Brain-on-a-chip Control System for Neural Response Mapping
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Solution Overview
Problem
Existing brain-on-a-chip intelligence complexes face challenges with high delay times and poor control effects due to their complex nonlinear nature, making it difficult to establish a stable output mapping relationship for neural responses, and require long training times with limited robustness in closed-loop control strategies.
Innovation Solution
A brain-on-a-chip intelligence complex control system is developed, incorporating a brain-on-a-chip basic module and an information interaction and training module with data preprocessing, neural signal decoding, reward and punishment control, task control, and mapping relationship models, enabling efficient and flexible closed-loop control, optimized through deep learning, to achieve autonomous and controllable neural responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If independent control of two environments (virtual and real) is used, then control flexibility is improved, but delay time increases and control effect deteriorates
Solution Approach 1:
The patent merges the virtual environment and real environment into a unified dual-environment system where the brain-on-a-chip simultaneously interacts with both. The MEA recording interface serves as a common platform that integrates virtual reality stimulation signals and real-world sensory feedback, eliminating the need for separate independent control systems and reducing transmission delays.
Solution Approach 2:
The patent implements a closed-loop feedback mechanism where sensory feedback from the real environment is continuously recorded by the MEA interface and fed back to the brain-on-a-chip, which then adjusts control signals to the virtual environment. This real-time feedback loop reduces delay by creating a continuous interaction cycle between virtual and real environments.
2Adaptability or versatility
If brain-on-a-chip is used as a complex nonlinear system, then biological intelligence is improved, but stability of output mapping relationship deteriorates
Solution Approach 1:
The patent employs deep learning optimization control that dynamically adjusts stimulation parameters (voltage, frequency, pulse width) and recording parameters based on the brain-on-a-chip's response characteristics. The system continuously learns and adapts the mapping relationship between stimulation inputs and neural outputs, stabilizing the otherwise unstable nonlinear mapping through data-driven parameter optimization.
Solution Approach 2:
The patent implements a training phase before actual control tasks where the brain-on-a-chip is pre-trained to establish stable mapping relationships. During this preliminary training, the system collects data and optimizes the neural network weights to predict neural responses accurately, creating a stable foundation for subsequent control operations.
3Ease of operation
If traditional closed-loop control strategy is used, then control capability is improved, but training time increases and robustness deteriorates
Solution Approach 1:
The patent replaces traditional mechanical closed-loop control strategies with a deep learning-based neural network model. Instead of using conventional control algorithms that require extensive trial-and-error training, the system uses machine learning models that can be trained more efficiently and then deployed for rapid control, reducing overall training time while maintaining control capability.
Solution Approach 2:
The patent separates the training process into a preliminary phase where the deep learning model is trained offline on collected neural data, and then deploys the trained model for rapid online control. This preliminary training of the neural network allows the system to achieve robust control capability without requiring extensive real-time training during actual operations.
Data Source
AI summary
Disclosed is a brain-on-a-chip intelligence complex control system comprising a basic module and an information interaction and training module. The latter integrates a neural signal decoding unit, reward and punishment control unit, task control model, and mapping relationship model. The neural signal decoding unit transforms neural response data into external device-recognizable control instructions. Employed for controlling the external device, the task control model creates a future target control instruction based on task feedback, retrieving the corresponding neural response. The mapping relationship model establishes connections between the brain-on-a-chip's stimulation sequence and neural responses. Calculating task completion, the reward and punishment control unit generates a reward or punishment signal based on task feedback, applying it to the brain-on-a-chip basic module. This innovative brain-on-a-chip intelligence complex enhances control and training capabilities through integrated modules.