Neural Network Activity Encoding via Topological Structures
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Solution Overview
Problem
Current encoding and decoding techniques for neural networks face challenges in efficiently processing and representing complex patterns of activity, particularly in distinguishing decision moments and encoding signals in recurrent neural networks, which affects the accuracy and efficiency of information processing and storage.
Innovation Solution
The implementation of a device and method that utilizes a recurrent neural network to identify decision moments by characterizing activity patterns, encoding signals based on complexity, and using topological structures to represent activity in neural networks, allowing for the encoding and decoding of information in a way that captures distinguishable complexity and ordering within the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional encoding and decoding techniques are used for neural networks, then the implementation is simpler, but the ability to distinguish decision moments and capture complexity in activity patterns is insufficient
Solution Approach 1:
The patent segments the continuous activity patterns in neural networks into discrete topological structures (simplices, cavities, etc.) that can be individually encoded. By dividing the complex activity patterns into identifiable topological components, the system can precisely distinguish decision moments while maintaining a structured encoding approach
Solution Approach 2:
The patent introduces topological dimensions (homology groups, Betti numbers) to encode activity patterns. Instead of using traditional temporal or spatial encoding, the system transforms activity patterns into topological features across multiple dimensions, enabling precise characterization of decision moments through topological invariants
2Loss of information
If topological structures are used to represent activity patterns, then the encoding captures complexity and ordering, but the processing and computation become more complex
Solution Approach 1:
The patent creates topological copies or representations of neural network activity patterns. Instead of directly processing the raw high-dimensional activity data, the system generates simplified topological models (simplicial complexes, persistence diagrams) that capture the essential complexity and ordering while being computationally tractable
Solution Approach 2:
The patent transforms activity patterns by changing the parameter space from raw neural activations to topological invariants (Betti numbers, persistence intervals). This parameter transformation preserves the essential complexity information while converting it into a form that is more amenable to systematic processing and analysis
3Measurement precision
If the encoding scheme captures detailed complexity of activity patterns, then the information representation is more accurate, but the transmission and storage requirements increase
Solution Approach 1:
The patent extracts only the essential topological features (persistent homology classes, significant cavities, key simplices) from the full activity patterns. By taking out only the most informative topological components rather than encoding all activity details, the system achieves accurate representation with reduced data volume for transmission and storage
Data Source
AI summary
A method that is implemented by one or more data processing devices can include receiving a training set that includes a plurality of representations of topological structures in patterns of activity in a source neural network and training a neural network using the representations either as an input to the neural network or as a target answer vector. The activity is responsive to an input into the source neural network.