Burst Detection Methods

  • Ellese Cotterill
  • Stephen J. EglenEmail author
Part of the Advances in Neurobiology book series (NEUROBIOL, volume 22)


‘Bursting’, defined as periods of high-frequency firing of a neuron separated by periods of quiescence, has been observed in various neuronal systems, both in vitro and in vivo. It has been associated with a range of neuronal processes, including efficient information transfer and the formation of functional networks during development, and has been shown to be sensitive to genetic and pharmacological manipulations. Accurate detection of periods of bursting activity is thus an important aspect of characterising both spontaneous and evoked neuronal network activity. A wide variety of computational methods have been developed to detect periods of bursting in spike trains recorded from neuronal networks. In this chapter, we review several of the most popular and successful of these methods.


Burst detection Spike train analysis Multielectrode arrays 



EC was supported by a Wellcome Trust PhD Studentship and a National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre Studentship.


  1. Abeles, M., Bergman, H., Gat, I., Meilijson, I., Seidemann, E., Tishby, N., et al. (1995). Cortical activity flips among quasi-stationary states. Proceedings of the National Academy of Sciences of the United States of America, 92, 8616–8620.Google Scholar
  2. Allen, C., & Stevens, C. F. (1994). An evaluation of causes for unreliability of synaptic transmission. Proceedings of the National Academy of Sciences of the United States of America, 91, 10380–10383.Google Scholar
  3. Amin, H., Maccione, A., Marinaro, F., Zordan, S., Nieus, T., & Berdondini, L. (2016). Electrical responses and spontaneous activity of human iPS-derived neuronal networks characterized for 3-month culture with 4096-electrode arrays. Frontiers in Neuroscience, 10, 1–15.CrossRefGoogle Scholar
  4. Bakkum, D. J., Radivojevic, M., Frey, U., Franke, F., Hierlemann, A., & Takahashi, H. (2013). Parameters for burst detection. Frontiers in Computational Neuroscience, 7, 193.PubMedPubMedCentralGoogle Scholar
  5. Bakkum, D. J., Shkolnik, A. C., Ben-Ary, G., Gamblen, P., DeMarse, B., & Potter, S. M. (2004). Removing some ‘A’ from AI: Embodied cultured networks. In F. Iida, R. Pfeifer, L. Steels, & Y. Kuniyoshi (Eds.), Embodied artificial intelligence (pp. 130–146). Berlin: Springer.CrossRefGoogle Scholar
  6. Barrionuevo, G., Benoit, O., & Tempier, P. (1981). Evidence for two types of firing pattern during the sleep-waking cycle in the reticular thalamic nucleus of the cat. Experimental Neurology, 72, 486–501.PubMedCrossRefPubMedCentralGoogle Scholar
  7. Ben-Ari, Y. (2001). Developing networks play a similar melody. Trends in Neurosciences, 24, 353–360.PubMedCrossRefGoogle Scholar
  8. Borst, J. G. G. (2010). The low synaptic release probability in vivo. Trends in Neurosciences, 33, 259–266.PubMedCrossRefPubMedCentralGoogle Scholar
  9. Boyack, K. W., Mane, K., & Börner, K. (2004). Mapping Medline papers, genes and proteins related to melanoma research. In Proceedings Eighth IEEE International Conference on Computer Vision (pp. 965–971).Google Scholar
  10. Branco, T., & Staras, K. (2009). The probability of neurotransmitter release: Variability and feedback control at single synapses. Nature Reviews Neuroscience, 10, 373–383.PubMedCrossRefPubMedCentralGoogle Scholar
  11. Burgos-Robles, A., Vidal-Gonzalez, I., Santini, E., & Quirk, G. J. (2007). Consolidation of fear extinction requires NMDA receptor-dependent bursting in the ventromedial prefrontal cortex. Neuron, 53, 871–880.PubMedCrossRefPubMedCentralGoogle Scholar
