Hidden structures detection in nonstationary spike trains

Abstract

Event Abstract Back to Event Hidden structures detection in nonstationary spike trains Ken Takiyama1* and Masato Okada2 1 The University of Tokyo, Japan 2 RIKEN ,The University of Tokyo, Japan Elucidating neural encoding from irregular neural activities is one of the most important issues in neuroscience. Firing rates abundantly include information encoded by neurons and are often estimated averaging neural activities across trials. We however need to estimate firing rates using only one or the least spike trains especially in the case that neural activities represent internal processes such as decision making and motor planning because the process differs from trial to trial. Firing rate estimation using only one spike train hence plays a crucial role to elucidate neural encoding. Many studies have been proving probabilistic models can estimate firing rates using one spike train in recent years. These studies assume stationarity of mean and temporal correlation within a trial. Stationarity means time series has temporally uniform statistical property. We call what does not have stationarity as nonstationary. Many studies, using hidden markov model (HMM), have been showing neural network activity transit among neural states within a trial. Since statistical properties of firing rates differ drastically in all neural states, firing rates include nonstationarity with in a trial in many cases. Recent studies have been indicating that neural states reflect the properties of inputs encoded by neurons. Because the studies also suggest the transitions show trial-by-trial variation, neural state estimation using only one spike train plays a crucial role for elucidating neural encoding. We construct the algorithm that can simultaneously estimate nonstationary firing rates, neural state transition timings and neural state numbers using only one spike train. Our algorithm consists of Switching State Space Model(SSSM). SSSM defines more than one prior distributions one of which generates observation data. SSSM can also estimate neural state transition timings where a prior distribution that generates observation switches. The HMMs assume firing rates are constant in each neural state. Firing rates however naturally showing temporal variation, Excluding the time variation probably obscures the neural state information. SSSM enables to estimate neural state transitions with considering the temporal variation. Learning and estimation algorithm are constituted of Variational Bayes method. Automatic relevance determination induced by Variational Bayes enables to estimate neural state number at a time. Synthetic data analysis shows our algorithm can simultaneously estimate nonstationary firing rates, neural state transition timings and neural state numbers using one spike train with high accuracy. Applying to real data, which is available on Neural Signal Archive, reveals our algorithm can estimate neural state information from area MT data. These neural states probably correspond to transient and sustained states which have been detected heuristically in area MT data. We confirm the HMM can not detect these neural states. These results suggest our algorithm has the versatility in neural state estimation and the effectiveness on neural encoding elucidation. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Poster Presentation Topic: Poster session III Citation: Takiyama K and Okada M (2010). Hidden structures detection in nonstationary spike trains. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00168 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: 02 Mar 2010; Published Online: 02 Mar 2010. * Correspondence: Ken Takiyama, The University of Tokyo, Chiba, Japan, takiyama@mns.k.u-tokyo.ac.jp 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 Ken Takiyama Masato Okada Google Ken Takiyama Masato Okada Google Scholar Ken Takiyama Masato Okada PubMed Ken Takiyama Masato Okada 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.