HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Supplemental Figures and Tables All Versions of this Article: 102/1/100    most recent 90699.2008v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Künsting, T. Articles by Spors, H. PubMed PubMed Citation Articles by Künsting, T. Articles by Spors, H.

Dynamics of Input Patterns Modulate the Behavior of a Model of Olfactory Bulb Function

¹Win Group of Olfactory Dynamics, Heidelberger Akademie der Wissenschaften, Heidelberg; ²Max Planck Institute for Medical Research, Heidelberg; and ³Department of Molecular Neurogenetics, Max Planck Institute of Biophysics, Frankfurt am Main

Submitted 21 June 2008; accepted in final form 1 April 2009

Input patterns to the olfactory bulb are dynamic and change in an odor-specific manner as measured by selective calcium imaging of olfactory bulb input. To our knowledge, none of the published models of olfactory bulb function uses dynamic input patterns. Therefore we tested how dynamic input alters the behavior of a simple model consisting of two layers. The membrane potential of the first-layer neurons, integrate-and-fire neurons corresponding to mitral cells, was modulated with a subthreshold oscillation at respiration frequency. The membrane potential of the second-layer neurons was used to discriminate input patterns. We implemented oscillating input with amplitudes and latencies different for each mitral cell. Not only varying the input amplitudes but also de-synchronizing the input, and varying the relation between latency and input amplitude, individually changed the model's performance significantly. The discrimination time was affected more easily than the number of second-layer neurons that can differentiate an odor pair. Increasing the de-synchronization, i.e., the spread of latency values, reduced the differences in response time between strong and weak stimulus pairs without reducing the number of reacting cells. Input phase relative to the subthreshold oscillation altered the effect of de-synchronization. Thus dynamic input changes performance parameters of models of olfactory information processing that can be verified experimentally.