Stream Neural Networks: Epoch-Free Learning with Persistent Temporal State
2026-02-25 • Neural and Evolutionary Computing
Neural and Evolutionary Computing
AI summaryⓘ
The authors explain that most neural networks learn by repeatedly seeing the same data, which isn’t possible with real-time streams where data can’t be revisited. They propose Stream Neural Networks (StNN) designed to handle data that comes in one direction without the ability to go back. Their system uses special neurons that keep track of information over time and stay stable even as data flows continuously. The authors also provide mathematical guarantees showing why their approach works well with irreversible streams.
neural networksstreaming datairreversible computationstateful neuronstemporal dependenciescontractive operatorepoch-based optimizationlong-horizon coherencephase-space analysis
Authors
Amama Pathan
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.