A phenomenological model of the synapse between the inner hair cell and auditory nerve: Long-term adaptation with power-law dynamics

There is growing evidence that the dynamics of biological systems that appear to be exponential over short time courses are in some cases better described over the long-term by power-law dynamics. A model of rate adaptation at the synapse between inner hair cells and auditory-nerve (AN) fibers that...

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Bibliographic Details
Published in:The Journal of the Acoustical Society of America 2009-11, Vol.126 (5), p.2390-2412
Main Authors: ZILANY, Muhammad S. A, BRUCE, Ian C, NELSON, Paul C, CARNEY, Laurel H
Format: Article
Language:English
Subjects:
Acoustic Stimulation
Adaptation, Physiological - physiology
Animals
Biological and medical sciences
Cochlear Nerve - physiology
Ear and associated structures. Auditory pathways and centers. Hearing. Vocal organ. Phonation. Sound production. Echolocation
Fundamental and applied biological sciences. Psychology
Hair Cells, Auditory, Inner - physiology
Humans
Models, Neurological
Noise
Perceptual Masking - physiology
Physiological Acoustics
Psychoacoustics
Synapses - physiology
Vertebrates: nervous system and sense organs
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Summary:There is growing evidence that the dynamics of biological systems that appear to be exponential over short time courses are in some cases better described over the long-term by power-law dynamics. A model of rate adaptation at the synapse between inner hair cells and auditory-nerve (AN) fibers that includes both exponential and power-law dynamics is presented here. Exponentially adapting components with rapid and short-term time constants, which are mainly responsible for shaping onset responses, are followed by two parallel paths with power-law adaptation that provide slowly and rapidly adapting responses. The slowly adapting power-law component significantly improves predictions of the recovery of the AN response after stimulus offset. The faster power-law adaptation is necessary to account for the "additivity" of rate in response to stimuli with amplitude increments. The proposed model is capable of accurately predicting several sets of AN data, including amplitude-modulation transfer functions, long-term adaptation, forward masking, and adaptation to increments and decrements in the amplitude of an ongoing stimulus.
ISSN:0001-4966
1520-8524
DOI:10.1121/1.3238250