Fractal Cognitive Node Hierarchy for Real-Time Explainable Learning
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Solution Overview
Problem
Existing technologies fail to effectively model and reproduce human-like cognitive behaviors and processes in artificial systems, lacking a comprehensive computational framework that can adapt to external data and perform real-time, scalable, and explainable learning and processing.
Innovation Solution
A fractal cognitive computing node (FCN) with inputs for spatial and temporal attention parameters, a memory for storing tuples, and processing units for similarity measures, confidence, coherence, and filtering, connected in hierarchical structures to perform learning and inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current automatic learning and processing mechanisms are used, then basic data processing can be performed, but human-like cognition and scalable abstraction cannot be effectively modeled
Solution Approach 1:
The patent implements a nested hierarchical structure where fractal cognitive computing nodes are organized into clusters, which are further nested within cognitive computing systems. Each level contains simplified versions of the same cognitive processing mechanisms, enabling scalable abstraction from sensory-motor to high attentional levels while maintaining human-like cognition capabilities across all scales.
Solution Approach 2:
The cognitive processing system is segmented into distinct hierarchical levels (sensory-motor, mid-level, high attentional) with specialized fractal cognitive computing nodes at each level. This segmentation allows each segment to handle specific cognitive tasks independently while contributing to overall system cognition, resolving the contradiction between cognitive versatility and system complexity.
2Adaptability or versatility
If hierarchical processing is implemented to model human cognition, then cognitive capabilities improve, but processing speed and real-time performance deteriorate
Solution Approach 1:
The fractal cognitive computing nodes pre-process and organize data into structured representations at lower hierarchical levels before passing to higher levels. Spatial attention mechanisms pre-identify relevant features, and temporal attention mechanisms pre-sequence temporal patterns, enabling faster real-time processing while maintaining comprehensive cognitive capabilities across all hierarchical levels.
3Measurement precision
If detailed spatio-temporal processing is performed to achieve accurate cognition, then measurement precision improves, but computational resources and time consumption increase
Solution Approach 1:
The patent applies local quality by implementing spatial attention mechanisms that selectively process only relevant spatial regions and temporal attention mechanisms that focus on critical time windows. This allows the system to maintain high spatio-temporal measurement precision for attention-selected features while reducing computational resources spent on irrelevant data, thereby improving overall computational efficiency.
Data Source
AI summary
A fractal cognitive computing node, a computer-implemented method for learning procedures, a computational cognition cluster, and a computational cognition architecture are provided. The FCN comprises a first input to receive a first input signal, a first output to provide a first output signal, a second input to receive a second input signal and a third input signal to receive a SA parameter. A memory of the FCN stores a collection of items. A processing unit implements a function that compares a combination of said first and second input signals with the stored collection and calculates a similarity measure for each compared item. The first output signal is calculated as a selection of the compared items having a similarity measure greater than said SA parameter. If the selection is empty, a new item is added to said memory. If not empty, the first output signal is set to said selection.