, Volume 3, Issue 3, pp 263–280 | Cite as

Effects of random external background stimulation on network synaptic stability after tetanization

A modeling study
  • Zenas C. Chao
  • Douglas J. Bakkum
  • Daniel A. Wagenaar
  • Steve M. Potter
Original Article


We constructed a simulated spiking neural network model to investigate the effects of random background stimulation on the dynamics of network activity patterns and tetanus induced network plasticity. The simulated model was a “leaky integrate-and-fire” (LIF) neural model with spike-timing-dependent plasticity (STDP) and frequency-dependent synaptic depression. Spontaneous and evoked activity patterns were compared with those of living neuronal networks cultured on multielectrode arrays. To help visualize activity patterns and plasticity in our simulated model, we introduced new population measures called Center of Activity (CA) and Center of Weights (CW) to describe the spatio-temporal dynamics of network-wide firing activity and network-wide synaptic strength, respectively. Without random background stimulation, the network synaptic weights were unstable and often drifted after tetanization. In contrast, with random background stimulation, the network synaptic weights remained close to their values immediately after tetanization. The simulation suggests that the effects of tetanization on network synaptic weights were difficult to control because of ongoing synchronized spontaneous bursts of action potentials, or “barrages.” Random background stimulation helped maintain network synaptic stability after tetanization by reducing the number and thus the influence of spontaneous barrages. We used our simulated network to model the interaction between ongoing neural activity, external stimulation and plasticity, and to guide our choice of sensory-motor mappings for adaptive behavior in hybrid neural-robotic systems or “hybrots.”

Index Entries

Cultured neural network spike-timing-dependent plasticity (STDP) frequency-dependent depression multi-electrode array (MEA) spatio-temporal dynamics tetanization model plasticity cortex bursting population coding 