  12. Câteau, H., & Reyes, A. D. (2006). Relation between single neuron and population spiking statistics and effects on network activity. Physical Review Letters, 96, 058101.PubMedCrossRefPubMedCentralGoogle Scholar
  13. Cattaneo, A., Maffei, L., & Morrone, C. (1981). Two firing patterns in the discharge of complex cells encoding different attributes of the visual stimulus. Experimental Brain Research, 43, 115–118.PubMedCrossRefPubMedCentralGoogle Scholar
  14. Charlesworth, P., Cotterill, E., Morton, A., Grant, S. G., & Eglen, S. J. (2015). Quantitative differences in developmental profiles of spontaneous activity in cortical and hippocampal cultures. Neural Development, 10, 1–10.PubMedPubMedCentralCrossRefGoogle Scholar
  15. Charlesworth, P., Morton, A., Eglen, S. J., Komiyama, N. H., & Grant, S. G. N. (2016). Canalization of genetic and pharmacological perturbations in developing primary neuronal activity patterns. Neuropharmacology, 100, 47–55.PubMedPubMedCentralCrossRefGoogle Scholar
  16. Chen, L., Deng, Y., Luo, W., Wang, Z., & Zeng, S. (2009). Detection of bursts in neuronal spike trains by the mean inter-spike interval method. Progress in Natural Science, 19(2), 229–235.CrossRefGoogle Scholar
  17. Chen, Z., & Brown, E. N. (2009). Discrete- and continuous-time probabilistic models and algorithms for inferring neuronal UP and DOWN states. Neural Computation, 21(7), 1797–1862.PubMedPubMedCentralCrossRefGoogle Scholar
  18. Chiappalone, M., Bove, M., Vato, A., Tedesco, M., & Martinoia, S. (2006). Dissociated cortical networks show spontaneously correlated activity patterns during in vitro development. Brain Research, 1093, 41–53.PubMedCrossRefGoogle Scholar
  19. Chiappalone, M., Novellino, A., Vajda, I., Vato, A., Martinoia, S., & van Pelt, J. (2005). Burst detection algorithms for the analysis of spatio-temporal patterns in cortical networks of neurons. Neurocomputing, 65–66, 653–662.CrossRefGoogle Scholar
  20. Chiu, C., & Weliky, M. (2001). Spontaneous activity in developing ferret visual cortex in vivo. Journal of Neuroscience, 21, 8906–8914.PubMedCrossRefPubMedCentralGoogle Scholar
  21. Cocatre-Zilgien, J. H., & Delcomyn, F. (1992). Identification of bursts in spike trains. Journal of Neuroscience Methods, 41(1), 19–30.PubMedCrossRefPubMedCentralGoogle Scholar
  22. Cotterill, E., Charlesworth, P., Thomas, C. W., Paulsen, O., & Eglen, S. J. (2016). A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks. Journal of Neurophysiology, 116, 306–321.PubMedPubMedCentralCrossRefGoogle Scholar
  23. Eisenman, L. N., Emnett, C. M., Mohan, J., Zorumski, C. F., & Mennerick, S. (2015). Quantification of bursting and synchrony in cultured hippocampal neurons. Journal of Neurophysiology, 114, 1059–1071.PubMedPubMedCentralCrossRefGoogle Scholar
  24. Epsztein, J., Brecht, M., & Lee, A. K. (2011). Intracellular determinants of hippocampal CA1 place and silent cell activity in a novel environment. Neuron, 70, 109–120.PubMedPubMedCentralCrossRefGoogle Scholar
  25. Evarts, E. V. (1964). Temporal patterns of discharge of pyramidal tract neurons during sleep and waking in the monkey. Journal of Neurophysiology, 27, 152–171.PubMedCrossRefPubMedCentralGoogle Scholar
  26. Froemke, R. C., Tsay, I. A., Raad, M., Long, J. D., & Dan, Y. (2006). Contribution of individual spikes in burst-induced long-term synaptic modification. Journal of Neurophysiology, 95, 1620–1629.PubMedCrossRefPubMedCentralGoogle Scholar
