Literature DB >> 9263430

Modeling inhibition of type II units in the dorsal cochlear nucleus.

K E Hancock1, K A Davis, H F Voigt.   

Abstract

Type II units in the dorsal cochlear nucleus (DCN) are characterized by vigorous but nonmonotonic responses to best frequency tones as a function of sound pressure level, and relatively weak responses to noise. A model of DCN neural circuitry was used to explore two hypothetical mechanisms by which neurons may be endowed with type II unit response properties. Both mechanisms assume that type II units receive excitatory input from auditory nerve (AN) fibers and inhibitory input from an unspecified class of cochlear nucleus interneurons that also receive excitatory AN input. The first mechanism, a lateral inhibition (LI) model, supposes that type II units receive inhibitory input from a number of narrowly tuned interneurons whose best frequencies (BFs) flank the BF of the type II unit. Tonal stimuli near BF result in only weak inhibitory input, but broadband stimuli recruit enough lateral inhibitors to greatly weaken the type II unit response. The second mechanism, a wideband inhibition (WBI) model, supposes that type II units receive inhibitory input from interneurons that are broadly tuned so that they respond more vigorously to broadband stimuli than to tones. Physiological and anatomical evidence points to the possible existence of such a class of neurons in the cochlear nucleus. The model extends an earlier computer model of an iso-frequency DCN patch to multiple frequency slices and adds a population of interneurons to provide the inhibition to model type II units (called 12-cells). The results show that both mechanisms accurately simulate responses of type II units to tones and noise. An experimental paradigm for distinguishing the two mechanisms is proposed.

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Year:  1997        PMID: 9263430     DOI: 10.1007/s004220050355

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  3 in total

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Journal:  Ann Biomed Eng       Date:  2006-12       Impact factor: 3.934

2.  Fitting of dynamic recurrent neural network models to sensory stimulus-response data.

Authors:  R Ozgur Doruk; Kechen Zhang
Journal:  J Biol Phys       Date:  2018-06-02       Impact factor: 1.365

3.  Adaptive Stimulus Design for Dynamic Recurrent Neural Network Models.

Authors:  R Ozgur Doruk; Kechen Zhang
Journal:  Front Neural Circuits       Date:  2019-01-22       Impact factor: 3.492

  3 in total

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