Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks

  • Manuel Reyes-SanchezEmail author
  • Rodrigo Amaducci
  • Irene Elices
  • Francisco B. Rodriguez
  • Pablo VaronaEmail author
Original Article


Hybrid circuits built by creating mono- or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this paper, we present a set of algorithms for the automatic adaptation of model neurons and connections in the creation of hybrid circuits with living neural networks. The algorithms perform model time and amplitude scaling, real-time drift adaptation, goal-driven synaptic and model tuning/calibration and also automatic parameter mapping. These algorithms have been implemented in RTHybrid, an open-source library that works with hard real-time constraints. We provide validation examples by building hybrid circuits in a central pattern generator. The results of the validation experiments show that the proposed dynamic adaptation facilitates closed-loop communication among living and artificial model neurons and connections, and contributes to characterize system dynamics, achieve control, automate experimental protocols and extend the lifespan of the preparations.


Interacting living and model neurons Closed-loop neuroscience Experiment automation Dynamic clamp Hybrid circuit real-time dynamic adaptation 



This work was supported by MINECO/ FEDER PGC2018-095895-B-I00, DPI2015-65833-P, TIN2017-84452-R and ONRG grant N62909-14-1-N279.

Supplementary material

(MP4 14.8 MB)


  1. Amaducci, R., Reyes-Sanchez, M., Elices, I., Rodriguez, F.B., Varona, P. (2019). Rthybrid: a standardized and open-source real-time software model library for experimental neuroscience. Frontiers in Neuroinformatics, 13, 11. Scholar
  2. Arroyo, D., Chamorro, P., Amigo, J.M., Rodriguez, F.B., Varona, P. (2013). Event detection, multimodality and non-stationarity: ordinal patterns, a tool to rule them all? The European Physical Journal Special Topics, 222(2), 457–472. Scholar
  3. Arsiero, M., Lúscher, H.R., Giugliano, M. (2007). Real-time closed-loop electrophysiology: towards new frontiers in in vitro investigations in the neurosciences. Archives italiennes de biologie, 145(3), 193–209.PubMedGoogle Scholar
  4. Bettencourt, J.C., Lillis, K.P., Stupin, L.R., White, J.A. (2008). Effects of imperfect dynamic clamp: computational and experimental results. Journal of Neuroscience Methods, 169(2), 282–289. Scholar
  5. Brette, R., Piwkowska, Z., Monier, C., Rudolph-Lilith, M., Fournier, J., Levy, M., Frégnac, Y, Bal, T., Destexhe, A. (2008). High-resolution intracellular recordings using a real-time computational model of the electrode. Neuron, 59(3), 379–391.CrossRefGoogle Scholar
  6. Broccard, F.D., Joshi, S., Wang, J., Cauwenberghs, G. (2017). Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems. Journal of Neural Engineering, 14(4), 41,002. Scholar
  7. Brochini, L., Carelli, P.V., Pinto, R.D. (2011). Single synapse information coding in intraburst spike patterns of central pattern generator motor neurons. Journal of Neuroscience, 31(34), 12,297–12,306.CrossRefGoogle Scholar
  8. Chamorro, P., Levi, R., Rodriguez, F.B., Pinto, R.D., Varona, P. (2009). Real-time activity-dependent drug microinjection. BMC Neuroscience, 10(1), P296. Scholar
  9. Chamorro, P., Muñiz, C, Levi, R., Arroyo, D., Rodriguez, F.B., Varona, P. (2012). Generalization of the dynamic clamp concept in neurophysiology and behavior. PLoS ONE, 7, 7. Scholar
  10. Christini, D.J., Stein, K.M., Markowitz, S.M., Lerman, B.B. (1999). Practical real-time computing system for biomedical experiment interface. Annals of Biomedical Engineering. Scholar
  11. Couto, J., Linaro, D., De Schutter, E., Giugliano, M. (2015). On the firing rate dependency of the phase response curve of rat purkinje neurons in vitro. PLoS Computational Biology, 11(3), e1004,112.CrossRefGoogle Scholar
  12. Destexhe, A., & Bal, T. (2009). Dynamic-clamp: from principles to applications. From Principles to Applications, 1, 443. Scholar
  13. Elices, I., & Varona, P. (2015). Closed-loop control of a minimal central pattern generator network. Neurocomputing, 170, 55–62. Scholar
