Influence of the basilar membrane shape and mechanical properties in the cochlear response: A numerical study

Study on damage of the macrostructure of the cochlea under the impact load

Due to the tiny and delicate structure of the cochlea, the auditory system is the most sensitive to explosion impact damage. After being damaged by the explosion impact wave, it usually causes long-term deafness, tinnitus, and other symptoms. To better understand the influence of impact load on the cochlea and basilar membrane (BM), a three-dimensional (3D) fluid-solid coupling finite element model was developed. This model accurately reflects the actual spatial spiral shape of the human cochlea, as well as the lymph environment and biological materials. Based on verifying the reliability of the model, the curve of impact load-amplitude response was obtained, and damage of impact load on the cochlea and the key macrostructure-BM was analyzed. The results indicate that impact wave at middle frequency has widest influence on the cochlea. Furthermore, impact loading causes tears in the BM and destroys the cochlear frequency selectivity.

A bioinspired configurable cochlea based on memristors

Cochleas are the basis for biology to process and recognize speech information, emulating which with electronic devices helps us construct high-efficient intelligent voice systems. Memristor provides novel physics for performing neuromorphic engineering beyond complementary metal-oxide-semiconductor technology. This work presents an artificial cochlea based on the shallen-key filter model configured with memristors, in which one filter emulates one channel. We first fabricate a memristor with the TiN/HfOx/TaOx/TiN structure to implement such a cochlea and demonstrate the non-volatile multilevel states through electrical operations. Then, we build the shallen-key filter circuit and experimentally demonstrate the frequency-selection function of cochlea’s five channels, whose central frequency is determined by the memristor’s resistance. To further demonstrate the feasibility of the cochlea for system applications, we use it to extract the speech signal features and then combine it with a convolutional neural network to recognize the Free Spoken Digit Dataset. The recognition accuracy reaches 92% with 64 channels, compatible with the traditional 64 Fourier transform transformation points of mel-frequency cepstral coefficients method with 95% recognition accuracy. This work provides a novel strategy for building cochleas, which has a great potential to conduct configurable, high-parallel, and high-efficient auditory systems for neuromorphic robots.