Abstract
Neuro-inspired implementations have attracted strong interest as a power efficient and robust alternative to the digital model of computation with a broad range of applications. Especially, neuro-mimetic systems able to produce and process spike-encoding schemes can offer merits like high noise-resiliency and increased computational efficiency. Towards this direction, integrated photonics can be an auspicious platform due to its multi-GHz bandwidth, its high wall-plug efficiency and the strong similarity of its dynamics under excitation with biological spiking neurons. Here, we propose an integrated all-optical neuron based on an InAs/InGaAs semiconductor quantum-dot passively mode-locked laser. The multi-band emission capabilities of these lasers allows, through waveband switching, the emulation of the excitation and inhibition modes of operation. Frequency-response effects, similar to biological neural circuits, are observed just as in a typical two-section excitable laser. The demonstrated optical building block can pave the way for high-speed photonic integrated systems able to address tasks ranging from pattern recognition to cognitive spectrum management and multi-sensory data processing.
Highlights
The enormous technological leaps in terms of hardware and software development of the last decades have provided the means to address a vast class of problems that require extensive computational power
The behavior of the dynamics observed in biological spiking neurons shares many similarities with various photonic components, with the additional advantage that the response time of photonic systems is orders of magnitude lower than their biological counterparts[19]
In order to highlight the importance of both excitatory and inhibitory functions, the fundamental operational properties of biological neurons are briefly discussed in the subsequent paragraphs
Summary
An interesting subset of neuro-mimetic architectures are the ones that exploit the spiking encoding scheme, which is a digital-analog hybrid that allows information to be represented both in time and space[4] This encoding strategy offers increased noise resilience[5] and potential improvements in terms of computational efficiency[6]. A single biological neural cell can be described, from an operational point of view, as a threshold driven leaky integrator that can be impelled to activation, or suppression, depending on the inputs that the neuron receives, following an all-or-nothing principle These inputs are a prerequisite condition for neural excitation, the temporal characteristics of the neuron’s response are solely governed by its internal dynamics[45]. The second category (Fig. 1b), defined as inhibitory neurons, includes nodes that activated through the same stimulus as excitatory neurons, when triggered, they emit pulses that tend to suppress activity in all connected nodes[46]
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