Uncovering dynamical structure in spiking neuronal networks

Abstract

Event Abstract Back to Event Uncovering dynamical structure in spiking neuronal networks Robert Haslinger1* 1 Department of Brain and Cognitive Sciences, MIT, United States The strongly recurrent topologies of neuronal networks couple the activity of their component neurons together. Such coupling often manifests in coordinated patterns of action potentials across neurons. With the advent of multi-electrode arrays and optogenetic techniques it has become possible to simultaneously record action potentials from hundreds of neurons, providing a window into the brain’s workings at the scale of the cortical column network. Structure, important for computation, is presumably present in the spiking activity of these networks, however, discerning exactly what that structure is presents some unique challenges. First, neuronal networks are extremely large and spike sparsely, while recording times are often short. Standard regression models will thus have many parameters to fit, but comparatively few observations with which to fit them. Second, correlations between neurons are not constant but change, often rapidly, with time. Third, although neuronal networks are strongly coupled, it is currently unclear how to quantify network’s spiking statistics beyond that captured by pairwise couplings. If the functional role of neuronal correlations and patterns of population spiking activity are to be understood, and their connection to the underlying dynamics of neuronal networks made clear, it is crucial to develop methodologies capable of fully characterizing the statistics of large populations of spiking neurons. I will discuss the current state of the art (regularized logistic regression, state space modeling, and high dimensional pattern clustering) for tackling the above challenges, and demonstrate how these can be used to uncover dynamical structure in networks of spiking neurons recorded from primate M1 during working memory tasks. Keywords: neural activity, spiking neurons Conference: XI International Conference on Cognitive Neuroscience (ICON XI), Palma, Mallorca, Spain, 25 Sep - 29 Sep, 2011. Presentation Type: Symposium: Oral Presentation Topic: Abstracts Citation: Haslinger R (2011). Uncovering dynamical structure in spiking neuronal networks. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI). doi: 10.3389/conf.fnhum.2011.207.00534 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: 14 Nov 2011; Published Online: 28 Nov 2011. * Correspondence: Dr. Robert Haslinger, Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, United States, rob.haslinger@gmail.com 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 Robert Haslinger Google Robert Haslinger Google Scholar Robert Haslinger PubMed Robert Haslinger 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.