Abstract
We introduce a framework of spike shuffling methods to test the significance and understand the biological meanings of the second-order statistics of spike patterns recorded in experiments or simulations. In this framework, each method is to evidently alter a specific pattern statistics, leaving the other statistics unchanged. We then use this method to understand the contribution of different second-order statistics to the variance of synaptic changes induced by the spike patterns self-organized by an integrate-and-fire (LIF) neuronal network under STDP and synaptic homeostasis. We find that burstiness/regularity and heterogeneity of cross-correlations are important to determine the variance of synaptic changes under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause the variance of synaptic changes when the network moves into strong synchronous states.
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
Similar content being viewed by others
References
Averbeck, B.B., Latham, P.E., Pouget, A.: Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7, 358–366 (2006)
Brette, R.: Computing with neural synchrony. PLoS Comput. Biol. 8, e1002561 (2012)
Jutras, M.J., Fries, P., Buffalo, E.A.: Gamma-band synchronization in the macaque hippocampus and memory formation. J. Neurosci. 29, 12521–12531 (2009)
Yamamoto, J., Suh, J., Takeuchi, D., Tonegawa, S.: Successful execution of working memory linked to synchronized high-frequency gamma oscillations. Cell 157, 845–857 (2014)
Clause, A., Kim, G., Sonntag, M., Weisz, C.J., Vetter, D.E., Rubsamen, R., et al.: The precise temporal pattern of prehearing spontaneous activity is necessary for tonotopic map refinement. Neuron 82, 822–835 (2014)
Xu, H., Furman, M., Mineur, Y.S., Chen, H., King, S.L., Zenisek, D., et al.: An instructive role for patterned spontaneous retinal activity in mouse visual map development. Neuron 70, 1115–1127 (2011)
Ji, D., Wilson, M.A.: Coordinated memory replay in the visual cortex and hippocampus during sleep. Nat. Neurosci. 10, 100–107 (2007)
Nirenberg, S., Latham, P.E.: Decoding neuronal spike trains: how important are correlations? Proc. Natl. Acad. Sci. USA 100, 7348–7353 (2003)
Prentice, J.S., Marre, O., Ioffe, M.L., Loback, A.R., Tkačik, G., Berry II, M.J.: Error-robust modes of the retinal population code. PLoS Comput. Biol. 12, e1005148 (2016)
Calabrese, A., Schumacher, J.W., Schneider, D.M., Paninski, L., Woolley, S.M.N.: A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds. PLoS One 6, e16104 (2011)
Macke, J.H., Berens, P., Ecker, A.S., Tolias, A.S., Bethge, M.: Generating spike trains with specified correlation coefficients. Neural Comput. 21, 397–423 (2009)
Krumin, M., Shoham, S.: Generation of spike trains with controlled auto- and cross-correlation functions. Neural Comput. 21, 1642–1664 (2009)
Bi, Z., Zhou, C.: Spike pattern structure influences synaptic efficacy variability under STDP and synaptic homeostasis. II: spike shuffling methods on LIF networks. Front. Comput. Neurosci. 10, 83 (2016)
Bi, Z., Zhou, C.: Spike pattern structure influences synaptic efficacy variability under STDP and synaptic homeostasis. I: spike generating models on converging motifs. Front. Comput. Neurosci. 10, 14 (2016)
Acknowledgments
CZ is partially supported by Hong Kong Baptist University (HKBU) Strategic Development Fund, NSFC-RGC Joint Research Scheme (Grant No. HKUST/NSFC/12-13/01) and NSFC (Grant No. 11275027). ZB is supported by HKBU Faculty of Science.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Bi, Z., Zhou, C. (2017). Testing and Understanding Second-Order Statistics of Spike Patterns Using Spike Shuffling Methods. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_64
Download citation
DOI: https://doi.org/10.1007/978-3-319-70093-9_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70092-2
Online ISBN: 978-3-319-70093-9
eBook Packages: Computer ScienceComputer Science (R0)