rnn-hidden-state-sequential-processing

IN premise — entries/2026/06/21/wiki-Recurrent_neural_network-chunk-1.md

Created 2026-06-21T09:55:52+00:00

Recurrent neural networks process sequential data by maintaining a hidden state updated at each time step based on current input and the previous hidden state, enabling temporal dependency capture.

Dependents

These beliefs depend on this one:

• IN rnn-theoretical-power-exceeded-practical-utility — RNNs possess strong theoretical computational power — Turing-completeness with rational weights (Siegelmann & Sontag 1994) and context-sensitive language recognition via LSTM that surpasses what HMM-based models achieve — while their sequential hidden-state processing, which updates state at each time step, represents an inherent constraint on parallelism.