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 computation reservoir emerging within a biological model network

Chaos in dynamical systems potentially provides many different dynamical states arising from a single attractor. We call this the reservoir property and give here a precise meaning to two aspects of such property. In both cases, the high flexibility of chaos comes into play, as compared to more regular regimes. In this article, we especially focus on the fact that chaotic attractors are known to possess an infinite number of embedded Unstable Periodic Orbits. In brain modeling, or for the purpose of suggesting computational devices that could take advantage of chaos, the different embedded dynamical states can be interpreted as different behaviors or computational modes suitable for particular tasks. Previously we proposed a rather abstract neural network model that mimicked cortex to some extent but where biological realism was not the major concern. In the present paper we show that the same potential for computation can be displayed by a more realistic neural model. The latter features spatiotemporal chaos of a type thus far only found in more “artificial ” models. We also note that certain network-related properties, previously overlooked, turn out to be essential for the generation of complex behavior