Abstract
Generating artificial data from experimental data as a means for implementing a null hypothesis is becoming widely used. The reason is twofold: increasing computer power now allows for this type of approach, and it has become clear that the complexity of experimental data does not in general allow one to formulate a null hypothesis analytically. This is particularly true for the correlation analysis of parallel spike trains. Neglecting statistical features of experimental data can easily lead to the occurrence of false positive results, which of course needs to be avoided. Therefore surrogate data are used to generate the predictor by modifying the original data in such a way that the feature of interest (temporal coordination of spikes) is destroyed but other features of the data are preserved. The latter aspect is the most demanding and requires the selection of a surrogate type that best fits the data at hand. This chapter will demonstrate the need for such a selection and will show selection criteria.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abeles M, Gat I (2001) Detecting precise firing sequences in experimental data. J Neurosci Meth 107(1–2):141–154
Berger D, Warren D, Normann R, Arieli A, Grün S (2007) Spatially organized spike correlation in cat visual cortex. Neurocomputing 70:2112–2116
Butts DA, Weng C, Jin J, Yeh C, Lesica NA, Alonso J, Stanley GB (2007) Temporal precision in the neural code and the timescales of natural vision. Nature 449:92–95
Date A, Bienenstock E, Geman S (1998) On the temporal resolution of neural activity. Technical report. Division of Applied Mathematics, Brown University
Denker M, Roux S, Timme M, Riehle A, Grün S (2007) Phase synchronization between LFP and spiking activity in motor cortex during movement preparation. Neurocomputing 70:2096–2101
Gerstein GL (2004) Searching for significance in spatio-temporal firing patterns. Acta Neurobiol Exp (Wars) 64(2):203–207
Gerstein GL, Perkel DH (1972) Mutual temporal relationships among neuronal spike trains. Statistical techniques for display and analysis. Biophysical J 12(5):453–473
Grün S (2009) Data-driven significance estimation of precise spike correlation. J Neurophysiol 101:1126–1140 (Review)
Grün S, Diesmann M, Grammont F, Riehle A, Aertsen A (1999) Detecting unitary events without discretization of time. J Neurosci Meth 94(1):67–79
Grün S, Diesmann M, Aertsen A (2002) Unitary events in multiple single-neuron spiking activity. I. Detection and significance. Neural Comput 14(1):43–80
Grün S, Riehle A, Diesmann M (2003) Effect of cross-trial nonstationarity on joint-spike events. Biol Cybern 88(5):335–351
Harrison MT, Geman S (2009) A rate and history-preserving resampling algorithm for neural spike trains. Neural Comput 21(5):1244–1258
Harrison MT, Amarasingham A, Geman S (2007) Jitter methods for investigating spike train dependencies. Computat Systems Neurosci Abstracts 3(17)
Hatsopoulus N, Geman S, Amarasingham A, Bienenstock E (2003) At what time scale does the nervous system operate?. Neurocomputing 52:25–29
Ikegaya Y, Aaron G, Cossart R, Aronov D, Lampl I, Ferster D, Yuste R (2004) Synfire chains and cortical songs: temporal modules of cortical activity. Science 304(5670):559–564
Ito H (2007) Bootstrap significance test of synchronous spike events a case study of oscillatory spike trains. Stat Med 26:3976–3996
Jones LM, Depireux DI, Simons DJ, Keller A (2004) Robust temporal coding in the trigeminal system. Science 304(5679):1986–1989
Louis S, Gerstein GL, Grün S, Diesmann M (in press) Surrogate spike train generation through dithering in operational time. Front Comput Neurosci
Maldonado P, Babul C, Singer W, Rodriguez E, Berger D, Grün S (2008) Synchronization of neuronal responses in primary visual cortex of monkeys viewing natural images. J Neurophysiol 100:1523–1532
Masuda N, Aihara K (2003) Duality of rate coding and temporal coding in multilayered feedforward networks. Neural Comput 15:103–125
Nádasdy Z, Hirase H, Czurkó A, Csicsvari J, Buzsáki G (1999) Replay and time compression of recurring spike sequences in the hippocampus. J Neurosci 19(21):9497–9507
Nawrot M, Aertsen A, Rotter S (1999) Single-trial estimation of neuronal firing rates: from single-neuron spike trains to population activity. J Neurosci Meth 94:81–92
Nawrot M, Boucsein C, Rodriguez-Molina V, Riehle A, Aertsen A, Rotter S (2007) Serial interval statistics of spontaneous activity in cortical neurons in vivo and in vitro. Neurocomputing 70:1717–1722
Nawrot M, Boucsein C, Rodriguez-Molina V, Riehle A, Aertsen A, Rotter S (2008) Measurement of variability dynamics in cortical spike trains. J Neurosci Meth 169:374–390
Pazienti A, Grün S (2007) Bounds of the ability to destroy precise coincidences by spike dithering. Advances in brain, vision, and artificial intelligence. Lecture notes in comput sci, vol 4729. Springer, Berlin, pp 428–437
Pazienti A, Maldonado P, Diesmann M, Grün S (2008) Effectiveness of systematic spike dithering depends on the precision of cortical synchronization. Brain Research 1225:39–46
Perkel DH, Gerstein GL, Moore GP (1967) Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. Biophys J 7(4):419–440
Pipa G, Grün S, van Vreeswijk C (2010) Impact of spike-train autostructure on probability distribution of joint-spike events. Neural Comput (under revision)
Pipa G, Wheeler DW, Singer W, Nikolić D (2008) NeuroXidence: reliable and efficient analysis of an excess or deficiency of joint-spike events. J Comput Neurosci 25(1):64–88
Shinomoto S, Kim H, Shimokawa T, Matsuno N, Funahashi S, Shima K, Fujita I, Tamura H, Doi T, Kawano K, Inaba N, Fukushima K, Kurkin S, Kurata K, Taira M, Tsutsui K, Komatsu H, Ogawa T, Koida K, Tanji J, Toyama K (2009) Relating neuronal firing patterns to functional differentiation of cerebral cortex. PLoS Comput Biol 7(5):12591–12603
Shmiel T, Drori R, Shmiel O, Ben-Shaul Y, Nádasdy Z, Shemesh M, Teicher M, Abeles M (2006) Temporally precise cortical firing patterns are associated with distinct action segments. J Neurophysiol 96(5):2645–2652
Smith MA, Kohn A (2008) Spatial and temporal scales of neuronal correlation in primary visual cortex. J Neurosci 28(48):12591–12603
van Rijsbergen CJ (1979) Information retrieval. Butterworth, London
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Louis, S., Borgelt, C., Grün, S. (2010). Generation and Selection of Surrogate Methods for Correlation Analysis. In: Grün, S., Rotter, S. (eds) Analysis of Parallel Spike Trains. Springer Series in Computational Neuroscience, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5675-0_17
Download citation
DOI: https://doi.org/10.1007/978-1-4419-5675-0_17
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-5674-3
Online ISBN: 978-1-4419-5675-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)