Neuro-Symbolic Computation via Predictive Coding

Abstract

This paper explores the integration of predictive coding, a core mechanism in neuroscience, with symbolic computation to create a novel approach to artificial intelligence reasoning. The central argument is that by combining the pattern recognition capabilities of neural networks with the rule-based reasoning of symbolic systems, we can develop more robust and efficient AI systems. The proposed architecture utilizes neural networks to predict future inputs within a sequence, while symbolic systems generate rules to correct these predictions, establishing a continuous feedback loop. This approach offers a more grounded and explainable form of AI reasoning, addressing limitations of purely neural or purely symbolic systems. We detail the core components of this hybrid system, focusing on the dynamics of prediction and correction, and outline potential avenues for future research. The goal is to move beyond current AI paradigms by creating systems that learn and reason in a manner more aligned with how the human brain operates. The key contribution lies in the formalized process of integrating predictive signals with symbolic constraints, leading to a dynamic system capable of adapting to complex and noisy environments.