We designed a neuromorphic single-electron oscillator network as an inhibitory neural network model that performs pulse-density modulation (PDM) [1]. The use of single-electron oscillator networks has been proposed for implementing functions of neural networks [2] and reaction-diffusion systems [3]. Single-electron circuits are being studied for use in next-generation LSI devices. We designed a...

We present a simple algorithm for detecting oscillatory behavior in discrete data. The data is used as an input driving force acting on a set of simulated damped oscillators. By monitoring the energy of the simulated oscillators, we can detect oscillatory behavior in data. In application to in vivo deep brain basal ganglia recordings, we found sharp peaks in the spectrum at 20 and 70 Hz. The al...

Neural networks have proven to be efficient for a number of practical applications ranging from image recognition identifying phase transitions in quantum physics models. In this paper, we investigate the application neural state classification single-shot measurement. We use dispersive readout superconducting transmon circuit demonstrate an increase assignment fidelity both two and three class...

We propose a simple cellular automata model for discrete-time pulse-coupled oscillators and study its network behavior. Given a finite simple graph and an integer n ≥ 3, each vertex will be considered as an identical oscillator of period n with the following weak coupling along the edges: any oscillator with a particular "blinking" state will postpone the update of its neighbors whose states ar...

This paper proposes a control algorithm based on adaptive dynamic programming to solve the infinite-horizon optimal control problem for known deterministic nonlinear systems with saturating actuators and non-quadratic cost functionals. The algorithm is based on an actor/critic framework where a critic neural network is used to learn the optimal cost and an actor neural network is used to learn ...

Neurons with oscillatory properties are a common feature of the nervous system, but little is known about how neural oscillators shape the behavior of neuronal networks or how network interactions influence the properties of neural oscillators. Mathematical models are used to examine the effect of electrically coupling an oscillatory neuron to a second neuron that is either silent or tonically ...

Despite recent major advances in computational power and control algorithms, the stable and robust control of a bipedal robot is still a challenging issue due to the complexity and high nonlinearity of robot dynamics. To address the issue an efficient and powerful alternative based on a biologically inspired control framework employing neural oscillators is proposed and...

Variational methods are used to determine the optimal currents that elicit spikes in various phase reductions of neural oscillator models. We show that, for a given reduced neuron model and target spike time, there is a unique current that minimizes a square-integral measure of its amplitude. For intrinsically oscillatory models, we further demonstrate that the form and scaling of this current ...

We proposed a higher-order coupling neural network model including the inhibitory neurons and examined the dynamical evolution of average number density and phase-neural coding under the spontaneous activity and external stimulating condition. The results indicated that increase of inhibitory coupling strength will cause decrease of average number density, whereas increase of excitatory couplin...

By introducing Colpitts oscillator as a chaotic system, this paper deals with back-stepping control method and investigates the restrictions and problems of the controller where non-existence of a suitable response in the presence of uncertainty is the most important problem to note. In this paper, the back-stepping sliding mode method is introduced as a robust method for controlling nonlinear ...