Neural Prosthesis Encoder Mimicking Nerve Cell Input Output Transformation
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Solution Overview
Problem
Current neural prosthetics often fail to accurately mimic the processing of impaired nerve cells, leading to degraded function in subjects as they do not provide a close proxy of the input/output transformation of healthy cells, resulting in suboptimal restoration of motor, auditory, or visual functions.
Innovation Solution
A method and device using encoders that closely mimic the input/output transformation of nerve cells, leveraging experimental data to generate a data-driven phenomenological model, allowing for the bypassing of impaired cells while maintaining near-normal function by simulating the processing of unimpaired cells, using a processor with spatiotemporal filters and nonlinear functions to generate coded outputs that drive healthy cells to produce responses similar to those of unimpaired subjects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a neural prosthetic bypasses impaired cells without accurately mimicking their processing, then the device complexity is reduced, but the functional output quality degrades
Solution Approach 1:
The patent creates a computational model that copies the input/output transformation characteristics of healthy nerve cells. By recording responses from healthy cells and using these responses to train encoder models, the system replicates the functional behavior of healthy cells without physically replacing them, thus achieving accurate signal processing while avoiding the complexity of complete cell replacement
Solution Approach 2:
The system changes the parameters of signal processing by using learned transformation functions derived from healthy cell responses. Instead of attempting to replicate the complete biological complexity of nerve cells, the patent transforms the input signal through parameterized models that capture the essential input/output characteristics, achieving functional equivalence with reduced complexity
2Manufacturing precision
If a neural prosthetic accurately mimics the processing of bypassed cells, then the functional output quality is improved, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary computational layer that mediates between the input signal and the output to healthy cells. This intermediary consists of encoder models trained on healthy cell responses, which act as a bridge to translate inputs into the appropriate transformation pattern without requiring direct replication of complex cellular structures
Solution Approach 2:
The patent substitutes the mechanical/biological system of healthy nerve cells with a computational model. Instead of attempting to physically replicate or replace damaged cells with complex biological structures, the system uses mathematical models and algorithms to replicate the functional transformation, replacing biological complexity with computational processing
3Ease of manufacture
If traditional neural prosthetics are used that do not mimic impaired cell processing, then the ease of manufacture is improved, but the reliability of function restoration deteriorates
Solution Approach 1:
The patent performs preliminary action by recording and analyzing responses from healthy cells before implementing the prosthetic. This preliminary data collection and model training phase allows the system to learn the appropriate transformation patterns in advance, ensuring that when the prosthetic is deployed, it can reliably restore function by applying these pre-learned transformations
Data Source
AI summary
A method improving or restoring neural function in a mammalian subject in need thereof, the method including: using an input receiver to record an input signal generated by a first set of nerve cells; using an encoder unit including a set of encoders to generate a set of coded outputs in response to the input signal; using the encoded outputs to drive an output generator; and using an output generator to activate a second set of nerve cells wherein the second set of nerve cells is separated from the first set of nerve cells by impaired set of signaling cells. In some embodiments, the second set of nerve cells produces a response that is substantially the same as the response in an unimpaired subject.