Abstract
It has been proposed that cortical neurons organize dynamically into functional groups (“cell assemblies”) by the temporal structure of their joint spiking activity. The Unitary Events analysis method detects conspicuous patterns of coincident spike activity among simultaneously recorded single neurons. The statistical significance of a pattern is evaluated by comparing the number of occurrences to the number expected on the basis of the firing rates of the neurons. Key elements of the method are the proper formulation of the null hypothesis and the derivation of the corresponding count distribution of coincidences used in the significance test. Performing the analysis in a sliding window manner results in a time-resolved measure of significant spike synchrony. In this chapter we review the basic components of UE analysis and explore its dependencies on parameters like the allowed temporal imprecision and features of the data like firing rate and coincidence rate. Violations of the assumptions of stationarity of the firing rate within the analysis window and Poisson statistics can be tolerated to a reasonable degree without inducing false positives. We conclude that the UE method is robust already in its basic form. Still, it is preferable to use coincidence distributions for the significance test that are well adapted to particular features of the data. The chapter presents practical advice and solutions based on surrogates.
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 (1982) Role of cortical neuron: integrator or coincidence detector?. Israel J Med Sci 18:83–92
Abeles M (1991) Corticonics: neural circuits of the cerebral cortex. Cambridge University Press, Cambridge
Abeles M, Gat I (2001) Detecting precise firing sequences in experimental data. J Neurosci Methods 107(1–2), 141–154
Aertsen A, Gerstein G, Habib M, Palm G (1989) Dynamics of neuronal firing correlation: modulation of “effective connectivity”. J Neurophysiol 61(5), 900–917
Baker S, Lemon R (2000) Precise spatiotemporal repeating patterns in monkey primary and supplementary motor areas occur at chance levels. J Neurophysiol 84(4):1770–1780
Barlow HB (1972) Single units and sensation: a neuron doctrine for perceptual psychology?. Perception 1:371–394
Ben-Shaul Y, Bergman H, Ritov Y, Abeles M (2001) Trial to trial variability in either stimulus or action causes apparent correlation and synchrony in neuronal activity. J Neurosci Methods 111(2):99–110
Bernander Ö, Koch C, Usher M (1994) The effect of synchronized inputs at the single neuron level. Neural Comput 6:622–641
Brody CD (1999a) Correlations without synchrony. Neural Comput 11:1537–1551
Brody CD (1999b) Disambiguating different covariation types. Neural Comput 11:1527–1535
Brown E, Barbieri R, Ventura V, Kass R, Frank L (2002) The time-rescaling theorem and its application to neural spike train data analysis. Neural Comput 14:325–346
Czanner G, Grün S, Iyengar S (2005) Theory of the snowflake plot and its relations to higher-order analysis methods. Neural Comput 17(7):1456–1479
Denker M, Riehle A, Diesmann M, Grün S (in press) Estimating the contribution of assembly activity to cortical dynamics from spike and population measures. J Comput Neurosci. doi:10.1007/s10827-010-0241-8
Diesmann M, Gewaltig MO, Aertsen A (1999) Stable propagation of synchronous spiking in cortical neural networks. Nature 402(6761):529–533
Gerstein G, Aertsen A (1985) Representation of cooperative firing activity among simultaneously recorded neurons. J Neurophysiol 54:1513–1528
Gerstein G, Perkel D, Dayhoff J (1985) Cooperative firing activity in simultaneously recorded populations of neurons: detection and measurement. J Neurosci 5:881–889
Gerstein G, Bedenbaugh P, Aertsen A (1989) Neuronal assemblies. IEEE Trans Biomed Eng 36(1):4–14
Goedeke S, Diesmann M (2008) The mechanism of synchronization in feed-forward neuronal networks. New J Phys 10:015007. doi:10.1088/1367-2630/10/1/015007
Grammont F, Riehle A (1999) Precise spike synchronization in monkey motor cortex involved in preparation for movement. Experimental Brain Res 128:118–122
Grammont F, Riehle A (2003) Spike synchronization and firing rate in a population of motor cortical neurons in relation to movement direction and reaction time. Biol Cybernet 88(5):360–373
Grün S (2009) Data-driven significance estimation of precise spike correlation. J Neurophysiol 101(3):1126–1140 (invited review)
Grün S, Diesmann M, Grammont F, Riehle A, Aertsen A (1999) Detecting unitary events without discretization of time. J Neurosci Methods 94(1):67–79
Grün S, Diesmann M, Aertsen A (2002a) ‘Unitary Events’ in multiple single-neuron spiking activity. I. Detection and significance. Neural Comput 14(1):43–80
Grün S, Diesmann M, Aertsen A (2002b) ‘Unitary Events’ in multiple single-neuron spiking activity. II. Non-stationary data. Neural Comput 14(1):81–119
Grün S, Riehle A, Diesmann M (2003) Effect of cross-trial nonstationarity on joint-spike events. Biol Cybernet 88(5):335–351
Gütig R, Aertsen A, Rotter S (2002) Significance of coincident spikes: count-based versus rate-based statistics. Neural Comput 14:121–153
Harris K (2005) Neural signatures of cell assembly organization. Nature Neurosci Rev 5(6):339–407
Harrison M, Geman S (2009) A rate and history-preserving resampling algorithm for neural spike trains. Neural Comput 21(5):1244–1258
Hebb DO (1949) The organization of behavior: a neuropsychological theory. Wiley, New York
Ito H (2007) Bootstrap significance test of synchronous spike events – a case study of oscillatory spike trains. Statistical Med 26:3976–3996