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  1. Bakkum, D. J., Shkolnik, A. C., Ben-Ary, G., Gamblen, P., Demarse, T. B., and Potter, S. M. (2004) Removing some ‘A’ from AI: embodied cultured networks. In: Embodied Artificial Intelligence. Iida, F., Steels, L., Pfeifer, R. (eds.) Springer-Verlag, Berlin.Google Scholar
  2. Baruchi, I. and Ben-Jacob, E. (2004) Functional holography of recorded neuronal networks activity. J. Neuroinformat. 2, 333–352.CrossRefGoogle Scholar
  3. Beggs, J. and Plenz, D. (2004) Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures. J. Neurosci. 24, 5216–5229.PubMedCrossRefGoogle Scholar
  4. Bliss, T. V. and Collingridge, G. L. A. (1993) A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361, 31–39.PubMedCrossRefGoogle Scholar
  5. Bliss, T. V. and Lømo, T. (1973) Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol. Lond. 232, 331–356.PubMedGoogle Scholar
  6. Brooks, R. (1999) In: Cambrian Intelligence, the Early History of the New AI: The MIT press, Cambridge.Google Scholar
  7. Brown, E. N., Kass, R. E., and Mitra, P. P. (2004) Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat. Neurosci. 7, 456–461.PubMedCrossRefGoogle Scholar
  8. Caminiti, R., Johnson, P. B., Burnod, Y., Galli, C., and Ferraina, S. (1990) Shifts of preferred directions of premotor cortical cells with arm movements performed across the workspace. Exp. Brain Res. 83, 228–232.PubMedCrossRefGoogle Scholar
  9. Davies, D. L. and Bouldin, D. W. (1979) A cluster separation measure. IEEE Trans. Pattern Anal. Machine Intell. 1, 224–227.Google Scholar
  10. DeMarse, T. B., Wagenaar, D. A., Blau, A. W., and Potter, S. M. (2001) The neurally controlled animat: biological brains acting with simulated bodies. Autonomous Robots 11, 305–310.CrossRefPubMedGoogle Scholar
  11. Georgopoulos, A. P. (1994) Population activity in the control of movement. In: Selectionism and the Brain, Academic Press, San Diego, pp. 103–119.Google Scholar
  12. Gross, G. W. and Kowalski, J. M. (1999) Origins of activity patterns in self-organizing neuronal networks in vitro. J. Intell. Mat. Sys. and Struct. 10, 558–564.CrossRefGoogle Scholar
  13. Gross, G. W., Rhoades, B. K., and Kowalski, J. K. (1993a) Dynamics of burst patterns generated by monolayer networks in culture. In: Neurobionics: An Interdisciplinary Approach to Substitute Impaired Functions of the Human Nervous System. Bothe, H. W., Samii, M., Eckmiller, R., (eds.) North-Holland, Amsterdam, pp. 89–121.Google Scholar
  14. Gross, G. W., Rhoades, B. K., Reust, D. L., and Schwalm, F. U. (1993b) Stimulation of monolayer networks in culture through thin-film indiumtin oxide recording electrodes. J. Neurosci. Methods 50, 131–143.PubMedCrossRefGoogle Scholar
  15. Ikegaya, Y., Aaron, G., Cossart, R., et al. (2004) Synfire chains and cortical songs: Temporal modules of cortical activity. Science 304, 559–564.PubMedCrossRefGoogle Scholar
  16. Izhikevich, E. M., Gally, J. A., and Edelman, G. M. (2004) Spike-timing dynamics of neuronal groups. Cereb. Cortex 14, 933–944.PubMedCrossRefGoogle Scholar
  17. Jimbo, Y., Tateno, T., and Robinson, H. P. C. (1999) Simultaneous induction of pathway-specific potentiation and depression in networks of cortical neurons. Biophys. J. 76, 670–678.PubMedCrossRefGoogle Scholar
  18. Kamioka, H., Maeda, E., Jimbo, Y., Robinson, H. P. C., and Kawana, A. (1996) Spontaneous periodic synchronized bursting during formation of mature patterns of connections in cortical cultures. Neurosci. Lett. 206, 109–112.PubMedCrossRefGoogle Scholar
  19. Kawaguchi, H. and Fukunishi, K. (1998) Dendrite classification in rat hippocampal neurons according to signal propagation properties—observation by multichannel optical recording in cultured neuronal networks. Exp. Brain Res. 122, 378–392.PubMedCrossRefGoogle Scholar
  20. Krichmar, J. and Edelman, G. (2002) Machine psychology: autonomous behavior, perceptual categorization and conditioning in a brain-based device. Cereb. Cortex 12, 818–830.PubMedCrossRefGoogle Scholar
  21. Latham, P. E., Richmond, B. J., Nirenberg, S., and Nelson, P. G. (2000) Intrinsic dynamics in neuronal networks. II. Experiment. J. Neurophys. 83, 828–835.Google Scholar
  22. Linden, D. J. (1994) Long-term synaptic depression in the mammalian brain. Neuron 12, 457–472.PubMedCrossRefGoogle Scholar
  23. Madhavan, R., Chao, Z. C., and Potter, S. M. (2005) Spontaneous bursts are better indicators of tetanus-induced plasticity than responses to probe stimuli. Proc. 2nd Intl. IEEE EMBS Conf. On Neural Engineering: 434–437.Google Scholar
  24. Madhavan, R., Wagenaar, D. A., Chao, Z. C., and Potter, S. M. (2003) Control of bursting in dissociated cortical cultures on multi-electrode arrays. Proc. Substrate-Integrated Micro-Electrode Arrays, Denton, TX.Google Scholar