  27. Gabbiani, F., Metzner, W., Wessel, R., & Koch, C. (1996). From stimulus encoding to feature extraction in weakly electric fish. Nature, 384, 563–567.CrossRefGoogle Scholar
  28. Gilchrist, K. H., Lewis, G. F., Gay, E. A., Sellgren, K. L., & Grego, S. (2015). High-throughput cardiac safety evaluation and multi-parameter arrhythmia profiling of cardiomyocytes using microelectrode arrays. Toxicology and Applied Pharmacology, 288, 249–257.PubMedPubMedCentralCrossRefGoogle Scholar
  29. Golbs, A., Nimmervoll, B., Sun, J.-J., Sava, I. E., & Luhmann, H. J. (2011). Control of programmed cell death by distinct electrical activity patterns. Cerebral Cortex, 21, 1192–1202.PubMedCrossRefPubMedCentralGoogle Scholar
  30. Gourévitch, B., & Eggermont, J. J. (2007). A nonparametric approach for detection of bursts in spike trains. Journal of Neuroscience Methods, 160(2), 349–358.PubMedCrossRefPubMedCentralGoogle Scholar
  31. Harris, R. E., Coulombe, M. G., & Feller, M. B. (2002). Dissociated retinal neurons form periodically active synaptic circuits. Journal of Neurophysiology, 88, 188–195.PubMedCrossRefPubMedCentralGoogle Scholar
  32. Heck, N., Golbs, A., Riedemann, T., Sun, J.-J., Lessmann, V., & Luhmann, H. J. (2008) Activity-dependent regulation of neuronal apoptosis in neonatal mouse cerebral cortex. Cerebral Cortex, 18, 1335–1349.PubMedCrossRefPubMedCentralGoogle Scholar
  33. Heikkilä, T. J., Ylä-Outinen, L., Tanskanen, J. M. A., Lappalainen, R. S., Skottman, H., Suuronen, R., et al. (2009). Human embryonic stem cell-derived neuronal cells form spontaneously active neuronal networks in vitro. Experimental Neurology, 218(1), 109–116.PubMedCrossRefGoogle Scholar
  34. Hennig, M. H., Grady, J., van Coppenhagen, J., & Sernagor, E. (2011). Age-dependent homeostatic plasticity of GABAergic signaling in developing retinal networks. Journal of Neuroscience, 31(34), 12159–12164.PubMedCrossRefPubMedCentralGoogle Scholar
  35. Ichikawa, M., Muramoto, K., Kobayashi, K., Kawahara, M., & Kuroda, Y. (1993). Formation and maturation of synapses in primary cultures of rat cerebral cortical cells: An electron microscopic study. Neuroscience Research, 16, 95–103.PubMedCrossRefPubMedCentralGoogle Scholar
  36. Illes, S., Fleischer, W., Siebler, M., Hartung, H.-P., & Dihné, M. (2007). Development and pharmacological modulation of embryonic stem cell-derived neuronal network activity. Experimental Neurology, 207, 171–176.PubMedCrossRefPubMedCentralGoogle Scholar
  37. Jackson, M. E., Homayoun, H., & Moghaddam, B. (2004). NMDA receptor hypofunction produces concomitant firing rate potentiation and burst activity reduction in the prefrontal cortex. Proceedings of the National Academy of Sciences of the United States of America, 101, 8467–8472.Google Scholar
  38. Kamioka, H., Maeda, E., Jimbo, Y., Robinson, H. P. C., & Kawana, A. (1996) Spontaneous periodic synchronized bursting during formation of mature patterns of connections in cortical cultures. Neuroscience Letters, 206, 109–112.PubMedCrossRefGoogle Scholar
  39. Kaneoke, Y., & Vitek, J. L. (1996). Burst and oscillation as disparate neuronal properties. Journal of Neuroscience Methods, 68(2), 211–223.PubMedCrossRefPubMedCentralGoogle Scholar
  40. Kapucu, F. E., Tanskanen, J. M. A., Mikkonen, J. E., Ylä-Outinen, L., Narkilahti, S., & Hyttinen, J. A. K. (2012). Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics. Frontiers in Computational Neuroscience, 6, 38.PubMedPubMedCentralCrossRefGoogle Scholar
  41. Kleinberg, J. (2002). Bursty and hierarchical structure in streams. In Proceedings of 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 91–101).Google Scholar
