Retinal Prosthesis Neuromorphic Spike Processing
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Solution Overview
Problem
Current retinal prostheses for treating age-related macular degeneration and retinitis pigmentosa have limited visual recovery effects due to simple image processing and an imbalance between processing speed and power consumption.
Innovation Solution
A retinal prosthesis comprising a capturing assembly, a neuromorphic processor, and a light stimulator that performs bionic full-spike processing, using a spiking recurrent model for predicting ganglion cell responses and stimulating ganglion cells with spike sequences, thereby reducing data size and computation while maintaining high processing speed and low power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If simple image processing is used in retinal prosthesis, then power consumption is reduced, but visual recovery effect is limited
Solution Approach 1:
The patent replaces traditional mechanical/image processing methods with a neuromorphic processing system that mimics biological neural networks. The capturing assembly encodes images as spike sequences, and the neuromorphic processor processes these sequences using spiking neurons and synapses, substituting conventional computational mechanisms with biologically-inspired ones that are more energy-efficient while maintaining or improving visual recovery quality
Solution Approach 2:
The patent changes the fundamental parameter representation from continuous image data to discrete spike sequences with temporal coding. By transforming the data format and processing paradigm, the system achieves lower power consumption through event-driven processing that only activates when changes occur, rather than continuously processing all pixels
2Reliability
If complex image processing is implemented in retinal prosthesis, then visual recovery effect is improved, but processing speed decreases and power consumption increases
Solution Approach 1:
The neuromorphic processor uses periodic spiking activity to process visual information in discrete temporal events rather than continuous processing. The spiking neurons fire at specific intervals based on input strength, creating a rhythmic, event-driven processing flow that maintains high speed while enabling complex computations through temporal patterns
Solution Approach 2:
The processing system is segmented into distinct functional modules: capturing assembly for spike encoding, neuromorphic processor for spike sequence processing, and light stimulator for output. This segmentation allows parallel processing of different visual features simultaneously, improving overall processing speed while maintaining complex processing capabilities
3Reliability
If complex image processing is implemented in retinal prosthesis, then visual recovery effect is improved, but power consumption increases
Solution Approach 1:
The patent replaces traditional mechanical/image processing methods with a neuromorphic processing system that mimics biological neural networks. The capturing assembly encodes images as spike sequences, and the neuromorphic processor processes these sequences using spiking neurons and synapses, substituting conventional computational mechanisms with biologically-inspired ones that are more energy-efficient while maintaining or improving visual recovery quality
Solution Approach 2:
The neuromorphic processor is designed to be self-regulating in its energy consumption. The spiking neurons only consume power when they need to fire based on input thresholds, and the system automatically adapts its processing intensity to the complexity of the visual scene, consuming minimal power for simple scenes and only increasing power usage when complex processing is genuinely needed
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The proposed solution improves visual recovery effects and reduces power consumption, enabling more effective visual perception for patients with retinal prostheses by performing concurrent computation and utilizing sparsity to minimize unnecessary calculations and storage.
Implementation Method 1
An array of photodiodes implanted beneath the retina can convert the near infrared laser into a stimulating current
Implementation Method 2
The neuromorphic processor is configured to predict spike responses of ganglion cells of an implant recipient of the retinal prosthesis according to a preset deep learning algorithm and the spike sequences
Implementation Method 3
The light stimulator is configured to stimulate the ganglion cells of the implant recipient of the retinal prosthesis based on the spike responses of the ganglion cells
Data Source
AI summary
Embodiments of the present disclosure relate to the biomedical technical field, and disclose a retinal prosthesis and a visual perception method based on the retinal prosthesis. The retinal prosthesis includes: a capturing assembly, a neuromorphic processor and a light stimulator. The capturing assembly is configured to capture an external scenario and encode the captured external scenario as spike sequences. The neuromorphic processor is configured to predict spike responses of ganglion cells of an implant recipient of the retinal prosthesis according to a preset deep learning algorithm and the spike sequences. The light stimulator is configured to stimulate the ganglion cells based on the spike responses of the ganglion cells. The retinal prosthesis can further reduce the data size and the amount of computation effectively, so that the power consumption is greatly reduced on the premise of keeping a relatively high processing speed.