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Burst Detection Methods

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In Vitro Neuronal Networks

Part of the book series: Advances in Neurobiology ((NEUROBIOL,volume 22))

Abstract

‘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.

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References

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • Bakkum, D. J., Radivojevic, M., Frey, U., Franke, F., Hierlemann, A., & Takahashi, H. (2013). Parameters for burst detection. Frontiers in Computational Neuroscience, 7, 193.

    PubMed  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Ben-Ari, Y. (2001). Developing networks play a similar melody. Trends in Neurosciences, 24, 353–360.

    Article  CAS  PubMed  Google Scholar 

  • Borst, J. G. G. (2010). The low synaptic release probability in vivo. Trends in Neurosciences, 33, 259–266.

    Article  CAS  PubMed  Google Scholar 

  • 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 

  • Branco, T., & Staras, K. (2009). The probability of neurotransmitter release: Variability and feedback control at single synapses. Nature Reviews Neuroscience, 10, 373–383.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Câteau, H., & Reyes, A. D. (2006). Relation between single neuron and population spiking statistics and effects on network activity. Physical Review Letters, 96, 058101.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chiu, C., & Weliky, M. (2001). Spontaneous activity in developing ferret visual cortex in vivo. Journal of Neuroscience, 21, 8906–8914.

    Article  CAS  PubMed  Google Scholar 

  • Cocatre-Zilgien, J. H., & Delcomyn, F. (1992). Identification of bursts in spike trains. Journal of Neuroscience Methods, 41(1), 19–30.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Gabbiani, F., Metzner, W., Wessel, R., & Koch, C. (1996). From stimulus encoding to feature extraction in weakly electric fish. Nature, 384, 563–567.

    Article  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Gourévitch, B., & Eggermont, J. J. (2007). A nonparametric approach for detection of bursts in spike trains. Journal of Neuroscience Methods, 160(2), 349–358.

    Article  PubMed  Google Scholar 

  • Harris, R. E., Coulombe, M. G., & Feller, M. B. (2002). Dissociated retinal neurons form periodically active synaptic circuits. Journal of Neurophysiology, 88, 188–195.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Kaneoke, Y., & Vitek, J. L. (1996). Burst and oscillation as disparate neuronal properties. Journal of Neuroscience Methods, 68(2), 211–223.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Krahe, R., & Gabbiani, F. (2004). Burst firing in sensory systems. Nature Reviews Neuroscience, 5, 13–23.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Lisman, J. E. (1997). Bursts as a unit of neural information: Making unreliable synapses reliable. Trends Neuroscience, 20(1), 38–43.

    Article  CAS  Google Scholar 

  • Lobb, C. J. (2014). Abnormal bursting as a pathophysiological mechanism in Parkinson’s disease. Basal Ganglia, 3, 187–195.

    Article  PubMed  Google Scholar 

  • 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 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nex Technologies. (2014). NeuroExplorer Manual. Nex Technologies.

    Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • O’Keefe, J., & Recce, M. L. (1993). Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus, 3, 317–330.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Paulsen, O., & Sejnowski, T. J. (2000). Natural patterns of activity and long-term synaptic plasticity. Current Opinion in Neurobiology, 10, 172–179.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Rhoades, B. K., & Gross, G. W. (1994). Potassium and calcium channel dependence of bursting in cultured neuronal networks. Brain Research, 643, 310–318.

    Article  CAS  PubMed  Google Scholar 

  • Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80, 1–27.

    Article  CAS  PubMed  Google Scholar 

  • Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275, 1593–1599.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Sherman, S. M. (2001). Tonic and burst firing: Dual modes of thalamocortical relay. Trends in Neurosciences, 24, 122–126.

    Article  CAS  PubMed  Google Scholar 

  • 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 

  • Steriade, M., Timofeev, I., & Grenier, F. (2001). Natural waking and sleep states: A view from inside neocortical neurons. Journal of Neurophysiology, 85, 1969–1985.

    Article  CAS  PubMed  Google Scholar 

  • Tam, D. (2002). An alternate burst analysis for detecting intra-burst firings based on inter-burst periods. Neurocomputing, 46, 1155–1159.

    Article  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Weliky, M., & Katz, L. C. (1999). Correlational structure of spontaneous neuronal activity in the developing lateral geniculate nucleus in vivo. Science, 285, 599–604.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Zhang, X., & Shasha, D. (2006). Better burst detection. In Proceedings of the 22nd International Conference on Data Engineering (p. 146).

    Google Scholar 

  • 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 

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Acknowledgements

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

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  1. Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK

    Ellese Cotterill & Stephen J. Eglen

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  1. Ellese Cotterill
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Correspondence to Stephen J. Eglen .

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Editors and Affiliations

  1. Rehab Technologies IIT-INAIL Joint Lab, Istituto Italiano di Tecnologia, Genova, Italy

    Michela Chiappalone

  2. Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy

    Valentina Pasquale

  3. Department of Clinical Neurophysiology, University of Twente, Enschede, The Netherlands

    Monica Frega

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Cotterill, E., Eglen, S.J. (2019). Burst Detection Methods. In: Chiappalone, M., Pasquale, V., Frega, M. (eds) In Vitro Neuronal Networks. Advances in Neurobiology, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-11135-9_8

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