  42. Ko, D., Wilson, C. J., Lobb, C. J., & Paladini, C. A. (2012). Detection of bursts and pauses in spike trains. Journal of Neuroscience Methods, 211(1), 145–158.PubMedPubMedCentralCrossRefGoogle Scholar
  43. Krahe, R., & Gabbiani, F. (2004). Burst firing in sensory systems. Nature Reviews Neuroscience, 5, 13–23.PubMedCrossRefPubMedCentralGoogle Scholar
  44. Krahe, R., Kreiman, G., Gabbiani, F., Koch, C., & Metzner, W. (2002). Stimulus encoding and feature extraction by multiple sensory neurons. Journal of Neuroscience, 22, 2374–2382.PubMedCrossRefPubMedCentralGoogle Scholar
  45. Kumar, R., Road, H., Jose, S., Road, H., Jose, S., Drive, R., et al. (2003). On the bursty evolution of blogspace. In International World Wide Web Conference (pp. 568–576).Google Scholar
  46. Legéndy C. R., & Salcman, M. (1985). Bursts and recurrences of bursts in the spike trains of spontaneously active striate cortex neurons. Journal of Neurophysiology, 53(4), 926–939.PubMedCrossRefPubMedCentralGoogle Scholar
  47. Leinekugel, X., Khazipov, R., Cannon, R., Hirase, H., Ben-Ari, Y., & Buzsáki, G. (2002). Correlated bursts of activity in the neonatal hippocampus in vivo. Science, 296, 2049–2052.PubMedCrossRefPubMedCentralGoogle Scholar
  48. Lisman, J. E. (1997). Bursts as a unit of neural information: Making unreliable synapses reliable. Trends Neuroscience, 20(1), 38–43.CrossRefGoogle Scholar
  49. Lobb, C. J. (2014). Abnormal bursting as a pathophysiological mechanism in Parkinson’s disease. Basal Ganglia, 3, 187–195.PubMedCrossRefPubMedCentralGoogle Scholar
  50. Lonardoni, D., Di Marco, S., Amin, H., Maccione, A., Berdondini, L., & Nieus, T. (2015). High-density MEA recordings unveil the dynamics of bursting events in cell cultures. Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015, 3763–3766.Google Scholar
  51. Maccione, A., Hennig, M. H., Gandolfo, M., Muthmann, O., van Coppenhagen, J., Eglen, S. J., et al. (2014). Following the ontogeny of retinal waves: Pan-retinal recordings of population dynamics in the neonatal mouse. The Journal of Physiology, 592(7), 1545–1563.PubMedPubMedCentralCrossRefGoogle Scholar
  52. Maeda, E., Robinson, H. P., & Kawana, A. (1995). The mechanisms of generation and propagation of synchronized bursting in developing networks of cortical neurons. The Journal of Neuroscience, 15(10), 6834–6845.PubMedCrossRefPubMedCentralGoogle Scholar
  53. Martens, M. B., Chiappalone, M., Schubert, D., & Tiesinga, P. H. E. (2014). Separating burst from background spikes in multichannel neuronal recordings using return map analysis. International Journal of Neural Systems, 24(04), 1450012.PubMedCrossRefPubMedCentralGoogle Scholar
  54. Martinson, J., Webster, H. H., Myasnikov, A. A., & Dykes, R. W. (1997). Recognition of temporally structured activity in spontaneously discharging neurons in the somatosensory cortex in waking cats. Brain Research, 750, 129–140.PubMedCrossRefPubMedCentralGoogle Scholar
  55. Mazzoni, A., Broccard, F. D., Garcia-Perez, E., Bonifazi, P., Ruaro, M. E., & Torre, V. (2007) On the dynamics of the spontaneous activity in neuronal networks. PLoS One, 2, e439.PubMedPubMedCentralCrossRefGoogle Scholar
  56. McCarley, R. W., Benoit, O., & Barrionuevo, G. (1983). Lateral geniculate nucleus unitary discharge in sleep and waking: State- and rate-specific aspects. Journal of Neurophysiology, 50, 798–818.PubMedCrossRefPubMedCentralGoogle Scholar
