Towards tracking homeostatic changes on high-density multielectrode arrays

Abstract

Event Abstract Back to Event Towards tracking homeostatic changes on high-density multielectrode arrays Dagmara Panas1*, Alessandro Maccione2, Luca Berdondini2 and Matthias Hennig1 1 University of Edinburgh, United Kingdom 2 Italian Institute of Technology, Italy Homeostatic plasticity is one of the key mechanisms ensuring the remarkable adaptive abilities of the brain. However, this is still a relatively scantly explored branch of both experimental and computational neuroscience - in particular on a large, multi-neuronal scale. With recent advance in recording techniques, the lack of experimental data can be easily overcome – novel multielectrode arrays allow for high-density recordings from in vitro cultures consisting of thousands of neurons. What is needed to complement this rich data is analysis techniques that would be able to shed some light on the mechanism of the underlying process – in contrast to most conventional analysis techniques, such as firing rates, correlations or inter-burst intervals, which provide little more than descriptive information. In search for measures able to capture more complex phenomena, over the last decade a new approach has been developed - pairwise maximum entropy modelling (MaxEnt). It is a statistical model that fits two sets of parameters to explain the probability of spiking patterns in the network: individual neuron parameters that could be interpreted as excitability; and pairwise interaction parameters that could be interpreted as the functional connection strength between neurons. Successful application of this model to a variety of recordings has helped reevaluate the importance of neuronal interactions in shaping network activity (Schneidman et al., 2006; Shlens et al., 2006). Additionally, the shortcomings of MaxEnt in certain cases can serve as an indicator of higher-order interactions between neurons (Ohiorhenuan et al., 2010). In present work we examine the extent to which the statistics of MaxEnd model fits and parameters can assist in understanding different modes of activity of a neuronal culture – specifically, along the duration of a homeostatic experiment. Neural activity from primary neuron cultures was recorded with the 4096 channel Active Pixel Sensor (APS) MEA, allowing for reliable isolation of single unit activity at near-cellular resolution (Berdondini et al., 2009). 20-minute datasets were obtained at different stages of homeostatic compensation during and after long-term CNQX application. For the datasets with a stationary activity state, large numbers of four-unit MaxEnt models were constructed for randomly chosen neurons on two spatial scales. Comparison of the statistics of the fits and parameters across the scales and across conditions indicates that different activity modes exhibit different profiles of local clustering and higher-order interactions. References Ohiorhenuan IE, Mechler F, Purpura KP, Schmid AM, Hu Q, Victor JD: Sparse coding and high-order interactions in fine-scale cortical networks. Nature 2010, 466:617-621 Schneidman E, Bialek W: Ising models for networks of real neurons. Nature 2006, 440:1007-1012 Shlens J, Field GD, Gauthier JL, Grivich MI, Petrusca D, Sher A, Litke AM, Chichilnisky EJ: The structure of multi-neuron firing patterns in primate retina. J Neuroscience 2006, 26(32):8254-8266 Keywords: cultured neurons, Homeostasis, Ising Model, multielectrode array Conference: Bernstein Conference 2012, Munich, Germany, 12 Sep - 14 Sep, 2012. Presentation Type: Poster Topic: Abstracts Citation: Panas D, Maccione A, Berdondini L and Hennig M (2012). Towards tracking homeostatic changes on high-density multielectrode arrays. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference 2012. doi: 10.3389/conf.fncom.2012.55.00150 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: 11 May 2012; Published Online: 12 Sep 2012. * Correspondence: Miss. Dagmara Panas, University of Edinburgh, Edinburgh, United Kingdom, d.panas@sms.ed.ac.uk 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 Dagmara Panas Alessandro Maccione Luca Berdondini Matthias Hennig Google Dagmara Panas Alessandro Maccione Luca Berdondini Matthias Hennig Google Scholar Dagmara Panas Alessandro Maccione Luca Berdondini Matthias Hennig PubMed Dagmara Panas Alessandro Maccione Luca Berdondini Matthias Hennig 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.