S y n c h r o n i z a t i o n

Neurons, pacemaker cells inside the human heart and fireflies synchronize their response as a part of their collective dynamics. Can this behavior be exploited in physically engineered structures to mimic the complex behavior for advanced computing and pattern recognition?

quantum nanomechanics time measurement spin mechanics nanomechanical computing

nonlinear nanomechanics synchronization biomolecular recognition MEMS & NEMS

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Seen in southeast Asia, it is one of the most dazzling natural visual effects known: large congregations of fireflies blinking on and off in unison (Fig. 1). They orchestrate their flashing in almost perfect rhythm, and at a constant tempo. Each firefly maintains its steady beat through an internal clock, essentially a tiny oscillator inside its brain. Following outside stimuli, this oscillator begins to lock phase, or synchronize, with the firefly congregation. A similar thing happens in the human heart: there, a cluster of pacemaker cells, known as the sinoatrial node, generates a synchronous oscillation that commands the rest of the heart to beat, in rhythm, for the duration of a life — typically some three billion pulses.

(Left) Fireflies, fireflies burning bright. Certain species of firefly flash in perfect synchrony — here Pteroptyx malaccae in a mangrove apple tree in Malaysia. (Right) Two suspended nanomechanical silicon beams coupled by a mechanical element.

The concept of synchronized oscillation in coupled systems is pervasive in both nature and human physiology,. Examples of synchronization include rhythmic blinking of fireflies, pacemaker cells in the sinoatrial node of a human heart, and the spin-orbit resonance of the planet Mercury. In physical systems, synchronization has been studied for over three centuries starting with Huygens’ discovery of the phenomenon in two coupled pendulum clocks to modern day experiments on coherent radiation in coupled spin-torque nano-oscillators, and parametric resonance in mechanical oscillators.

A two-oscillator system demonstrates inherently rich linear and nonlinear dynamics, contrasting its deceptive simplicity. Following the historical observation of synchronization of two pendulum clocks by Huygens, Appleton and van der Pol showed that the frequency of a triode generator can be entrained, or synchronized, to an external drive; their work was motivated by the potential application in radio communication. The first systematic studies of synchronization in biological systems, and in particular, human physiology, started with Peskin’s attempt to model self-synchronization of cardiac pacemaker cells to understand the generation of a heart beat. In biological neurocomputing, neural networks show rhythmic behavior, exemplified in many brain subsystems, in which the pattern recognition properties are similar to those of oscillator networks.

The neural computing paradigm has been long inspired by the understanding that synchronization of oscillations in the brain is somehow related to the associative memory and learning functions. Beyond the mathematical modeling of neural network systems, practical realization of neural computing on a scalable architecture would be revolutionary. In spite of intense theoretical activities, the fundamental limitation to the practical realization of such parallel computing systems has been hardware engineering challenges. Some of these challenges include low power and high-speed requirements for autonomous computation, scalability, and manufacturability. One approach in addressing these issues is to construct the building-block network from micromechanical oscillator elements. Because of their small size, micromechanical oscillator structures have high normal-mode frequencies in the gigahertz range. Therefore, an individual unit of this network, a single micromechanical oscillator, possesses the necessary requirements: high speed, high density and scalability.

Our work in this field involves building oscillator networks to perform simple computation operations. Using multiple synchronization states, even complex operations such as pattern recognition can be performed.