Unveiling the role of plasticity rules in reservoir computing

TL;DR

This paper investigates how different plasticity rules enhance reservoir computing by reducing correlations and increasing neuron separation, leading to improved nonlinear time series prediction performance.

Contribution

It demonstrates that combining synaptic and non-synaptic plasticity rules in reservoir computing models yields superior prediction accuracy compared to single plasticity approaches.

Findings

Plasticity reduces pair-wise correlations in the reservoir.

Plasticity increases neurons' ability to distinguish similar inputs.

Optimal performance occurs near the edge of instability.

Abstract

Reservoir Computing (RC) is an appealing approach in Machine Learning that combines the high computational capabilities of Recurrent Neural Networks with a fast and easy training method. Likewise, successful implementation of neuro-inspired plasticity rules into RC artificial networks has boosted the performance of the original models. In this manuscript, we analyze the role that plasticity rules play on the changes that lead to a better performance of RC. To this end, we implement synaptic and non-synaptic plasticity rules in a paradigmatic example of RC model: the Echo State Network. Testing on nonlinear time series prediction tasks, we show evidence that improved performance in all plastic models are linked to a decrease of the pair-wise correlations in the reservoir, as well as a significant increase of individual neurons ability to separate similar inputs in their activity space. Here we provide new insights on this observed improvement through the study of different stages on the plastic learning. From the perspective of the reservoir dynamics, optimal performance is found to occur close to the so-called edge of instability. Our results also show that it is possible to combine different forms of plasticity (namely synaptic and non-synaptic rules) to further improve the performance on prediction tasks, obtaining better results than those achieved with single-plasticity models.