Entropy-Based Neural Channel Compression for Low-Heat Implants
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Solution Overview
Problem
Brain machine interfaces generate excessive heat and consume significant power due to the use of numerous sensors, potentially damaging brain tissue and reducing precision in identifying intended actions.
Innovation Solution
A neural implant with a multiplexer and processor embedded in the brain applies a compression algorithm to combine neural activity data based on entropy values, reducing the number of channels and data transmission, thereby minimizing heat generation and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of sensors are embedded in the brain to detect brain activity across a wide area, then measurement precision of neural activity is improved, but heat generation and power consumption increase disproportionately
Solution Approach 1:
The patent combines multiple sensor channels into fewer aggregated channels using entropy-based compression algorithms. Sensors that detect correlated neural activity are merged into single output channels, reducing the total number of active sensors and thereby decreasing heat generation while preserving essential neural information.
Solution Approach 2:
The patent transforms the data representation by changing from individual sensor readings to entropy-based compressed representations. By calculating entropy values and using them to guide channel aggregation, the system changes the parameter space from raw sensor data to compressed neural activity patterns, reducing thermal load while maintaining detection precision.
2Measurement precision
If a large number of sensors are embedded in the brain to detect brain activity across a wide area, then measurement precision of neural activity is improved, but power consumption increases disproportionately
Solution Approach 1:
The patent merges multiple high-power sensor channels into fewer aggregated channels through entropy-based compression. By combining sensors that detect correlated activity, the system reduces the number of active high-power components while maintaining the ability to detect and interpret neural activity with sufficient precision.
Solution Approach 2:
The patent extracts only the most essential neural information by removing redundant data from correlated sensor channels. The entropy-based algorithm identifies and extracts unique information from each channel while discarding redundant correlated signals, thereby reducing power consumption without sacrificing measurement precision.
3Measurement precision
If data from multiple sensor channels is transmitted without compression, then measurement precision is maintained, but heat generation and power consumption increase
Solution Approach 1:
The patent merges multiple data channels into fewer aggregated channels before transmission using entropy-based compression. By combining correlated channels and transmitting only the essential aggregated information, the system maintains neural activity accuracy while significantly reducing the data transmission load and associated heat generation.
Solution Approach 2:
The patent changes the data transmission parameter from raw uncompressed sensor readings to entropy-compressed representations. This transformation reduces the volume of data requiring transmission while preserving essential neural information, thereby decreasing the thermal load from wireless or wired data transmission.
Data Source
AI summary
In some embodiments, there is provided a system, which comprises at least one computer that is communicatively coupled with at least one sensor. The at least one sensor is embedded in a brain and configured to detect a plurality of activity data associated with neurons of the brain, implement a compression algorithm on the plurality of activity data by comparing a first entropy value associated with a first set of data points of the plurality of activity data with a second entropy value associated with a second set of data points of the plurality of activity data, and combining the first set of data points responsive to determining that the first entropy value is lower than the second entropy value, and transmit the combination of the first set of data points to the at least one computer.