UC San Diego

Coupled oscillators always exhibit all kinds of emergent collective modes and intriguing synchronization phenomena, ranging from collective oscillations in bacteria to beating of cilia, from rhythms in biological neurons to phase synchronization in brain, from nanomechanical and nanoelectromechanical oscillators to spin Hall and spin torque nano-oscillators. This dissertation explores the unique dynamical behaviors of coupled spiking oscillators based on Mott materials. Spiking oscillator, a special type of oscillator that produces short-duration spikes (around 30 ns), contrasts with smoothly evolving harmonic oscillators. Spiking oscillators can emulate the electrical activity of brain, can develop large scale spiking neural networks, and can potentially be the building block for the energy-efficient oscillator-based computing. To design a complex network capable of performing advanced computational tasks, it is necessary to understand the basic phenomenology of the interactions between two spiking oscillators. First, I report the unusual emergence of a stochastic pattern in capacitively coupled spiking oscillators. While a moderate capacitive coupling results in a deterministic alternating spiking, increasing the coupling strength leads counterintuitively to stochastic disruptions of the alternating spiking sequence. Then I switch my focus to the thermally coupled spiking oscillators. Transition between two integer modes occurs through an unusual stochastic synchronization regime instead of the loss of spiking coherence. In the stochastic synchronization regime, random length spiking sequences belonging to the 1:1 and 2:1 mode are intermixed. By carefully tuning the load resistance and the input voltages of the coupled spiking oscillators, termed neuristors, I demonstrate a wide variety of reconfigurable electrical dynamics mirroring biological neurons, including all-or-nothing law, rate coding, stochastic leaky integrate-and-fire, excitatory and inhibitory functionalities. Moreover, random number generator will be demonstrated by taking advantage of the stochasticity hiding behind the synchronization. This dissertation investigates the basic phenomenology of the interactions between two spiking oscillators, discovers several usual findings, and establishes the foundation for scalable and energy-efficient brain-inspired computing.