Whole-Brain Emulation With Brain-State Prediction for Behavior Modeling
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Solution Overview
Problem
Current brain mapping and simulation technologies primarily focus on structural and functional mapping of neural connections, lacking comprehensive emulation of whole brain and organism functions, particularly in generating accurate behavioral models.
Innovation Solution
A system and method for generating and emulating whole brain and organism models using machine learning, incorporating training datasets, encoder and decoder modules, and next-state prediction models to simulate brain states and behaviors, enabling interaction with both virtual and physical environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If structural and functional mapping of neural connections is performed, then understanding of brain architecture is improved, but comprehensive emulation of whole brain functions is not achieved
Solution Approach 1:
The system segments brain emulation into multiple components: structural mapping module, functional mapping module, and behavioral emulation module. Each module processes specific aspects of brain data independently before integrating results, allowing comprehensive emulation while maintaining precision in individual mapping tasks
Solution Approach 2:
The patent implements nested emulation levels where structural maps are embedded within functional maps, which are in turn embedded within whole-brain behavioral models. This nested architecture allows detailed structural precision to be preserved while building up to comprehensive whole-brain emulation capabilities
2Measurement precision
If machine learning models are trained on brain data, then behavioral prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary processing of brain data including normalization, feature extraction, and pre-training of base models before final behavioral prediction tasks. This preliminary action reduces the complexity of subsequent training while maintaining or improving prediction accuracy
Solution Approach 2:
The patent implements dynamic model architecture that adapts computational complexity based on task requirements. The system can switch between simplified and detailed models, adjusting computational resources dynamically to balance accuracy and complexity for different behavioral prediction tasks
Data Source
AI summary
Systems and methods to allow for generating a simulated humanoid. The simulated humanoid operates in a simulation space with simulated humanoid having a whole brain emulation module. The whole brain emulation module includes a virtual stimuli input module that is configured to receive or capture stimuli input data. The whole brain emulation module includes an encoder that is configured to translate the stimuli input data into a simulated functional neurodata frame. The whole brain emulation module also includes a brain state module that maintains a current brain state corresponding to a current functional neurodata frame of the simulated humanoid.