Event Abstract Back to Event Which model can properly describe dynamics and smoothness of firing rate? Firing rate, which is assumed to be inherent in the observed spike trains, plays an important role in decoding. Though estimating the true firing rate requires sufficiently many spike trains, we have to estimate the firing rate using limited spike trains that are obtained from a neurophysiological experiment. In such a situation, estimating firing rate with probabilistic model is effective. Selecting the proper probabilistic model that can describe both the dynamics and the appropriate smoothness of the firing rate hence is one of the issues in neuroscience. We construct the algorithm that can simultaneously estimate the firing rate, selects the proper model, and determine the proper smoothness depending on the functional form of the firing rate using belief propagation (BP). Since the BP can calculate the marginal likelihood, we can select the proper model and determine the proper smoothness based on model selection framework and empirical Bayes method, respectively. The prior distributions are Line process model, Gaussian model, and Cauchy model, which corresponds to the special case of Student'st model. These models can describe a priori knowledge that the firing rate evolves smoothly. Both the Line process model and the Cauchy process model can also express the discontinuous variation. We estimate two kinds of the firing rates: The first firing rate shows temporally smooth fluctuation, but the second firing rate includes discontinuous variation. We discuss which model is the best in each firing rate estimation experiment. Estimating the firing rate using a limited number of spike trains involve the assumption that the firing rate includes smoothness of which both the form and the degree are unknown. The Line process model, in the first experiment, gives the largest marginal likelihood value of the three models, which implies that the Line process model can appropriately express both the form and the degree of the firing rate smoothness. On the other hand, stimulus added to the neuron causes the discontinuous variation to the firing rate. Since many neurophysiological experiments observe the neural responses to stimuli, the algorithm that can estimate the firing rate involving discontinuity is needed. But there has yet to be the algorithm if the stimulus timings are entirely-unknown. We show, in the second experiment, the Line process model can also describe the discontinuous firing rate the best. The Line process model being able to estimate the unknown stimulus timings, we show the effectiveness of the Line process model on estimating the firing rate involving the discontinuous variation. 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). Which model can properly describe dynamics and smoothness of firing rate?. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.256 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: 03 Feb 2009; Published Online: 03 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract Supplemental Data 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.