Ergodicity of spike trains: when does trial averaging make sense? Export

@article{citeulike:1419861, abstract = {Neuronal information processing is often studied on the basis of spiking patterns. The relevant statistics such as firing rates calculated with the peri-stimulus time histogram are obtained by averaging spiking patterns over many experimental runs. However, animals should respond to one experimental stimulation in real situations, and what is available to the brain is not the trial statistics but the population statistics. Consequently, physiological ergodicity, namely, the consistency between trial averaging and population averaging, is implicitly assumed in the data analyses, although it does not trivially hold true. In this letter, we investigate how characteristics of noisy neural network models, such as single neuron properties, external stimuli, and synaptic inputs, affect the statistics of firing patterns. In particular, we show that how high membrane potential sensitivity to input fluctuations, inability of neurons to remember past inputs, external stimuli with large variability and temporally separated peaks, and relatively few contributions of synaptic inputs result in spike trains that are reproducible over many trials. The reproducibility of spike trains and synchronous firing are contrasted and related to the ergodicity issue. Several numerical calculations with neural network examples are carried out to support the theoretical results.}, author = {Masuda, N. and Aihara, K.}, citeulike-article-id = {1419861}, citeulike-linkout-0 = {http://dx.doi.org/10.1162/089976603321780308}, citeulike-linkout-1 = {http://neco.mitpress.org/cgi/content/abstract/15/6/1341}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/12816576}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=12816576}, day = {1}, doi = {10.1162/089976603321780308}, issn = {0899-7667}, journal = {Neural computation}, keywords = {averaging, network, population, variability}, month = {June}, number = {6}, pages = {1341--1372}, posted-at = {2007-09-26 16:41:27}, priority = {2}, title = {Ergodicity of spike trains: when does trial averaging make sense?}, url = {http://dx.doi.org/10.1162/089976603321780308}, volume = {15}, year = {2003} }

TY - JOUR ID - citeulike:1419861 L3 - citeulike-article-id:1419861 N2 - Neuronal information processing is often studied on the basis of spiking patterns. The relevant statistics such as firing rates calculated with the peri-stimulus time histogram are obtained by averaging spiking patterns over many experimental runs. However, animals should respond to one experimental stimulation in real situations, and what is available to the brain is not the trial statistics but the population statistics. Consequently, physiological ergodicity, namely, the consistency between trial averaging and population averaging, is implicitly assumed in the data analyses, although it does not trivially hold true. In this letter, we investigate how characteristics of noisy neural network models, such as single neuron properties, external stimuli, and synaptic inputs, affect the statistics of firing patterns. In particular, we show that how high membrane potential sensitivity to input fluctuations, inability of neurons to remember past inputs, external stimuli with large variability and temporally separated peaks, and relatively few contributions of synaptic inputs result in spike trains that are reproducible over many trials. The reproducibility of spike trains and synchronous firing are contrasted and related to the ergodicity issue. Several numerical calculations with neural network examples are carried out to support the theoretical results. IS - 6 JF - Neural computation SN - 0899-7667 EP - 1372 TI - Ergodicity of spike trains: when does trial averaging make sense? VL - 15 SP - 1341 KW - averaging KW - network KW - population KW - variability AU - Masuda, N AU - Aihara, K PY - 2003/06/01/ UR - http://dx.doi.org/10.1162/089976603321780308 ER -