  14. Elices, I., & Varona, P. (2017). Asymmetry factors shaping regular and irregular bursting rhythms in central pattern generators. Frontiers in Computational Neuroscience, 11.
  15. Elices, I., Levi, R., Arroyo, D., Rodriguez, F.B., Varona, P. (2019). Robust dynamical invariants in sequential neural activity. Scientific Reports, 9(1), 9048. Scholar
  16. Ghigliazza, R.M., & Holmes, P. (2004). Minimal models of bursting neurons: how multiple currents, conductances, and timescales affect bifurcation diagrams. SIAM Journal on Applied Dynamical Systems. Scholar
  17. Golowasch, J., Casey, M., Abbott, L.F., Marder, E. (1999). Network stability from activity-dependent regulation of neuronal conductances. Neural Computation, 11 (5), 1079–1096. Scholar
  18. Gomez-Gonzalez, J., Destexhe, A., Bal, T. (2014). Application of active electrode compensation to perform continuous voltage-clamp recordings with sharp microelectrodes. Journal of Neural Engineering, 11, 5. Scholar
  19. Grashow, R., Brookings, T., Marder, E. (2010). Compensation for variable intrinsic neuronal excitability by circuit-synaptic interactions. Journal of Neuroscience, 30(27), 9145–9156.CrossRefGoogle Scholar
  20. Hindmarsh, J.L., & Rose, R.M. (1984). A model of neuronal bursting using three coupled first order differential equations.Google Scholar
  21. Hooper, R.M., Tikidji-Hamburyan, R.A., Canavier, C.C., Prinz, A.A. (2015). Feedback control of variability in the cycle period of a central pattern generator. Journal of Neurophysiology, 114(5), jn.00,365/2015. Scholar
  22. Hull, T.E., Enright, W.H., Fellen, B.M., Sedgwick, A.E. (1972). Comparing numerical methods for ordinary differential equations. SIAM Journal on Numerical Analysis, 9(4), 603–637.CrossRefGoogle Scholar
  23. Izhikevich, E. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6), 1569–1572. Scholar
  24. Kemenes, I., Marra, V., Crossley, M., Samu, D., Staras, K., Kemenes, G., Nowotny, T. (2011). Dynamic clamp with StdpC software. Nature Protocols, 6(3), 405–417.CrossRefGoogle Scholar
  25. Krook-Magnuson, E., Armstrong, C., Oijala, M., Soltesz, I. (2013). On-demand optogenetic control of spontaneous seizures in temporal lobe epilepsy. Nature Communications, 4, 1376. Scholar
  26. Le Masson, G, Renaud-Le Masson, S, Debay, D, Bal, T. (2002). Feedback inhibition controls spike transfer in hybrid thalamic circuits. Nature, 417(6891), 854–858.CrossRefGoogle Scholar
  27. Linaro, D., Couto, J., Giugliano, M. (2014). Command-line cellular electrophysiology for conventional and real-time closed-loop experiments. Journal of Neuroscience Methods, 230, 5–19.CrossRefGoogle Scholar
  28. Linaro, D., Couto, J., Giugliano, M. (2015). Real-time electrophysiology: using closed-loop protocols to probe neuronal dynamics and beyond. JoVE (Journal of Visualized Experiments), e52,320–e52,320.
  29. Marder, E., & Calabrese, R.L. (1996). Principles of rhythmic motor pattern generation. Physiological Reviews, 76, 687–717.CrossRefGoogle Scholar
  30. Mishchenko, M.A., Gerasimova, S.A., Lebedeva, A.V., Lepekhina, L.S., Pisarchik, A.N., Kazantsev, V.B. (2018). Optoelectronic system for brain neuronal network stimulation. PLOS ONE 13(6), e0198, 396. Scholar
  31. Muñiz, C, Arganda, S, Rodriguez, F.B., de Polavieja, G.G., Varona, P. (2005). Realistic stimulation through advanced dynamic-clamp protocols. Lecture Notes in Computer Science, 3561, 95–105. Scholar
  32. Muñiz, C, Rodriguez, F.B., Varona, P. (2009). RTBiomanager: a software platform to expand the applications of real-time technology in neuroscience. BMC Neuroscience, 10(Suppl 1), P49. Scholar
  33. Norman, S.E., Butera, R.J., Canavier, C.C. (2016). Stochastic slowly adapting ionic currents may provide a decorrelation mechanism for neural oscillators by causing wander in the intrinsic period. Journal of Neurophysiology. Scholar
  34. Nowotny, T, & Varona, P. (2012). Dynamic clamp, (pp. 613–621). Netherlands: Springer.Google Scholar
