HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Martignon, L. Articles by Vaadia, E. Search for Related Content PubMed PubMed Citation Articles by Martignon, L. Articles by Vaadia, E.

Neural Coding: Higher-Order Temporal Patterns in the Neurostatistics of Cell Assemblies

Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany

Gustavo Deco

Siemens AG, Corporate Technology ZT, 81739 Munich, Germany

Kathryn Laskey

Department of Systems Engineering, George Mason University, Fairfax, VA 22030, U.S.A.

Mathew Diamond

Cognitive Neuroscience Sector, International School of Advanced Studies, 34014 Trieste, Italy

Winrich Freiwald

Center for Cognitive Science, University of Bremen, 28359 Bremen, Germany

Eilon Vaadia

Department of Physiology, Hebrew University of Jerusalem, Jerusalem 91010, Israel

Recent advances in the technology of multiunit recordings make it possible to test Hebb's hypothesis that neurons do not function in isolation but are organized in assemblies. This has created the need for statistical approaches to detecting the presence of spatiotemporal patterns of more than two neurons in neuron spike train data. We mention three possible measures for the presence of higher-order patterns of neural activation—coefficients of log-linear models, connected cumulants, and redundancies—and present arguments in favor of the coefficients of log-linear models. We present test statistics for detecting the presence of higher-order interactions in spike train data by parameterizing these interactions in terms of coefficients of log-linear models. We also present a Bayesian approach for inferring the existence or absence of interactions and estimating their strength. The two methods, the frequentist and the Bayesian one, are shown to be consistent in the sense that interactions that are detected by either method also tend to be detected by the other. A heuristic for the analysis of temporal patterns is also proposed. Finally, a Bayesian test is presented that establishes stochastic differences between recorded segments of data. The methods are applied to experimental data and synthetic data drawn from our statistical models. Our experimental data are drawn from multiunit recordings in the prefrontal cortex of behaving monkeys, the somatosensory cortex of anesthetized rats, and multiunit recordings in the visual cortex of behaving monkeys.

This article has been cited by other articles:

				A. Tang, D. Jackson, J. Hobbs, W. Chen, J. L. Smith, H. Patel, A. Prieto, D. Petrusca, M. I. Grivich, A. Sher, et al. A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical Networks In Vitro J. Neurosci., January 9, 2008; 28(2): 505 - 518. [Abstract] [Full Text] [PDF]

				V. Ventura Spike Train Decoding Without Spike Sorting Neural Comput., April 1, 2007; 20(4): 923 - 963. [Abstract] [Full Text] [PDF]

				J. Shlens, G. D. Field, J. L. Gauthier, M. I. Grivich, D. Petrusca, A. Sher, A. M. Litke, and E. J. Chichilnisky The structure of multi-neuron firing patterns in primate retina. J. Neurosci., August 9, 2006; 26(32): 8254 - 8266. [Abstract] [Full Text] [PDF]

				P. Tino and A. J. S. Mills Learning beyond finite memory in recurrent networks of spiking neurons. Neural Comput., March 1, 2006; 18(3): 591 - 613. [Abstract] [Full Text] [PDF]

				T. Wennekers and N. Ay Finite State Automata Resulting from Temporal Information Maximization and a Temporal Learning Rule Neural Comput., October 1, 2005; 17(10): 2258 - 2290. [Abstract] [Full Text] [PDF]

				A. K. Lee and M. A. Wilson A Combinatorial Method for Analyzing Sequential Firing Patterns Involving an Arbitrary Number of Neurons Based on Relative Time Order J Neurophysiol, October 1, 2004; 92(4): 2555 - 2573. [Abstract] [Full Text] [PDF]

				L. Paninski, S. Shoham, M. R. Fellows, N. G. Hatsopoulos, and J. P. Donoghue Superlinear Population Encoding of Dynamic Hand Trajectory in Primary Motor Cortex J. Neurosci., September 29, 2004; 24(39): 8551 - 8561. [Abstract] [Full Text] [PDF]

				S. R. Lehky Bayesian Estimation of Stimulus Responses in Poisson Spike Trains Neural Comput., July 1, 2004; 16(7): 1325 - 1343. [Abstract] [Full Text] [PDF]

				J. M. Samonds, J. D. Allison, H. A. Brown, and A. B. Bonds Cooperation between Area 17 Neuron Pairs Enhances Fine Discrimination of Orientation J. Neurosci., March 15, 2003; 23(6): 2416 - 2425. [Abstract] [Full Text] [PDF]

				A. Kuhn, A. Aertsen, and S. Rotter Higher-Order Statistics of Input Ensembles and the Response of Simple Model Neurons Neural Comput., January 1, 2003; 15(1): 67 - 101. [Abstract] [Full Text] [PDF]

				H. Nakahara and S.-i. Amari Information-Geometric Measure for Neural Spikes Neural Comput., October 1, 2002; 14(10): 2269 - 2316. [Abstract] [Full Text]

				S. Grun, M. Diesmann, and A. Aertsen Unitary Events in Multiple Single-Neuron Spiking Activity: I. Detection and Significance Neural Comput., January 1, 2002; 14(1): 43 - 80. [Abstract] [Full Text] [PDF]