Testing a neural coding hypothesis using surrogate data.

@article{hirata_08_testing, abstract = {Determining how a particular neuron, or population of neurons, encodes information in their spike trains is not a trivial problem, because multiple coding schemes exist and are not necessarily mutually exclusive. Coding schemes generally fall into one of two broad categories, which we refer to as rate and temporal coding. In rate coding schemes, information is encoded in the variations of the average firing rate of the spike train. In contrast, in temporal coding schemes, information is encoded in the specific timing of the individual spikes that comprise the train. Here, we describe a method for testing the presence of temporal encoding of information. Suppose that a set of original spike trains is given. First, surrogate spike trains are generated by randomizing each of the original spike trains subject to the following constraints: the local average firing rate is approximately preserved, while the overall average firing rate and the distribution of primary interspike intervals are perfectly preserved. These constraints ensure that any rate coding of information present in the original spike trains is preserved in the members of the surrogate population. The null-hypothesis is rejected when additional information is found to be present in the original spike trains, implying that temporal coding is present. The method is validated using artificial data, and then demonstrated using real neuronal data.}, address = {Aihara Complexity Modelling Project, ERATO, JST, Japan; Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan; Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.}, author = {Hirata, Yoshito and Katori, Yuichi and Shimokawa, Hidetoshi and Suzuki, Hideyuki and Blenkinsop, Timothy A. A. and Lang, Eric J. J. and Aihara, Kazuyuki }, citeulike-article-id = {2970656}, doi = {http://dx.doi.org/10.1016/j.jneumeth.2008.05.004}, issn = {0165-0270}, journal = {Journal of neuroscience methods}, keywords = {coding, method, rate, temporal}, month = {May}, pdf = {hirata\_08\_testing.pdf}, posted-at = {2008-07-09 14:07:03}, priority = {2}, title = {Testing a neural coding hypothesis using surrogate data.}, url = {http://dx.doi.org/10.1016/j.jneumeth.2008.05.004}, year = {2008} }

TY - JOUR ID - hirata_08_testing L3 - citeulike-article-id:2970656 TI - Testing a neural coding hypothesis using surrogate data. JF - Journal of neuroscience methods AD - Aihara Complexity Modelling Project, ERATO, JST, Japan; Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan; Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan. SN - 0165-0270 N2 - Determining how a particular neuron, or population of neurons, encodes information in their spike trains is not a trivial problem, because multiple coding schemes exist and are not necessarily mutually exclusive. Coding schemes generally fall into one of two broad categories, which we refer to as rate and temporal coding. In rate coding schemes, information is encoded in the variations of the average firing rate of the spike train. In contrast, in temporal coding schemes, information is encoded in the specific timing of the individual spikes that comprise the train. Here, we describe a method for testing the presence of temporal encoding of information. Suppose that a set of original spike trains is given. First, surrogate spike trains are generated by randomizing each of the original spike trains subject to the following constraints: the local average firing rate is approximately preserved, while the overall average firing rate and the distribution of primary interspike intervals are perfectly preserved. These constraints ensure that any rate coding of information present in the original spike trains is preserved in the members of the surrogate population. The null-hypothesis is rejected when additional information is found to be present in the original spike trains, implying that temporal coding is present. The method is validated using artificial data, and then demonstrated using real neuronal data. KW - coding KW - method KW - rate KW - temporal AU - Hirata, Yoshito AU - Katori, Yuichi AU - Shimokawa, Hidetoshi AU - Suzuki, Hideyuki AU - Blenkinsop, Timothy AU - Lang, Eric AU - Aihara, Kazuyuki PY - 2008/May/15/ UR - http://dx.doi.org/10.1016/j.jneumeth.2008.05.004 ER -