  25. Markram, H., Gupta, A., Uziel, A., Wang, Y., and Tsodyks, M. (1998) Information processing with frequency-dependent synaptic connections. Neurobiology of Learning and Memory 70, 101–112.PubMedCrossRefGoogle Scholar
  26. Marom, S. and Shahaf, G. (2002) Development, learning and memory in large random networks of cortical neurons: Lessons beyond anatomy. Quart. Rev. Biophys. 35, 63–87.CrossRefGoogle Scholar
  27. McIntyre, C. and Grill, W. (2002) Extracellular stimulation of central neurons: influence of stimulus waveform and frequency on neuronal output. J. Neurophys. 88, 1592–1604.Google Scholar
  28. Meyer, J. A. and Wilson, S. W. (1991) From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior. MIT Press, Cambridge.Google Scholar
  29. Natschlager, T., Markram, H., and Maass, W. (2002) Computer models and analysis tools for neural microcircuits. In: A Practical Guide to Neuroscience Databases and Associated Tools.Google Scholar
  30. Pesaran, B., Pezaris, J., Sahani, M., Mitra, P., and Andersen, R. (2002) Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nat. Neurosci. 5, 805–811.PubMedCrossRefGoogle Scholar
  31. Potter, S. M. (2005) Two-photon microscopy for 4D imaging of living neurons. In: Imaging in Neuroscience and Development: A Laboratory Manual (Yuste, R. and Konnerth, A., eds.). Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY: pp. 59–70.Google Scholar
  32. Potter, S. M. and DeMarse, T. B. (2001) A new approach to neural cell culture for long-term studies. J. Neurosci. Methods 110, 17–24.PubMedCrossRefGoogle Scholar
  33. Potter, S. M., Fraser, S. E., and Pine, J. (1997) Animat in a Petri Dish: Cultured Neural Networks for Studying Neural Computation. Proceedings of the Fourth Joint Symposium on Neural Computation, UCSD, pp. 167–174.Google Scholar
  34. Potter, S. M., Lukina, N., Longmuir, K. J., and Wu, Y. (2001) Multi-site two-photon imaging of neurons on multi-electrode arrays. SPIE Proc. 4262, 104–110.CrossRefGoogle Scholar
  35. Potter, S. M., Wagenaar, D. A., and DeMarse, T. B. Closing the loop: stimulation feedback systems for embodied MEA cultures. In: Advances in Network Electrophysiology Using Multi-Electrode Arrays. Taketani, M., Baudry, M. (eds.) Kluwer, New York, in press.Google Scholar
  36. Rambani, K., Booth, M. C., Brown, E. A., Raikov, I., and Potter, S. M. (2005) Custom made multiphoton microscope for long-term imaging of neuronal cultures to explore structural and functional plasticity. Proc. SPIE, 5700: 102–108.CrossRefGoogle Scholar
  37. Schaal, S., Ijspeert, A., Billard, A., Vijayakumar, S., and Hallam, J., Meyer, J. (eds.) (2004) From animals to animats 8: Proceedings of the Eighth International Conference on the Simulation of Adaptive Behavior: Bradford Books, Cambridge, MA.Google Scholar
  38. Segev, R. and Ben-Jacob, E. (2000) Generic modeling of chemotactic based self-wiring of neural networks. Neural Networks 13, 185–199.PubMedCrossRefGoogle Scholar
  39. Shahaf, G. and Marom, S. (2001) Learning in networks of cortical neurons. J. Neurosci. 21, 8782–8788.PubMedGoogle Scholar
  40. Song, S., Miller, K. D., and Abbott, L. F. (2000) Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919–926.PubMedCrossRefGoogle Scholar
  41. Tateno, T. and Jimbo, Y. (1999) Activity-dependent enhancement in the reliability of correlated spike timings in cultured cortical neurons. Biol. Cybern. 80, 45–55.PubMedCrossRefGoogle Scholar
  42. Wagenaar, D. A., Madhavan, R., Pine, J., and Potter, S. (2005) Controlling Bursting in Cortical Cultures with Closed-Loop Multi-Electrode Stimulation. J. Neurosci. 25, 680–688.PubMedCrossRefGoogle Scholar
  43. Wagenaar, D. A., Madhaven, R., and Potter, S. M. (2003) Stimulating news for MEA enthusiasts. In: SIMEA 2003. Denton, TX, USA.Google Scholar
  44. Wagenaar, D. A., Pine, J., and Potter, S. (2004) Effective parameters for stimulation of dissociated cultures using multi-electrode arrays. J. Neurosci. Methods 138, 27–37.PubMedCrossRefGoogle Scholar
  45. Wagenaar, D. A. and Potter, S. M. (2004) A versatile all-channel stimulator for electrode arrays, with real-time control. J. Neural Eng. 1, 39–45.PubMedCrossRefGoogle Scholar
  46. Wong, R. O. L., Meister, M., and Shatz, C. J. (1993) Transient period of correlated bursting activity during development of the mammalian retina. Neuron 11, 923–938.PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press Inc 2005

Authors and Affiliations

  • Zenas C. Chao
    • 1
  • Douglas J. Bakkum
    • 1
  • Daniel A. Wagenaar
    • 2
  • Steve M. Potter
    • 1
  1. 1.Department of Biomedical EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of PhysicsCalifornia Institute of TechnologyPasadenaUSA

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