Predicting Spike Trains in a Nonlinear Model of Active Auditory Mechanosensitivity

While much is known about the process of hearing, how sounds are encoded into neural signals is still largely unsolved. We build on previous models of mechanosensitivity, ion transport, and neurotransmitter release to produce a theoretical model for sound detection in auditory hair cells. These sensory cells detect vibrations in the inner ear and convert them into electrical signals that trigger spike trains in the innervating neurons. The model begins with a description of the active nonlinear mechanosensitivity in the hair cell bundle, proceeds to describe the electrical circuit of ion channels in the soma, and models the neurotransmitter release at the synapse. Therefore, we can predict the neural spike train elicited from specific displacement-wave signals at the hair bundle stereocilia. We also introduce a new method for analyzing spike train patterns to describe the encoding of these time-dependent signals. We aim to build off this method to accurately describe both the encoding and decoding of sounds into and from neural signals via cellular mechanosensitivity.