Neural Processing Graphs for Brain-Like Cognition on Standard Hardware
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Solution Overview
Problem
Existing systems fail to emulate brain function, interconnection, and operation at a functional abstraction level similar to biological brains, requiring excessive computing power and lacking integration of cognitive functions on widely available computing resources.
Innovation Solution
A system comprising neural processing graphs that model biological brain structure and operation, allowing scalable performance on various computing platforms, with customizable modules and the ability to incorporate external system functions and share learned information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems simulate biological synapses and neurons at fine biochemical detail levels, then they can model individual neuron and synapse operations, but they require large amounts of computing power beyond that available in widely available computers
Solution Approach 1:
The system segments the biological brain model into discrete functional components (neurons, synapses, neural assemblies, cognitive functions) that can be simulated at appropriate levels of detail. This allows selective simulation of fine biochemical details only where necessary, while using higher-level abstractions elsewhere, thereby reducing overall computing power requirements while maintaining biochemical accuracy where needed.
Solution Approach 2:
The system introduces a new dimension of abstraction by organizing neural components into hierarchical levels (molecular → cellular → assembly → cognitive function). This multi-dimensional organization allows the system to switch between fine biochemical detail and high-level cognitive abstraction depending on the specific computational task, resolving the contradiction between detail accuracy and computing power requirements.
2Productivity
If existing systems implement high-level cognitive functions using system architectures very different from biological brains, then they can compute cognitive capabilities on available hardware, but it becomes difficult to use these systems to understand biological brain operation or use biological observations to inspire artificial system design
Solution Approach 1:
The system creates a universal computational framework that can operate at multiple levels simultaneously - from fine biochemical simulations to high-level cognitive functions. This multi-functional architecture allows the same system to both compute cognitive capabilities and maintain biological fidelity, enabling dual purposes of cognitive computation and biological understanding without requiring separate systems.
Solution Approach 2:
The system introduces intermediate representation layers that bridge biological neural structures and computational cognitive functions. These intermediate layers (neural assemblies, functional modules) serve as mediators that preserve biological architectural insights while enabling efficient cognitive computation, thus resolving the contradiction between biological fidelity and computational efficiency.
3Measurement precision
If systems focus on fine levels of biochemical detail below the functional level, then they can model individual neuron and synapse operation, but they require large amounts of computing power beyond that available in widely available computers
Solution Approach 1:
The system dynamically adjusts the level of simulation detail based on the computational context and requirements. For tasks requiring high neural operation detail, the system activates fine-grained biochemical simulations. For tasks requiring broader cognitive patterns, the system transitions to higher-level abstractions. This dynamic adaptation reduces computational resource requirements while maintaining neural operation detail where necessary.
Data Source
AI summary
Provided herein is a system for creating, modifying, deploying and running intelligent systems by combining and customizing the function and operation of reusable component modules arranged into neural processing graphs which direct the flow of signals among the modules, analogous in part to biological brain structure and operation as compositions of variations on functional components and subassemblies.