Ito J, Maldonado P, Singer W, Grün S (submitted) Saccade-related LFP modulations support synchrony of visually elicited spikes
Johnson DH (1996) Point process models of single-neuron discharges. J Comput Neurosci 3(4):275–299
Kass R, Ventura V (2006) Spike count correlation increases with length of time interval in the presence of trial-to-trial variation. Neural Comput 18(11):2583–2591
Kilavik B, Roux S, Ponce-Alvarez A, Confais J, Grün S, Riehle A (2009) Long-term modifications in motor cortical dynamics induced by intensive practice. J Neurosci 29(40):12653–12663
Kumar A, Rotter S, Aertsen A (2008) Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model. J Neurosci 28:5268–5280
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
Marsalek P, Koch C, Maunsel J (1997) On the relationship between synaptic input and spike output jitter in individual neurons. Proc Natl Acad Sci 94:735–740
Nawrot M, Aertsen A, Rotter S (1999) Single-trial estimation of neuronal firing rates: from single-neuron spike trains to population activity. J Neurosci Methods 94:81–92
Nawrot M, Aertsen A, Rotter S (2003) Elimination of response latency variability in neuronal spike trains. Biol Cybernet 88:321–334
Nawrot M, Boucsein C, Rodriguez-Molina V, Aertsen A, Grün S, 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 (2008a) Measurement of variability dynamics in cortical spike trains. J Neurosci Methods 169:374–390
Nawrot M, Farkhooi F, Grün S (2008b) Significance of coincident spiking considering inter-spike interval variability and serial interval correlation. In: Frontiers in computational neuroscience. Conference abstract: Bernstein symposium 2008. doi:10.3389/conf.neuro.10.2008.01.017
Palm G (1981) Evidence, information and surprise. Biol Cybernet 42:57–68
Pauluis Q, Baker SN (2000) An accurate measure of the instantaneous discharge probability with application to unitary joint-event analysis. Neural Comput 12(3):647–669
Pazienti A, Maldonado P, Diesmann M, Grün S (2008) Effectiveness of systematic spike dithering depends on the precision of cortical synchronization. Brain Res 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
Perkel DH, Gerstein GL, Smith MS, Tatton WG (1975) Nerve-impulse patterns: a quantitative display technique for three neurons. Brain Res 100:271–296
Pipa G, Grün S, van Vreeswijk C (under revision) Impact of spike-train autostructure on probability distribution of joint-spike events. Neural Comput
Pipa G, Riehle A, Grün S (2007) Validation of task-related excess of spike coincidences based on NeuroXidence. Neurocomputing 70:2064–2068. Published online 2006: doi:10.1016/j.neucom.2006.10.142
Pipa G, van Vreeswijk C, Grün S (in preparation) Impact of spike-train autostructure on Unitary Events
Pipa G, Wheeler D, Singer W, Nikolic D (2008) NeuroXidence: reliable and efficient analysis of an excess or deficiency of joint-spike events. J Comput Neurosci 25(1):64–88
Prut Y, Vaadia E, Bergman H, Haalman I, Hamutal S, Abeles M (1998) Spatiotemporal structure of cortical activity: properties and behavioral relevance. J Neurophysiol 79(6):2857–2874
Reyes A (2003) Synchrony-dependent propagation of firing rate in iteratively constructed networks in vitro. Nature Neurosci 6:593–599
Richmond B (2009) Stochasticity spikes and decoding: sufficiency and utility of order statistics. Biol Cybernet 100(9):447–457
Riehle A, Grün S, Diesmann M, Aertsen A (1997) Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278(5345):1950–1953
Riehle A, Grammont F, Diesmann M, Grün S (2000) Dynamical changes and temporal precision of synchronized spiking activity in monkey motor cortex during movement preparation. J Physiol (Paris) 94:569–582
Rodriguez-Molina V, Aertsen A, Heck D (2007) Spike timing and reliability in cortical pyramidal neurons: Effects of EPSC kinetics input synchronization and background noise on spike timing. PLoS ONE 2(3):e319. doi:10.1371/journal.pone.0000319
Roy A, Steinmetz PN, Niebur E (2000) Rate limitations of unitary event analysis. Neural Comput 12:2063–2082
Shadlen MN, Movshon AJ (1999) Synchrony unbound: a critical evaluation of the temporal binding hypothesis. Neuron 24:67–77
Shimazaki H, Amari S, Brown E, Grün S (2009) State-space analysis on time-varying correlations in parallel spike sequences. In: IEEE international conference on acoustics, speech, and signal processing (ICASSP), pp 3501–3504
Shimazaki H, Shinomoto S (2010) Kernel bandwidth optimization in spike rate estimation. J Comput Neurosci. doi:10.1007/s10827-009-0180-4
Singer W (1999) Neuronal synchrony: a versatile code for the definition of relations?. Neuron 24(1):49–65
Softky WR, Koch C (1993) The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J Neurosci 13(1):334–350
Vaadia E, Haalman I, Abeles M, Bergman H, Prut Y, Slovin H, Aertsen A (1995) Dynamics of neuronal interactions in monkey cortex in relation to behavioral events. Nature 373:515–518
Ventura V, Carta R, Kass R, Gettner S, Olson C (2002) Statistical analysis of temporal evolution in single-neuron firing rates. Biostatistics 3(1):1–20
Ventura V, Cai C, Kass R (2005) Trial-to-trial variability and its effect on time-varying dependency between two neurons. J Neurophysiol 94(4):2928–2939
Von der Malsburg C (1981) The correlation theory of brain function. Internal report 81-2, Max-Planck-Institute for Biophysical Chemistry, Göttingen, FRG
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
Grün, S., Diesmann, M., Aertsen, A. (2010). Unitary Event 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_10
Download citation
DOI: https://doi.org/10.1007/978-1-4419-5675-0_10
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)