Sliding-window training on readout layer enhances liquid state machines for temporal prediction.

Abstract

Temporal prediction is essential in scientific and engineering problems, particularly for low-power online inference. Reservoir computing is attractive because a fixed recurrent system maps inputs into a high-dimensional state decoded by a simple readout. Liquid state machines instantiate this idea with recurrent spiking neurons, yielding sparse event-driven states suited to neuromorphic and digital hardware. However, the stepwise protocol trains the readout from an instantaneous state, which can be information-limited when evidence is distributed across time. We improve temporal prediction by providing the readout with a finite temporal context. We concatenate a sliding window of recent reservoir spike states and train on either ridge regression or a transformer readout. On the Nonlinear AutoRegressive Moving Average (NARMA5) benchmark, increasing context length improves both readouts. Median test NRMSE for ridge regression decreases from 2.106 under stepwise decoding to 0.317, an 85% reduction. The transformer readout shows the same trend, decreasing from 0.807 to 0.179, a 78% reduction. The same qualitative trend is further observed on NARMA10 and Mackey Glass prediction tasks. These results demonstrate that exposing recent reservoir evolution at the decoder can substantially reduce the limitations of stepwise decoding while retaining the standard reservoir computing protocol.