Quantum microBrain Neural Chip Bypassing Dead Neurons
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Solution Overview
Problem
Current brain monitoring and stimulation technologies are inadequate in detecting and tracking neurological diseases and disorders, as they fail to consider the initial state of neuronal regions and interplay, leading to incomplete data and ineffective treatments, and cannot quantitatively assess the progression of neurological conditions.
Innovation Solution
The Quantum microBrain (QMB) implantable neural chip, which records and stimulates both optical and electrical neural activity, uses a novel nanotechnological approach to bypass dead or non-functional neurons by analyzing waveforms and propagation delays, and includes a digital CPU part and an analog part with optical and electrical sensors, powered by the Brain Code mathematical framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional brain monitoring and stimulation technologies are used, then device simplicity is maintained, but measurement precision and reliability are insufficient due to inability to detect and track neurological disease progression
Solution Approach 1:
The implantable device is segmented into distinct functional modules: optical recording module, electrical recording module, optical stimulation module, electrical stimulation module, and processing module. Each module performs a specific function, allowing the complex system to be built from manageable components while achieving high measurement precision through specialized sensors and multi-modal detection capabilities
Solution Approach 2:
The implantable device integrates multiple functions into a single universal platform that can perform both recording (optical and electrical) and stimulation (optical and electrical) operations. This multi-functional design enables comprehensive neurological monitoring and treatment while tracking disease progression, eliminating the need for multiple separate devices and improving measurement precision through coordinated multi-modal data collection
2Loss of information
If conventional monitoring systems are used, then ease of operation is maintained, but loss of information occurs due to inability to capture initial neuronal state and interplay
Solution Approach 1:
The system continuously monitors neurological signals over extended periods, capturing the initial neuronal state and tracking changes in neuronal interplay throughout disease progression. This continuous multi-modal recording (optical and electrical) ensures no critical information is missed, providing complete data sets for accurate disease tracking and treatment evaluation
Solution Approach 2:
The processing module analyzes recorded signals in real-time and provides feedback to adjust stimulation parameters and recording settings. This feedback mechanism ensures optimal data capture while adapting to changing neurological conditions, preventing information loss due to suboptimal recording parameters and enabling comprehensive tracking of neuronal state changes
3Reliability
If conventional stimulation methods are used, then treatment effectiveness is limited, but device complexity remains low
Solution Approach 1:
The stimulation parameters (amplitude, frequency, duration) are dynamically adjusted based on real-time analysis of recorded neurological signals and disease progression stage. This dynamic adaptation allows the system to optimize treatment efficacy for each patient's specific condition and progression stage, significantly improving treatment reliability compared to static conventional methods while managing complexity through automated control algorithms
Solution Approach 2:
The system employs multiple stimulation parameters that can be independently modified (optical wavelength, electrical amplitude, pulse frequency) to target different neurological conditions and disease stages. This multi-parameter control enables precise treatment customization, improving treatment efficacy through parameter optimization while the integrated processing module manages the complexity of coordinating these multiple parameters
Data Source
AI summary
For example, in an embodiment, an implant device may comprise a plurality of fibers adapted to receive electrical signals, optical signals, or both electrical and optical signals from neural signals of the brain tissue and to transmit signals, to provide stimulation of the brain tissue, and a controller to receive signals, determine direct neural connections by analyzing waveforms and propagation delays of the received signals, recognize at least one dead or non-functional neuron and determine direct neural connections affected by the at least one dead or non-functional neuron, and forward neural signals around the at least one dead or non-functional neuron by recording the received signals from one side of the at least one dead or non-functional neuron and transmitting the recorded signals to stimulate an other side of the at least one dead or non-functional neuron.