Task-specific node pruning enhances computational efficiency of reservoir computing networks
Publication
Yadav et al. Chaos, 35, 083123 (2025)
In this work, we introduced a task-driven network pruning framework, starting from a large reservoir and iteratively removing nodes while optimizing performance. Results show:
- Pruned networks retain—often improve—accuracy
- Structural metrics such as spectral radius, out-degree, and input–readout asymmetry reorganize systematically
- Pruning reveals functionally critical subnetworks within Erdős-Rényi random networks.
Read full article here: M. Yadav and M. Stender, “Task-specific node pruning enhances computational efficiency of reservoir computing networks,” Chaos 35, 083123 (2025)
