Power-law distributions of inter-spike intervals in in vivo cortical neurons

Abstract

Event Abstract Back to Event Power-law distributions of inter-spike intervals in in vivo cortical neurons Spike sequences recorded from cortical neurons in an awake animal are known to be highly irregular. The irregular spike sequences carry crucial information for understanding complicated balance between excitatory and inhibitory inputs and intrinsic spike-generating mechanisms of neurons. Irregular spike sequences in vitro have been explained by various stochastic processes, especially the gamma process. However, it is still unclear how cortical neurons in vivo generate the spike sequence. To clarify this, we recorded the spike sequence of identified neurons in the rat motor cortex by juxtacellular recording. We found that the inter-spike interval (ISI) distribution of neurons recorded in this in vivo experiment exhibited a power-law decay rather than an exponential decay of the gamma distribution. This power-law was commonly found in pyramidal and fast-spiking neurons in different cortical layers, although the power-law exponents varied from neuron to neuron. We found that the experimentally observed ISI distributions can be well explained by spike generation through doubly stochastic gamma process (DSGP) model. This model is based on two hypotheses. First, an observed ISI is determined by a gamma distribution with regularity parameter (kappa) and instantaneous firing rate (xi). Second, (xi) is determined by another gamma distribution with regularity parameter (alpha) and mean firing rate R. The latter gamma distribution describes the distribution of time-varying firing rate of in vivo neurons. In this DSGP model, the observed ISIs obey a beta distribution of the second kind (Beta-2) with power-law decay if (xi) varies on time, while the observed ISIs obey a gamma distribution with exponential decay if (xi)is constant. This model is consistent with the previous result of in vitro experiments where instantaneous firing rate (xi) was constant and the observed ISIs obeyed a gamma distribution. We found that the regularity (kappa) of the intrinsic spike-generating mechanism and the regularity (alpha) of the time-varying instantaneous firing rate were different in different neurons. The recorded neurons were classified into three types by the regularity parameters (kappa) and (alpha): the promptly spike-generating type (large (kappa)), the stationary rate type (large (alpha)), and irregular firing type (small (kappa) and (alpha)). The neurons of former two types were found mostly in deep layers, while those of the last type were found in both deep and superficial layers. These results imply that information coding scheme of cortical networks is layer dependent. 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). Power-law distributions of inter-spike intervals in in vivo cortical neurons. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.320 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: 04 Feb 2009; Published Online: 04 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.