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• W116758865 abstract "Event Abstract Back to Event Functional Connectivity between Neuronal Ensembles through Nonlinear Modeling Increasing availability of multi-unit data gives new urgency to the need for effective tools to analyze the recorded activity of neuronal ensembles. Moreover, the implementation of neuroprosthetic devices requires a reliable and accurate quantitative representation of the input-output transformations present in the system under study. Nonparametric, data driven models with predictive capabilities are excellent candidates for these purposes. When modeling input-output relations in multi-input neuronal systems, it is important to select the subset of inputs that are functionally and causally related to the output. Inputs that do not convey information about the actual transformation not only increase the computational burden but also affect the generalization of the model. Moreover, a reliable functional connectivity measure can provide patterns of information flow that can be linked to physiological and anatomical properties of the system. We propose a method based on the Volterra modeling approach that selects distinct subsets of inputs for each output based on the prediction of the respective models and its statistical evaluation using the Mann-Whitney statistic. The algorithm builds successive models with increasing number of inputs and examines whether the inclusion of additional inputs benefits the predictive accuracy of the overall model. The method accounts for nonlinear causal relationships between the inputs and outputs. It also explores possible second-order (inter-modulatory) cross-interactions among the inputs. The method’s robustness to various cases of point-process noise (spurious spikes, spike jitter, deleted spikes, missasigned spikes) was tested through simulated examples. Comparison of the proposed algorithm’s performance with widely used methods such as Granger causality and cross-coherence based methods reveals greater robustness to noise. The method was applied to multi-unit recordings from the CA3 (input) and CA1 (output) regions of the hippocampus in behaving rats, in order to reveal spatiotemporal connectivity maps of the input-output transformation taking place in the CA3-CA1 synapse. The contribution of the nonlinear components and the inclusion of cross-interacting inputs accounts for 25% and 8% more connections respectively, when the algorithm is applied to hippocampal data. The method provides a practical, data driven and computationally efficient way to explore causal connections among multiple neuronal ensembles, while accounting for nonlinearities and cross-interactions among the inputs of the system. 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). Functional Connectivity between Neuronal Ensembles through Nonlinear Modeling. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.116 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 Feb 2009; Published Online: 02 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 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." @default.
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• W116758865 title "Functional Connectivity between Neuronal Ensembles through Nonlinear Modeling" @default.
• W116758865 doi "https://doi.org/10.3389/conf.neuro.06.2009.03.116" @default.
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