  57. Meister, M., Wong, R. O. L., Baylor, D. A., & Shatz, C. J. (1991). Synchronous bursts of action potentials in ganglion cells of the developing mammalian retina. Science, 252, 939–943.CrossRefGoogle Scholar
  58. Miller, B. R., Walker, A. G., Barton, S. J., & Rebec, G. V. (2011). Dysregulated neuronal activity patterns implicate corticostriatal circuit dysfunction in multiple rodent models of Huntington’s disease. Frontiers in Systems Neuroscience, 5, 26.PubMedPubMedCentralCrossRefGoogle Scholar
  59. Nex Technologies. (2014). NeuroExplorer Manual. Nex Technologies.Google Scholar
  60. Ni, Z. G., Bouali-Benazzouz, R., Gao, D. M., Benabid, A. L., & Benazzouz, A. (2001). Time-course of changes in firing rates and firing patterns of subthalamic nucleus neuronal activity after 6-OHDA-induced dopamine depletion in rats. Brain Research, 899, 142–147.PubMedCrossRefPubMedCentralGoogle Scholar
  61. Nicolas, J., Hendriksen, P. J. M., van Kleef, R. G. D. M., de Groot, A., Bovee, T. F. H., Rietjens, I. M. C. M., et al. (2014). Detection of marine neurotoxins in food safety testing using a multielectrode array. Molecular Nutrition and Food Research, 58, 2369–2378.PubMedCrossRefPubMedCentralGoogle Scholar
  62. Odawara, A., Katoh, H., Matsuda, N., & Suzuki, I. (2016). Physiological maturation and drug responses of human induced pluripotent stem cell-derived cortical neuronal networks in long-term culture. Science Reports, 6, 1–14.CrossRefGoogle Scholar
  63. O’Keefe, J., & Recce, M. L. (1993). Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus, 3, 317–330.PubMedCrossRefGoogle Scholar
  64. Otto, T., Eichenbaum, H., Wible, C. G., & Wiener, S. I. (1991). Learning-related patterns of CA1 spike trains parallel stimulation parameters optimal for inducing hippocampal longterm potentiation. Hippocampus, 1, 181–192.PubMedCrossRefPubMedCentralGoogle Scholar
  65. Pasquale, V., Martinoia, S., & Chiappalone, M. (2010). A self-adapting approach for the detection of bursts and network bursts in neuronal cultures. Journal of Computational Neuroscience, 29(1–2), 213–229.PubMedCrossRefGoogle Scholar
  66. Pasquale, V., Massobrio, P., Bologna, L. L., Chiappalone, M., & Martinoia, S. (2008). Self-organization and neuronal avalanches in networks of dissociated cortical neurons. Neuroscience, 153, 1354–1369.PubMedPubMedCentralCrossRefGoogle Scholar
  67. Paulsen, O., & Sejnowski, T. J. (2000). Natural patterns of activity and long-term synaptic plasticity. Current Opinion in Neurobiology, 10, 172–179.PubMedPubMedCentralCrossRefGoogle Scholar
  68. Pike, F. G., Meredith, R. M., Olding, A. W. A., & Paulsen, O. (2004). Postsynaptic bursting is essential for ‘Hebbian’ induction of associative long-term potentiation at excitatory synapses in rat hippocampus. The Journal of Physiology, 518, 571–576.CrossRefGoogle Scholar
  69. Pimashkin, A., Kastalskiy, I., Simonov, A., Koryagina, E., Mukhina, I., & Kazantsev, V. (2011). Spiking signatures of spontaneous activity bursts in hippocampal cultures. Frontiers in Computational Neuroscience, 5, 1–12.CrossRefGoogle Scholar
  70. Pluta, S., Naka, A., Veit, J., Telian, G., Yao, L., Hakim, R., et al. (2015). A direct translaminar inhibitory circuit tunes cortical output. Nature Neuroscience, 18, 1631–1640.PubMedPubMedCentralCrossRefGoogle Scholar
  71. Radons, G., Becker, J. D., Dülfer, B., & Krüger, J. (1994). Analysis, classification, and coding of multielectrode spike trains with hidden Markov models. Biological Cybernetics, 71, 359–373.PubMedCrossRefPubMedCentralGoogle Scholar
