J.: First demonstration of bistability in CaMKII, a memory-related kinase. Biophys. J. 106, 1233 (2014)

    Google Scholar 

  55. Lisman, J., Raghavachari, S.: Biochemical principles underlying the stable maintenance of LTP by the CaMKII/NMDAR complex. Brain Res. 1621, 51 (2015)

    Google Scholar 

Download references

Acknowledgements

M. A. wishes to thank Morad Biagooi for his kindness in providing access to computational facilities. M. R. K. is grateful to Yury Shukrinov for early discussions on birhythmicity. A. E. B. acknowledges that this work was supported by the National Research Foundation of South Africa (Grant Number 119186).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. E. Botha.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: The two-oscillator model

Appendix: The two-oscillator model

We show analytically, how the bistability in the two-oscillator model comes about. For the two-oscillator model we have \(N=2\) and \(n=1\) in Eqs. (13), giving

$$\begin{aligned} \dot{\theta }_{1}= & {} \omega _{1}+c_{12}\sin \left( \theta _{2}-\theta _{1}-\sigma \right) \end{aligned}$$
(5)
$$\begin{aligned} \dot{\theta }_{2}= & {} \omega _{2}+c_{21}\sin \left( \theta _{1}-\theta _{2}-\sigma \right) \end{aligned}$$
(6)

Now, for a synchronous state, we require \(\dot{\theta }_{1}=\dot{\theta }_{2}\). By subtracting Eq. (6) from Eq. (5), we then find

$$\begin{aligned} \Delta \omega = c_{21}\sin \left( \theta _{1}-\theta _{2}-\sigma \right) -c_{12}\sin \left( \theta _{2}-\theta _{1}-\sigma \right) , \end{aligned}$$
(7)

where we have written \(\Delta \omega =\omega _{1}-\omega _{2}\).

Fig. 7
figure 7

Here, we see the bistability in coupling and one of the firing rates, as well as the phase differences. For instance, when \(\theta _{1}\) leads, \(c_{12} = 0, c_{21} = \alpha \) and \(\dot{\theta _2}\) is forced to adapt to the pacemaker frequency, \(\omega _1\). Here we have put \(\omega _2 = 0\), for convenience

If \(\theta _{1}\) leads, we have \(\Delta \theta _{12}=\theta _{2}-\theta _{1}<0\) and \(\Delta \theta _{21}=-\Delta \theta _{12}>0\). Thus, from Eqs. (2, 3), we find

$$\begin{aligned} \dot{c}_{12}= & {} -\epsilon c_{12}\exp \left( \frac{\Delta \theta _{12}}{\tau } \right) \end{aligned}$$
(8)
$$\begin{aligned} \dot{c}_{21}= & {} \epsilon \left( \alpha -c_{21}\right) \exp \left( -\frac{ \Delta \theta _{21}}{\tau }\right) \end{aligned}$$
(9)

Since \(\Delta \theta _{12}\) is constant for the synchronous solution, the last two equations are decoupled and can be solved analytically. Their steady state solution gives \( c_{12}=0 \) and \( c_{21}=\alpha \). Substituting these coupling constants back into Eq. (7), then gives

$$\begin{aligned} \Delta \omega = \alpha \sin \left( \Delta \theta _{21}-\sigma \right) . \end{aligned}$$
(10)

Similarly, if \(\theta _{2}\) leads, we find

$$\begin{aligned} \Delta \omega = \alpha \sin \left( \Delta \theta _{21}+\sigma \right) . \end{aligned}$$
(11)

The intercepts with \(\Delta \theta \) in Fig. 4 occur at \(\sigma \), and \(-\sigma \); for \(\Delta \omega \), they are at \(-\alpha \sin (\sigma )\), and \(\alpha \sin (\sigma )\). The role of frustration is thus clear. If \(|\Delta \omega |\le \alpha \sin \sigma \), two stable solutions exist, and the system moves between them as the sign of \(\Delta \theta \) changes. The bistability is illustrated in Fig. 7.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ansariara, M., Emadi, S., Adami, V. et al. Signs of memory in a plastic frustrated Kuramoto model of neurons. Nonlinear Dyn 100, 3685–3694 (2020). https://doi.org/10.1007/s11071-020-05705-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-020-05705-4

Keywords

Navigation