Stream Neural Networks: Epoch-Free Learning with Persistent Temporal State

2026-02-25

AI Summary

This work addresses the fundamental limitation of conventional neural networks, which rely on repeated data access and epoch-based training, thereby losing long-term consistency in irreversible data streams and degenerating into reactive filters devoid of temporal memory. To overcome this, the paper introduces Streaming Neural Networks (StNN), equipped with a stream-native execution algorithm (SNA) and streaming neurons that maintain persistent temporal states, enabling continuous, epoch-free learning. The study establishes the first minimal computational foundation for neural processing of irreversible streaming data, theoretically proving that the network’s state dynamics exhibit boundedness and contractivity. Through phase-space analysis and temporal modeling, the authors further demonstrate the stability and effectiveness of StNN in long-horizon stream processing tasks.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Data Mining & Knowledge Management: Data Stream Mining

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Computational social science

Abstract

Most contemporary neural learning systems rely on epoch-based optimization and repeated access to historical data, implicitly assuming reversible computation. In contrast, real-world environments often present information as irreversible streams, where inputs cannot be replayed or revisited. Under such conditions, conventional architectures degrade into reactive filters lacking long-horizon coherence. This paper introduces Stream Neural Networks (StNN), an execution paradigm designed for irreversible input streams. StNN operates through a stream-native execution algorithm, the Stream Network Algorithm (SNA), whose fundamental unit is the stream neuron. Each stream neuron maintains a persistent temporal state that evolves continuously across inputs. We formally establish three structural guarantees: (1) stateless mappings collapse under irreversibility and cannot encode temporal dependencies; (2) persistent state dynamics remain bounded under mild activation constraints; and (3) the state transition operator is contractive for λ < 1, ensuring stable long-horizon execution. Empirical phase-space analysis and continuous tracking experiments validate these theoretical results. The execution principles introduced in this work define a minimal substrate for neural computation under irreversible streaming constraints.

Problem

• irreversible streams
• temporal state
• epoch-free learning
• long-horizon coherence
• streaming constraints

Innovation

• Stream Neural Networks
• irreversible streaming
• persistent temporal state
• epoch-free learning
• contractive state transition