Detection of non-stationary higher-order spike correlation

Abstract

Event Abstract Back to Event Detection of non-stationary higher-order spike correlation Precise spike coordination in the spiking activities of a neuronal population is discussed as an indication of coordinated network activity in form of a cell assembly relevant for information processing. Supportive evidence for its relevance in behavior was provided by the existence of excess spike synchrony occurring dynamically in relation to behavioral context [e.g. Riehle et. al., Science (278) 1950-1953, 1997]. This finding was based on the null-hypothesis of full independence. However, one can assume that neurons jointly involved in assemblies express higher-order correlation (HOC) between their activities. Previous work on HOC assumed stationary condition. Here we aim at analyzing simultaneous spike trains for time-dependent HOCs to trace active assemblies. We suggest to estimate the dynamics of HOCs by means of a state-space analysis with a log-linear observation model. A log-linear representation of the parallel spikes provides a well-defined measure of HOC based on information geometry (Amari, IEEE Trans. Inf. Theory (47) 1701-1711, 2001). We developed a nonlinear recursive filtering/smoothing algorithm for the time-varying log-linear model by applying a log-quadratic approximation to its posterior distribution. The time-scales of each parameter and their covariation are automatically optimized via the EM-algorithm under the maximum likelihood principle. To obtain the most predictive model, we compare the goodness-of-fit of hierarchical log-linear models with different order of interactions using the Akaike information criterion (AIC; Akaike, IEEE Trans. Autom. Control (19) 716-723, 1974). While inclusion of increasingly higher-order interaction terms improves model accuracy, estimation of higher-order parameters suffers from large variances due to the paucity of synchronous spikes in the data. This bias-variance trade-off is optimally resolved with the model that minimizes the AIC. Complexity of the model is thus selected based on the sample size of the data and the prominence of the higher-order structure. Application of the proposed method to simultaneous recordings of neuronal activity is expected to provide us with new insights into the dynamics of assembly activities, their composition, and behavioral relevance. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Detection of non-stationary higher-order spike correlation. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.019 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 29 Jan 2009; Published Online: 29 Jan 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.