rnn-task-degradation-analysis

This framework analyzes graceful degradation in recurrent neural networks by training multiple initializations and measuring performance under connection pruning, scaling, and timing variations. It quantifies weight similarity and spectral properties to evaluate robustness.

Different weight initializations lead to distinct dynamical solutions that achieve the same temporal task. This framework evaluates how these diverse solutions influence performance degradation when network size decreases or time intervals increase.

Yes, you can measure weight similarity and spectral properties across multiple RNN initializations using the provided Python-based workflow. It trains several networks and analyzes convergence dynamics alongside solution diversity metrics.

The best way to evaluate RNN performance degradation under varying time intervals is to train multiple initializations and apply this framework's degradation analysis. It systematically measures network robustness under timing variations.

You need a Python environment to study neural dynamics and convergence in recurrent networks. This framework implements a Python-based workflow for training, analysis, and visualization of diversity and degradation metrics without external dependencies.