Neurosynaptic Computer System Hierarchical Topological Pattern Processing
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Solution Overview
Problem
Current methods for constructing and operating recurrent artificial neural networks lack the ability to efficiently read and process complex topological elements and computational results, limiting their effectiveness in simulating brain-like cognitive algorithms and decision-making processes.
Innovation Solution
The development of a neurosynaptic computer system that utilizes a spiking recurrent neural network to identify and encode complex topological elements, generate neural codes, and decode them into target outputs, mimicking the brain's hierarchical decision-making processes and enabling efficient computation, storage, and communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current methods for constructing and operating recurrent artificial neural networks are used, then the network structure can be implemented, but the ability to efficiently read and process complex topological elements and computational results is limited
Solution Approach 1:
The patent segments complex topological elements into hierarchical levels of simpler sub-elements. The system identifies and processes topological elements by breaking them down into constituent parts, enabling efficient reading and processing of complex structures through systematic decomposition into manageable components that can be handled sequentially
Solution Approach 2:
The patent introduces a hierarchical dimension to organize topological elements by complexity level. By adding this dimensional structure, the system can process elements from simple to complex in a structured manner, transforming the processing challenge from handling monolithic complex elements to managing a multi-level hierarchy where each level builds upon the previous one
2Productivity
If the neurosynaptic computer system processes diverse data types and executes complex mathematical operations, then computing power is enhanced, but the system complexity increases
Solution Approach 1:
The patent implements a universal neurosynaptic computer system that can process multiple data types (sensory, motor, cognitive) and execute various mathematical operations through a single integrated architecture. The system uses unified neural code representations and common processing mechanisms that handle diverse computational tasks, eliminating the need for separate specialized systems for each function
Solution Approach 2:
The patent introduces neural code as an intermediary representation layer between input data and processing operations. This neural code serves as a universal mediator that translates diverse data types into a common format that the neurosynaptic system can process efficiently, simplifying the interface between varied inputs and the core computational engine
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a set of elements that form a cognitive process in a recurrent neural network. The method comprises identifying activity in the recurrent neural network that comports with relatively simple topological patterns, using the identified relatively simple topological patterns as a constraint to identify relatively more complex topological patterns of activity in the recurrent neural network, using the identified relatively more complex topological patterns as a constraint to identify relatively still more complex topological patterns of activity in the recurrent neural network, and outputting identifications of the topological patterns of activity that have occurred in the recurrent neural network.