Subwavelength neuromorphic nanophotonic integrated circuits for spike-based computing : challenges and prospects

Event-activated biological-inspired subwavelength (sub-λ) optical neural networks are of paramount importance for energy-efficient and high-bandwidth artificial intelligence (AI) systems. Despite the significant advances to build active optical artificial neurons using for example phase-change materials, lasers, photodetectors, and modulators, miniaturized integrated sources and detectors suited for few-photon spike-based operation and of interest for neuromorphic optical computing are still lacking. In this invited paper we outline the main challenges, opportunities, and recent results towards the development of interconnected neuromorphic nanoscale light-emitting diodes (nanoLEDs) as key-enabling artificial spiking neuron circuits in photonic neural networks. This method of spike generation in neuromorphic nanoLEDs paves the way for sub-λ incoherent neural circuits for fast and efficient asynchronous brain-inspired computation

Generalized master equations and fractional Fokker–Planck equations from continuous time random walks with arbitrary initial conditions

In the standard continuous time random walk the initial state is taken as a non-equilibrium state, in which the random walking particle has just arrived at a given site. Here we consider generalizations of the continuous time random walk to accommodate arbitrary initial states. One such generalization provides information about the initial state through the introduction of a first waiting time density that is taken to be different from subsequent waiting time densities. Another generalization provides information about the initial state through the prior history of the arrival flux density. The master equations have been derived for each of these generalizations. They are different in general but they are shown to limit to the same master equation in the case of an equilibrium initial state. Under appropriate conditions they also reduce to the master equation for the standard continuous time random walk with the non-equilibrium initial state. The diffusion limit of the generalized master equations is also considered, with Mittag-Leffler waiting time densities, resulting in the same fractional Fokker–Planck equation for different initial conditions

Noise induced aperiodic rotations of particles trapped by a non-conservative force

We describe a mechanism whereby random noise can play a constructive role in the manifestation of a pattern, aperiodic rotations, that would otherwise be damped by internal dynamics. The mechanism is described physically in a theoretical model of overdamped particle motion in two dimensions with symmetric damping and a non-conservative force field driven by noise. Cyclic motion only occurs as a result of stochastic noise in this system. However, the persistence of the cyclic motion is quantified by parameters associated with the non-conservative forcing. Unlike stochastic resonance or coherence resonance, where noise can play a constructive role in amplifying a signal that is otherwise below the threshold for detection, in the mechanism considered here, the signal that is detected does not exist without the noise. Moreover, the system described here is a linear system

Nonconservative dynamics of optically trapped high-aspect-ratio nanowires

We investigate the dynamics of high-aspect-ratio nanowires trapped axially in a single gradient force optical tweezers. A power spectrum analysis of the dynamics reveals a broad spectral resonance of the order of kHz with peak properties that are strongly dependent on the input trapping power. A dynamical model incorporating linear restoring optical forces, a nonconservative asymmetric coupling between translational and rotational degrees of freedom, viscous drag, and white noise provides an excellent fit to experimental observations. A persistent low-frequency cyclical motion around the equilibrium trapping position, with a frequency distinct from the spectral resonance, is observed from the time series data