Dynamic Neuron Parameter Segmentation for Neural Network Adaptability
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Solution Overview
Problem
Current artificial neural networks face challenges in dynamically defining the dynamics of multiple neurons, particularly in efficiently processing and adapting neuronal data structures for complex applications, where traditional methods are cumbersome or impractical.
Innovation Solution
A method and apparatus for dynamically setting neuron values by processing a data structure comprising parameters, determining segments, neuron types, and boundaries, and generating neural models based on these parameters, allowing for adaptable and efficient neuronal dynamics in artificial neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional computational techniques are used to define neuronal dynamics, then implementation is straightforward, but the system becomes cumbersome and impractical for complex applications
Solution Approach 1:
The patent segments neuronal parameters into distinct categories (e.g., membrane potential parameters, synaptic parameters, ion channel parameters) organized in hierarchical data structures. This segmentation allows the system to handle complex neuronal dynamics by processing individual parameter groups independently, reducing overall computational complexity while maintaining high adaptability.
Solution Approach 2:
The patent implements dynamic parameter assignment where neuronal model parameters can be modified at runtime based on neuron type specifications and application requirements. The system dynamically loads and configures parameter sets without requiring complete model redefinition, enabling adaptable neuronal dynamics while avoiding the cumulative complexity of static traditional methods.
2Productivity
If fixed neuronal parameter sets are used, then processing is simple, but the system cannot adapt to different neuron types and complex applications
Solution Approach 1:
The patent creates a universal neuronal parameter framework that can accommodate multiple neuron types (e.g., cortical neurons, hippocampal neurons, motor neurons) through a common data structure template. This universal structure allows the system to efficiently process different neuron types by simply swapping parameter values rather than implementing separate processing logic for each type, thereby maintaining high productivity while achieving broad adaptability.
Solution Approach 2:
The patent enables efficient parameter changes by organizing neuronal parameters in configurable data structures that can be dynamically instantiated for different neuron types. The system changes parameters through controlled variable assignment and data structure instantiation rather than fundamental model restructuring, allowing rapid adaptation to different neuron types while preserving computational efficiency.
Data Source
AI summary
A method for dynamically setting a neuron value processes a data structure including a set of parameters for a neuron model and determines a number of segments defined in the set of parameters. The method also includes determining a number of neuron types defined in the set of parameters and determining at least one boundary for a first segment.