Hyperdimensional Computing with Neural Symbolic Integration

Abstract

This paper proposes a novel approach to artificial intelligence by integrating hyperdimensional computing (HDC) with neural symbolic computation. The core idea is to leverage the strengths of both paradigms: HDC's efficiency in high-dimensional pattern recognition and neural networks' ability to generate and manipulate symbolic representations. We outline a hybrid architecture where HDC acts as a fast, distributed pattern detector, while a neural network constructs and refines symbolic representations of the detected patterns. These symbolic representations then guide the HDC computations, creating a feedback loop that enhances both recognition accuracy and interpretability. The paper details the proposed architecture, focusing on the interaction between the two components and the mechanisms for knowledge transfer. We argue that this integration represents a significant step towards more robust and explainable AI systems, and demonstrate a potential path for achieving emergent intelligence through the synergistic combination of these computational approaches. The architecture is designed to minimize the computational burden of symbolic processing while maximizing the pattern recognition capabilities of HDC. The system's ability to translate complex patterns into a symbolic form allows for reasoning and deduction, ultimately leading to more sophisticated cognitive processes.