Neural Implant Signal Processing for Naturalistic Cochlear Timing
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Solution Overview
Problem
Cochlear implants have difficulty in accurately conveying pitch and fine timing of sound, particularly affecting users of tonal languages and music appreciation, due to unrealistic neural responses and computational inefficiencies in processing sound in real time.
Innovation Solution
A method and system utilizing a trained recurrent neural network to process input sound patterns and transform them into naturalistic firing patterns for cochlear implants, accounting for outer and inner hair cell contributions, synaptic, and axonal activation, enabling faster and more accurate simulation of neural responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a phenomenological model is used to process sound and produce fine timing for cochlear neurons, then pitch perception and fine timing accuracy are improved, but processing time becomes excessively long and real-time processing is not achieved
Solution Approach 1:
The patent creates a simplified computational copy of the complex phenomenological model using machine learning algorithms. Instead of directly implementing the full biophysical model, the system trains neural networks to replicate its timing predictions, achieving comparable fine timing accuracy with dramatically reduced computational requirements that enable real-time processing
Solution Approach 2:
The patent replaces the computationally intensive mechanical/biophysical modeling approach with a machine learning-based system. By substituting the traditional phenomenological model with trained neural networks, the system achieves the same timing precision function with much lower computational overhead, enabling real-time operation
2Reliability
If high-rate pulsatile stimulation is used to desynchronize neural responses, then speech perception in noisy environments is improved, but the number of pulses increases and processing complexity increases
Solution Approach 1:
The patent implements dynamic pulse rate adjustment based on the temporal structure of the input signal. The machine learning model analyzes the fine timing requirements of different sound segments and adapts the pulse rate accordingly, using high rates only when necessary for speech perception while reducing rates during less critical periods, thereby optimizing both performance and complexity
3Measurement precision
If electrode distance from modiolar wall is reduced to target spiral ganglion neurons directly, then spatial encoding accuracy is improved, but hardware complexity and surgical difficulty increase
Solution Approach 1:
The patent compensates for suboptimal electrode positioning by dynamically adjusting stimulation parameters based on real-time neural response feedback. The machine learning system analyzes the actual neural responses and adapts pulse amplitude, duration, and timing to achieve accurate spatial encoding even when electrodes are not optimally positioned near the modiolar wall, thereby maintaining precision without requiring complex hardware modifications
Data Source
AI summary
A system and method for neural implant processing is disclosed. The method includes receiving, at a receiver of a neural implant, an input activation pattern; processing, by a front-end processing algorithm, the input activation pattern to produce a target population firing pattern for one or more neurons; and transforming, by a back-end processing algorithm, the target population firing pattern to a simulation pattern that induces a response with naturalistic timing. The neural implant includes a cochlear implant, a vestibular implant, a retinal vision prostheses, a deep brain stimulator, or a spinal cord stimulator.