System identification and modeling of primary electrosensory afferent response dynamics in the weakly electric fish Apteronotus leptorhynchus
Xu, Zhian
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Permalink
https://hdl.handle.net/2142/22318
Description
Title
System identification and modeling of primary electrosensory afferent response dynamics in the weakly electric fish Apteronotus leptorhynchus
Author(s)
Xu, Zhian
Issue Date
1996
Doctoral Committee Chair(s)
Nelson, Mark E.
Department of Study
Biophysics and Computational Biology
Discipline
Biophysics and Computational Biology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Biology, Neuroscience
Biophysics, General
Language
eng
Abstract
The first stage of information processing in the electrosensory system of weakly electric fish involves the encoding of changes in transdermal potential into trains of action potentials in primary afferent nerve fibers. Using amplitude modulations (AMs) of the electric organ discharge (EOD), we systematically studied the stimulus coding properties of probability coding (P-type) electrosensory afferents in the weakly electric fish Apteronotus leptorhynchus (brown ghost knife fish). In response to brief (1 sec) AM step stimuli, we find that the time course of firing rate adaptation in P-type afferents can be approximated by one or two exponentially decaying components plus a tonic component. If the duration of the step stimulus is prolonged, however, the apparent tonic component of the response continues to fall slowly with time. By recording responses to prolonged step stimuli of 10-20 minutes in duration, we show that the overall time course is better described by a logarithmic function of the form A/(log(t) + B). For most units this form successfully fits the entire time course of the response over 5 log units in time, front milliseconds to hundreds of seconds, using just two free parameters. Based on experimental data using both step and sinusoidal AM stimuli, we have constructed a linear-nonlinear cascade model to describe the response properties of P-type afferents. We use parametric system identification technique to quantitatively characterize the response dynamics of the system. Comparisons of experimental data and model predictions to filtered white noise AM stimuli show that our linear-nonlinear cascade model can accurately predict responses to arbitrary AM stimuli. Finally a preliminary biophysical model of P-type afferent response dynamics has been developed to help identify cellular mechanisms that influence the encoding properties of primary afferent nerve fibers.
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