Transmitter-Threshold Neural Model for Adaptive Low-Complexity Automation
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Solution Overview
Problem
Existing neurocomputational models are susceptible to underfitting, leading to high complexity and resource consumption, and struggle to adapt to dynamic environments due to rigidity and difficulty in accounting for environmental variability.
Innovation Solution
A trivial transmitter model comprising source, interneuron, and motor units that dynamically adjust transmitter quantities to automate processes, using a computing system to manage and configure neural networks efficiently, allowing for plasticity events to maintain balance and adapt to environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If known neurocomputational models are used to achieve high performance, then performance is improved, but device complexity and resource consumption increase significantly
Solution Approach 1:
The patent extracts and eliminates unnecessary computational components from complex neurocomputational models, retaining only the essential transmitter-receiver mechanisms needed for basic neural processing. This extraction reduces model complexity while preserving core functional capabilities, directly addressing the contradiction between performance and complexity.
Solution Approach 2:
The patent employs simplified, lightweight transmitter and receiver components that can be easily instantiated and discarded, replacing heavy computational structures. These simple objects achieve the necessary computational functionality with minimal resource consumption, resolving the trade-off between performance and resource requirements.
2Reliability
If known neurocomputational models are used to achieve high performance, then performance is improved, but energy consumption increases substantially
Solution Approach 1:
The patent removes energy-intensive computational operations from complex neural models, retaining only the essential signal transmission and reception mechanisms. This extraction dramatically reduces energy consumption while maintaining the core processing capabilities needed for neural network functionality.
Solution Approach 2:
The patent uses low-cost, low-energy transmitter and receiver components that consume minimal power compared to traditional neural network processors. These simple objects achieve the necessary computational tasks with substantially reduced energy consumption, directly addressing the performance-energy contradiction.
3Productivity
If known neurocomputational models are used, then processing capability is achieved, but adaptability to dynamic environments is reduced due to rigidity
Solution Approach 1:
The patent implements dynamic adjustability in the transmitter-receiver system, allowing connection strengths and transmission parameters to be modified in real-time based on environmental conditions. This dynamic capability enables the system to adapt to changing environments while maintaining processing functionality, resolving the rigidity problem.
Solution Approach 2:
The patent enables continuous adjustment of key parameters such as transmitter release rates, receiver sensitivity thresholds, and connection weights. These parameter changes allow the system to adapt to different environmental conditions and task requirements, directly improving adaptability while preserving processing capability.
Data Source
AI summary
A trivial transmitter model enables processes to be automated and/or networks to be managed efficiently and reliably. The trivial transmitter model includes one or more interneuron units that receive a quantity of transmitters including at least a portion of a first quantity of a source transmitter released by a source unit, determine a second quantity of an interneuron transmitter based on the quantity of received transmitters including at least the portion of the first quantity of the source transmitter, and release the second quantity of the interneuron transmitter, wherein the second quantity of the interneuron transmitter is configured to perform an action upon satisfying a predetermined threshold.