  35. Nowotny, T, & Varona, P. (2014). Dynamic clamp technique. In Encyclopedia of Computational Neuroscience (pp. 1–4). New York: Springer, DOI Google Scholar
  36. Nowotny, T, Zhigulin, V.P., Selverston, A.I., Abarbanel, H.D.I., Rabinovich, M.I. (2003). Enhancement of synchronization in a hybrid neural circuit by spike-timing dependent plasticity. Journal of Neuroscience, 23(30), 9776–9785. 23/30/9776 [pii].CrossRefGoogle Scholar
  37. Olypher, A., Cymbalyuk, G, Calabrese, R.L. (2006). Hybrid systems analysis of the control of burst duration by low-voltage-activated calcium current in leech heart interneurons. Journal of Neurophysiology. Scholar
  38. Patel, Y.A., George, A., Dorval, A.D., White, J.A., Christini, D.J., Butera, R.J. (2017). Hard real-time closed-loop electrophysiology with the real-time eXperiment interface (RTXI). PLoS Computational Biology, 13, 5. Scholar
  39. Pinto, R.D., Varona, P., Volkovskii, A.R., Szücs, A, Abarbanel, H.D., Rabinovich, M.I. (2000). Synchronous behavior of two coupled electronic neurons. Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 62(2), 2644–2656. Scholar
  40. Pinto, R., Elson, R., Szücs, A, Rabinovich, M., Selverston, A., Abarbanel, H. (2001). Extended dynamic clamp: controlling up to four neurons using a single desktop computer and interface. Journal of Neuroscience Methods, 108(1), 39–48. Scholar
  41. Prinz, A.A., Abbott, L., Marder, E. (2004). The dynamic clamp comes of age. Trends in Neurosciences, 27(4), 218–224. Scholar
  42. Prsa, M., Galiñanes, G.L., Huber, D. (2017). Rapid integration of artificial sensory feedback during operant conditioning of motor cortex neurons. Neuron, 93(4), 929–939.e6. Scholar
  43. Robinson, H.P.C., & Kawai, N. (1993). Injection of digitally synthesized synaptic conductance transients to measure the integrative properties of neurons. Journal of Neuroscience Methods. Scholar
  44. Rulkov, N.F. (2002). Modeling of spiking-bursting neural behavior using two-dimensional map. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 65, 4.CrossRefGoogle Scholar
  45. Sakurai, A., & Katz, P.S. (2017). Artificial synaptic rewiring demonstrates that distinct neural circuit configurations underlie homologous behaviors. Current Biology, 27(12), 1721–1734.e3. Scholar
  46. Samu, D., Marra, V., Kemenes, I., Crossley, M., Kemenes, G., Staras, K., Nowotny, T. (2012). Single electrode dynamic clamp with StdpC. Journal of Neuroscience Methods, 211(1), 11–21. Scholar
  47. Selverston, A.I. (2005). A neural infrastructure for rhythmic motor patterns. Cellular and Molecular Neurobiology, 25(2), 223– 244.CrossRefGoogle Scholar
  48. Sharp, A.A., O’Neil, M.B., Abbott, L.F., Marder, E. (1993). The dynamic clamp: artificial conductances in biological neurons. Trends in Neurosciences, 16(10), 389–394. Scholar
  49. Szücs, A, Varona, P., Volkovskii, A.R., Abarbanel, H.D.I., Rabinovich, M.I., Selverston, A.I. (2000). Interacting biological and electronic neurons generate realistic oscillatory rhythms. Neuroreport, 11(3), 563–569. Scholar
  50. Varona, P., Torres, J.J., Abarbanel, H.D.I., Rabinovich, M.I., Elson, R.C. (2001). Dynamics of two electrically coupled chaotic neurons: experimental observations and model analysis. Biological Cybernetics, 84 (2), 91–101. Scholar
  51. Varona, P., Arroyo, D., Rodriguez, F.B., Nowotny, T. (2016). Chapter 6 - online event detection requirements in closed-loop neuroscience. In Hady, A.E. (Ed.) Closed loop neuroscience (pp. 81–91). San Diego: Academic Press, DOI CrossRefGoogle Scholar
  52. Wang, S., Chandrasekaran, L., Fernandez, F.R., White, J.A., Canavier, C.C. (2012). Short conduction delays cause inhibition rather than excitation to favor synchrony in hybrid neuronal networks of the entorhinal cortex. PLoS Computational Biology, 8(1), e1002,306.CrossRefGoogle Scholar
  53. Yarom, Y. (1991). Rhythmogenesis in a hybrid system-interconnecting an olivary neuron to an analog network of coupled oscillators. Neuroscience, 44(2), 263–275.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica SuperiorUniversidad Autónoma de MadridMadridSpain

Personalised recommendations