Reservoir Computing based on Quenched Chaos

Reservoir computing (RC) is a brain-inspired computing framework that employs a transient dynamical system whose reaction to an input signal is transformed to a target output. One of the central problems in RC is to find a reliable reservoir with a large criticality, since computing performance of a reservoir is maximized near the phase transition. In this work, we propose a continuous reservoir that utilizes transient dynamics of coupled chaotic oscillators in a critical regime where sudden amplitude death occurs. This "explosive death" not only brings the system a large criticality which provides a variety of orbits for computing, but also stabilizes them which otherwise diverge soon in chaotic units. The proposed framework shows better results in tasks for signal reconstructions than RC based on explosive synchronization of regular phase oscillators. We also show that the information capacity of the reservoirs can be used as a predictive measure for computational capability of a reservoir at a critical point. (c) 2020 Elsevier Ltd. All rights reserved

Reservoir computing based on explosive synchronization and quenched chaos

Department of Mathematical SciencesSynchronous oscillations in neuronal ensembles have been proposed to provide a neural basis for the information processes in the brain. In this work, we present a reservoir computing(RC), a highly e???cient bio-inspired architecture, based on oscillator synchronization in a critical regime. The algorithm uses the high-dimensional transient dynamics perturbed by an input and translates it into proper output stream. One of the bene???ts of adopting coupled phase oscillators as neuromorphic elements is that the synchrony among oscillators can be ???nely tuned at arti???cial state. Especially near a critical state, the marginally synchronized oscillators operate with high e???ciency and maintain better computing performances. We also show that explosive synchronization that is induced from speci???c neuronal connectivity produces more improved and stable outputs. This work provides a systematic way to encode computing in a large size coupled oscillator, which may be useful in designing neuromorphic devices. Furthermore we develop RC based on ???explosive death??? of chaos. The proposed reservoir utilizes transient dynamics of coupled chaotic oscillators in a critical regime where sudden amplitude death occurs. Explosive death not only brings the system a large criticality which provides a variety of orbits for computing, but also stabilizes them which otherwise diverge soon in chaotic units. The proposed framework shows better results in tasks for signal reconstructions than RC based on explosive synchronization of regular phase oscillators. We also show that the information capacity of the reservoirs at a critical point can be used as a predictive measure for computational capability of a reservoir.clos

Dynamical reservoir properties as network effects

Abstract. It has been proposed that chaos can serve as a reservoir providing an infinite number of dynamical states [1, 2, 3, 4, 5]. These can be interpreted as different behaviors, search actions or computational states which are selectively adequate for different tasks. The high flexibility of chaotic regimes has been noted, as well as other advantages over regular regimes. However, the model neurons used to demonstrate these ideas could be criticized as lacking physical or biological realism. In the present paper we show that the same kind of rich behavior displayed by the toy models can be found with a more realistic neural model [6]. Furthermore, much of the complex behavior arises from network properties often overlooked in the literature. 1 Chaotic spatiotemporal neural chaos and its use Following the discovery of putative chaotic regimes in electrical signals from the brain, and much scientific speculation as to the possible roles of chaos in cognition, actual computational models were proposed [1, 2, 3, 4, 5]. These model