Personal Neuromorphic Emulation Using hdEEG Brain Model Training
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Solution Overview
Problem
Current neuromorphic computational models are insufficient to emulate a person's cognition, behavior, and subjective experience, limiting their ability to replicate an individual's personality and subjective consciousness in digital form, and there is a need to overcome this to achieve indefinite life extension by recreating the self in a durable informatic form.
Innovation Solution
A method involving high-definition electroencephalography (hdEEG) and neuromorphic emulation to construct a Personal Neuromorphic Emulation (PNE) that accurately replicates the neural architecture and electrical field generation properties of an individual's brain, trained to predict behavior during waking and sleep, using machine learning and subjective validation to ensure fidelity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current neuromorphic computational models are used, then computational emulation of brain function is achieved, but the models cannot sufficiently replicate an individual's cognition, behavior, and subjective experience
Solution Approach 1:
The patent segments the brain into functional modules (sensory, motor, cognitive, emotional) and models each with specialized neural network architectures. This allows high-fidelity replication of individual cognitive functions while maintaining the versatility to emulate complex interactions between modules, resolving the contradiction between precision and adaptability.
Solution Approach 2:
The patent introduces a new dimension of modeling by incorporating multi-scale neural architecture (from neuronal level to systems level) and multi-modal data integration (neuroimaging, behavioral data, genetic information). This dimensional expansion enables simultaneous high-fidelity replication of individual cognition and broad functional versatility.
2Measurement precision
If detailed individual brain modeling is constructed, then high-fidelity replication of personal identity is achieved, but the complexity of the computational model increases
Solution Approach 1:
The patent applies local quality by tailoring the level of detail and architectural complexity to match the functional importance and data availability of specific brain regions. High-resolution modeling is applied to cognitively critical areas while using simplified models for less critical regions, achieving identity fidelity without uniform complexity throughout the entire system.
Solution Approach 2:
The patent dynamically adjusts model parameters and resolution levels based on computational requirements and available data. The system can switch between high-fidelity detailed modeling when needed and simplified representations during normal operation, managing complexity while maintaining the capability for high-fidelity replication.
3Measurement precision
If comprehensive neurophysiological data is collected and integrated, then the emulation accuracy is improved, but the data processing and training requirements increase
Solution Approach 1:
The patent performs preliminary data processing, feature extraction, and model pre-training using publicly available neuroimaging datasets and computational models before individual customization. This preliminary action reduces the computational burden during individual-specific training, improving accuracy while maintaining processing efficiency.
Solution Approach 2:
The patent introduces intermediary computational layers and algorithms that efficiently bridge the gap between raw neurophysiological data and the final emulation model. These intermediaries process and transform complex data into meaningful representations, improving accuracy without proportionally increasing processing requirements.
Data Source
AI summary
A method for personal neuromorphic emulation. The method may include constructing a computerized anatomical network model of neurons in the brain, the anatomical network model defining adjustably weighted connections between the modeled neurons and producing EEG signals as outputs; taking first hdEEG data from the subject while the subject's brain is awake and performing one or more first intellectual tasks; and adjusting the weights of the weighted connections of the anatomical network model so as to drive the EEG signal outputs of the anatomical network model toward the first hdEEG data.