  72. Raichman, N., & Ben-Jacob, E. (2008). Identifying repeating motifs in the activation of synchronized bursts in cultured neuronal networks. Journal of Neuroscience Methods, 170, 96–110.PubMedCrossRefPubMedCentralGoogle Scholar
  73. Rhoades, B. K., & Gross, G. W. (1994). Potassium and calcium channel dependence of bursting in cultured neuronal networks. Brain Research, 643, 310–318.PubMedCrossRefPubMedCentralGoogle Scholar
  74. Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80, 1–27.PubMedCrossRefPubMedCentralGoogle Scholar
  75. Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275, 1593–1599.PubMedPubMedCentralCrossRefGoogle Scholar
  76. Selinger, J. V., Kulagina, N. V., O’Shaughnessy, T. J., Ma, W., & Pancrazio, J. J. (2007). Methods for characterizing interspike intervals and identifying bursts in neuronal activity. Journal of Neuroscience Methods, 162(1–2), 64–71.PubMedCrossRefGoogle Scholar
  77. Senn, V., Wolff, S. B. E., Herry, C., Grenier, F., Ehrlich, I., Gründemann, J., et al. (2014). Long-range connectivity defines behavioral specificity of amygdala neurons. Neuron, 81, 428–437.PubMedCrossRefPubMedCentralGoogle Scholar
  78. Sherman, S. M. (2001). Tonic and burst firing: Dual modes of thalamocortical relay. Trends in Neurosciences, 24, 122–126.PubMedCrossRefPubMedCentralGoogle Scholar
  79. Singh, A., Mewes, K., Gross, R. E., DeLong, M. R., Obeso, J. A., & Papa, S. M. (2016). Human striatal recordings reveal abnormal discharge of projection neurons in Parkinson’s disease. Proceedings of the National Academy of Sciences of the United States of America, 113, 9629–9634.Google Scholar
  80. Steriade, M., Timofeev, I., & Grenier, F. (2001). Natural waking and sleep states: A view from inside neocortical neurons. Journal of Neurophysiology, 85, 1969–1985.PubMedCrossRefPubMedCentralGoogle Scholar
  81. Tam, D. (2002). An alternate burst analysis for detecting intra-burst firings based on inter-burst periods. Neurocomputing, 46, 1155–1159.CrossRefGoogle Scholar
  82. Thomas, M. J., Watabe, A. M., Moody, T. D., Makhinson, M., & O’Dell, T. J. (1998). Postsynaptic complex spike bursting enables the induction of LTP by theta frequency synaptic stimulation. The Journal of Neuroscience, 18, 7118–7126.PubMedCrossRefPubMedCentralGoogle Scholar
  83. Thomson, A. M. (1997). Activity-dependent properties of synaptic transmission at two classes of connections made by rat neocortical pyramidal axons in vitro. The Journal of Physiology, 502, 131–147.PubMedPubMedCentralCrossRefGoogle Scholar
  84. Tobler, P. N., Dickinson, A., & Schultz, W. (2003). Coding of predicted reward omission by dopamine neurons in a conditioned inhibition paradigm. The Journal of Neuroscience, 23, 10402–10410.PubMedCrossRefPubMedCentralGoogle Scholar
  85. Tokdar, S., Xi, P., Kelly, R. C., & Kass, R. E. (2010). Detection of bursts in extracellular spike trains using hidden semi-Markov point process models. Journal of Computational Neuroscience, 29(1–2), 203–212.PubMedCrossRefPubMedCentralGoogle Scholar
  86. Turnbull, L., Dian, E., & Gross, G. (2005) The string method of burst identification in neuronal spike trains. Journal of Neuroscience Methods, 145(1–2), 23–35.PubMedCrossRefPubMedCentralGoogle Scholar
  87. Valdivia, P., Martin, M., LeFew, W. R., Ross, J., Houck, K. A., & Shafer, T. J. (2014). Multi-well microelectrode array recordings detect neuroactivity of ToxCast compounds. Neurotoxicology, 44, 204–217.PubMedCrossRefGoogle Scholar
  88. Välkki, I. A., Lenk, K., Mikkonen, J. E., & Kapucu, F. E. (2017). Network-wide adaptive burst detection depicts neuronal activity with improved accuracy. Frontiers in Computational Neuroscience, 11, 40.PubMedPubMedCentralCrossRefGoogle Scholar
