Physiological and psychophysical methods allow for an extended investigation of ascending (afferent) neural pathways from the ear to the brain in mammals, and their role in enhancing signals in noise. However, there is increased interest in descending (efferent) neural fibers in the mammalian auditory pathway. This efferent pathway operates via the olivocochlear system, modifying auditory processing by cochlear innervation and enhancing human ability to detect sounds in noisy backgrounds. Effective speech intelligibility may depend on a complex interaction between efferent time-constants and types of background noise. In this study, an auditory model with efferent-inspired processing provided the front-end to an automatic-speech-recognition system (ASR), used as a tool to evaluate speech recognition with changes in time-constants (50 to 2000 ms) and background noise type (unmodulated and modulated noise). With efferent activation, maximal speech recognition improvement (for both noise types) occurred for signal-to-noise ratios around 10 dB, characteristic of real-world speech-listening situations. Net speech improvement due to efferent activation (NSIEA) was smaller in modulated noise than in unmodulated noise. For unmodulated noise, NSIEA increased with increasing time-constant. For modulated noise, NSIEA increased for time-constants up to 200 ms but remained similar for longer time-constants, consistent with speech-envelope modulation times important to speech recognition in modulated noise. The model improves our understanding of the complex interactions involved in speech recognition in noise, and could be used to simulate the difficulties of speech perception in noise as a consequence of different types of hearing loss.

The human auditory efferent system may play a role in improving speech-in-noise recognition with an associated range of time constants. Computational auditory models with efferent-inspired feedback demonstrate improved speech-in-noise recognition with long efferent time constants (2000 ms). This study used a similar model plus an Automatic Speech Recognition (ASR) system to investigate the role of shorter time constants. ASR speech recognition in noise improved with efferent feedback (compared to no-efferent feedback) for both short and long efferent time constants. For some signal-to-noise ratios, speech recognition in noise improved as efferent time constants were increased from 118 to 2000 ms.

This paper presents a fully implantable multi-channel neural prosthesis for epidural stimulation. The prosthesis features three telemetry-operated independent stimulators providing in total eighteen stimulation channels. The stimulator circuits were implemented in a 0.6-µm CMOS technology. The prosthesis is protected in a hermetically sealed ceramic enclosure and encapsulated in medical grade silicone rubber. In-vitro measured results with electrodes in saline are presented.

This paper presents a fully implantable closed-loop device for use in freely moving rodents to investigate new treatments for motor neuron disease. The 0.18 µm CMOS integrated circuit comprises 4 stimulators, each featuring 16 channels for optical and electrical stimulation using arbitrary current waveforms at frequencies from 1.5 Hz to 50 kHz, and a bandwidth programmable front-end for neural recording. The implant uses a Qi wireless inductive link which can deliver >100 mW power at a maximum distance of 2 cm for a freely moving rodent. A backup rechargeable battery can support 10 mA continuous stimulation currents for 2.5 hours in the absence of an inductive power link. The implant is controlled by a graphic user interface with broad programmable parameters via a Bluetooth low energy bidirectional data telemetry link. The encapsulated implant is 40 mm × 20 mm × 10 mm. Measured results are presented showing the electrical performance of the electronics and the packaging method.

This paper presents a prototype integrated bidirectional stimulator ASIC capable of mixed opto-electro stimulation and electrophysiological signal recording. The development is part of the research into a fully implantable device for treating motor neurone disease using optogenetics and stem cell technology. The ASIC consists of 4 stimulator units, each featuring 16-channel optical and electrical stimulation using arbitrary current waveforms with an amplitude up to 16 mA and a frequency from 1.5 Hz to 50 kHz, and a recording front-end with a programmable bandwidth of 1 Hz to 4 kHz, and a programmable amplifier gain up to 74 dB. The ASIC was implemented in a 0.18-µm CMOS technology. Simulated performance in stimulation and recording is presented.