Speaker
Description
Cochlear synaptopathy (CS) is a form of auditory neural damage characterized by the loss of synapses between inner hair cells and auditory nerve fibers (ANFs), often without changes in audiometric thresholds. It is associated with noise exposure and aging and is thought to underlie difficulties in speech perception in noisy environments despite normal hearing sensitivity. CS is believed to disproportionately affect high-threshold ANFs with low and medium spontaneous rates (LSR/MSR). However, isolating and quantifying the contribution of LSR/MSR fiber loss to speech-in-noise perception remains challenging.Here, we address this problem using a cross-species framework combining 1-D transmission line models of the human and gerbil auditory periphery, invasive gerbil recordings, and human behavioral data. A fixed pair of vowel stimuli (/i/ and /y/) is presented in noise across all measurements and simulations. A recurrent neural network, driven by CAP inter-spike interval features, is trained to discriminate between the vowels.The degree of cochlear deafferentation is calibrated using invasive measurements from normal-hearing and kainic acid (KA)-treated gerbils, a model for CS. This framework links ground-truth ANF recordings to human behavioral performance through a unified modeling approach.Preliminary results show that the loss of LSR and MSR fibers degrades speech-in-noise discrimination differently from the loss of high spontaneous rate (HSR) fibers or elevated auditory thresholds. Notably, complete loss of LSR fibers can be more detrimental than a 50% loss of HSR fibers, despite a smaller total reduction in fiber count.