  89. Van Den Pol, A. N., Obrietan, K., & Belousov, A. (1996). Glutamate hyperexcitability and seizure-like activity throughout the brain and spinal cord upon relief from chronic glutamate receptor blockage in culture. Neuroscience, 74, 653–674.CrossRefGoogle Scholar
  90. Van Huizen, F., Romijn, H. J., & Habets, A. M. M. C. (1985). Synaptogenesis in rat cerebral cortex cultures is affected during chronic blockade of spontaneous bioelectric activity by tetrodotoxin. Developmental Brain Research, 19, 67–80.CrossRefGoogle Scholar
  91. Van Pelt, J., Corner, M. A., Wolters, P. S., Rutten, W. L. C., & Ramakers, G. J. A. (2004). Longterm stability and developmental changes in spontaneous network burst firing patterns in dissociated rat cerebral cortex cell cultures on multielectrode arrays. Neuroscience Letters, 361, 86–89.PubMedCrossRefPubMedCentralGoogle Scholar
  92. Van Pelt, J., Vajda, I., Wolters, P. S., Corner, M. A., & Ramakers, G. J. A. (2005). Dynamics and plasticity in developing neuronal networks in vitro. Progress in Brain Research, 147, 173–188.PubMedPubMedCentralGoogle Scholar
  93. Van Pelt, J., Wolters, P. S., Corner, M. A., Rutten, W. L. C., & Ramakers, G. J. A. (2004). Long-term characterization of firing dynamics of spontaneous bursts in cultured neural networks. IEEE Transactions on Biomedical Engineering, 51, 2051–2062.CrossRefGoogle Scholar
  94. Wagenaar, D., Demarse, T. B., & Potter, S. M. (2005). MeaBench: A toolset for multi-electrode data acquisition and on-line analysis. In Proceedings of 2nd International IEEE EMBS Conference on Neural Engineering (pp. 518–521)Google Scholar
  95. Wagenaar, D. A., Pine, J., & Potter, S. M. (2006). An extremely rich repertoire of bursting patterns during the development of cortical cultures. BMC Neuroscience, 7, 11.PubMedPubMedCentralCrossRefGoogle Scholar
  96. Walker, A. G., Miller, B. R., Fritsch, J. N., Barton, S. J., & Rebec, G. V. (2008). Altered information processing in the prefrontal cortex of Huntington’s disease mouse models. The Journal of Neuroscience, 28, 8973–8982.PubMedPubMedCentralCrossRefGoogle Scholar
  97. Weliky, M., & Katz, L. C. (1999). Correlational structure of spontaneous neuronal activity in the developing lateral geniculate nucleus in vivo. Science, 285, 599–604.CrossRefGoogle Scholar
  98. Weyand, T. G., Boudreaux, M., & Guido, W. (2001). Burst and tonic response modes in thalamic neurons during sleep and wakefulness. Journal of Neurophysiology, 85(3), 1107–1118.PubMedCrossRefPubMedCentralGoogle Scholar
  99. Xu, W., Morishita, W., Buckmaster, P. S., Pang, Z. P., Malenka, R. C., & Südhof, T. C. (2012). Distinct neuronal coding schemes in memory revealed by selective erasure of fast synchronous synaptic transmission. Neuron, 73, 990–1001.PubMedPubMedCentralCrossRefGoogle Scholar
  100. Ylä-Outinen, L., Heikkilä, J., Skottman, H., Suuronen, R., Aänismaa, R., & Narkilahti, S. (2010). Human cell-based micro electrode array platform for studying neurotoxicity. Frontiers in Neuroengineering, 3, 1–9.CrossRefGoogle Scholar
  101. Zhang, X., & Shasha, D. (2006). Better burst detection. In Proceedings of the 22nd International Conference on Data Engineering (p. 146).Google Scholar
  102. Zhu, Y., & Shasha, D. (2003). Efficient elastic burst detection in data streams. In Proceedings of Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 336–345).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Applied Mathematics and Theoretical PhysicsUniversity of CambridgeCambridgeUK

Personalised recommendations