Abstract
Event Abstract Back to Event Active dendrites: adaptation to spike-based communication B. Ujfalussy1* and M. Lengyel2 1 KFKI Res Inst for Particle and Nuclear Physics, Hungary 2 University of Cambridge, Department of Engineering, United Kingdom Dendritic trees possess a plethora of voltage-dependent conductances that render them sophisticated computational devices well beyond the realm of traditional cable theory. Branch specific regulation of dendritic excitability and synaptic plasticity suggests that local nonlinear processes play an essential role in single-cell and network computations. Indeed, much of classical theoretical work focussed on how units that perform complex, nonlinear mappings from their inputs to their outputs contribute to cognitively relevant computations. However, the variables relevant for such computations have often been assumed to be represented by analogue quantities (e.g., somatic membrane potential), ignoring that neurons communicate by spikes, and spike generation loses information about subthreshold membrane potential dynamics. Here we studied how the nonlinear properties of dendrites may be adapted to the computational demands of spike-based communication. We found that the optimal implementation of even purely linear dendritic computations requires the interplay of many independent nonlinear subunits within the postsynaptic dendritic tree. We demonstrate that nonlinear dendritic trees bring significant benefits to single-neuron computation across a wide range of input correlations, even if the individual synapses are optimal estimators of the corresponding presynaptic membrane potentials. Keywords: Neurophysiology, Neuroscience Conference: 13th Conference of the Hungarian Neuroscience Society (MITT), Budapest, Hungary, 20 Jan - 22 Jan, 2011. Presentation Type: Abstract Topic: Neurophysiology Citation: Ujfalussy B and Lengyel M (2011). Active dendrites: adaptation to spike-based communication. Front. Neurosci. Conference Abstract: 13th Conference of the Hungarian Neuroscience Society (MITT). doi: 10.3389/conf.fnins.2011.84.00063 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 Mar 2011; Published Online: 23 Mar 2011. * Correspondence: Dr. B. Ujfalussy, KFKI Res Inst for Particle and Nuclear Physics, Budapest, Hungary, ubi@rmki.kfki.hu 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 B. Ujfalussy M. Lengyel Google B. Ujfalussy M. Lengyel Google Scholar B. Ujfalussy M. Lengyel PubMed B. Ujfalussy M. Lengyel 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.
Highlights
The operation of neural circuits fundamentally depends on the capacity of neurons to perform complex, nonlinear mappings from their inputs to their outputs
We present predictions about how the statistics of presynaptic inputs should be matched by the clustering patterns of synaptic inputs onto active subunits of the dendritic tree
That while in the multivariate Ornstein–Uhlenbeck (mOU) model, supralinear integration arises due to dynamical changes in uncertainty, in the extended model it is associated with a change in a hypothesis
Summary
The operation of neural circuits fundamentally depends on the capacity of neurons to perform complex, nonlinear mappings from their inputs to their outputs. Since the vast majority of synaptic inputs impinge the dendritic membrane, its morphology, and passive as well as active electrical properties play important roles in determining the functional capabilities of a neuron Both theoretical and experimental studies suggest that active, nonlinear processing in dendritic trees can significantly enhance the repertoire of singe neuron operations [1, 2]. Previous functional approaches to dendritic processing were limited because they studied dendritic computations in a firing rate-based framework [3, 4], essentially requiring both the inputs and the output of a cell to have stationary firing rates for hundreds of milliseconds They ignored the effects and consequences of temporal variations in neural activities at the time scale of inter-spike intervals characteristic of in vivo states [5].
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