Pattern-Based Data Representation: A Foundational Concept for Post-Neural Computing Systems

Pattern-Based Data Representation: A Foundational Concept for Post-Neural Computing Systems Author: Anurag Dongare (Independent Researcher, India) Abstract: This work presents the concept of the Pattern-Based Data Representation System (PBDRS) — a proposed post-neural computing paradigm where systems store and process patterns of relationships rather than discrete data values. The framework envisions computation as an adaptive process of recognizing, composing, and evolving structural patterns that describe underlying information. Unlike traditional data-centric systems, PBDRS emphasizes pattern abstraction as the core unit of computation, enabling explainable reasoning, dynamic adaptation, and massive compression. The concept unifies ideas from symbolic AI, semantic embeddings, and neural pattern evolution into a single model of self-evolving intelligence. Potential applications include: Interpretable and self-learning AI models Grammar-based data compression and pattern databases Self-optimizing programming languages Predictive and adaptive cybersecurity systems This paper introduces the theoretical foundation of pattern-centric computing and invites open collaboration toward building systems that learn and evolve by recognizing the patterns that make up knowledge itself. Keywords: Pattern-based computing, symbolic AI, data representation, neural-symbolic systems, programming language design, post-neural architecture. License: Creative Commons Attribution 4.0 (CC BY 4.0).