arXiv:2508.09925v2 Announce Type: replace-cross
Abstract: We introduce a novel class of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) paradigm, called Residual Reservoir Memory Networks (ResRMNs). ResRMN combines a linear memory reservoir with a non-linear reservoir, where the latter is based on residual orthogonal connections along the temporal dimension for enhanced long-term propagation of the input. The resulting reservoir state dynamics are studied through the lens of linear stability analysis, and we investigate diverse configurations for the temporal residual connections. The proposed approach is empirically assessed on time-series and pixel-level 1-D classification tasks. Our experimental results highlight the advantages of the proposed approach over other conventional RC models.
Feasibility of PIANO-Cog for older adults: A randomised controlled pilot trial exploring changes in cognition and brain microstructure.
Background: Executive functions are a key target of cognitive interventions for older adults due to their central role in daily functioning and maintaining a good

