# Neuromorphic Spatiotemporal Information Processing Using Neuro-Photodetector Systems
## 1 Introduction  
Fundamental information processing units of the human brain are interconnected electrically excitable cells--known as neurons and synapses [1; 2; 3; 4; 5]. Importantly, depending on strength, duration, and repetition rate of the stimuli, the neurons exhibit few unique features, such as leaky integration, automatic fire, and recovery [5]. On the other hand, a synapse can store the memory typically short- and long-term memories [1; 6; 7]. Indeed, numerous interconnected neurons and synapses form micro- to nano-scale networks, provide a unique platform to do fault-tolerance, energy-efficient information processing, as well as store memory. This parallel architecture can effectively be implemented to design a robust computing system beyond the conventional [PERSON] computer [3; 5; 8; 9]. Therefore, research efforts have been made in recent years to mimic brain-like functions, artificially for instance, Mott insulator [5], redox memristors [1; 5; 10], phase-change memristors [3; 5; 11], and chalcogenide threshold switches [12] have been implemented for temporal synaptic integration. Nevertheless, with neuromorphic devices based on these approaches, there still exist a major impediment; specifically, memristors are suffering from nonlinearities, large write noise, and high operative voltage during operation, which in turn pose challenges to apply it for energy efficient brain-like computing [9; 13]. An alternative could be to design photo-operated devices in contrast to the electric triggering, which in fact work with the real sense simulation, because around 70% of the information that humans get from the outside world comes from vision [4; 14]. Furthermore, optical triggered devices operate without any contact, and provide promising approaches to realize high operating bandwidth and avoidable heating during operation [2; 3; 4; 15; 16].  
It is worth it to mention here that the visual system is a unique part of the biological neural network, in which retina transforms light signals to electrical pulses, similar to a photodetector [17; 18; 19].  
In fact, spatiotemporal information within the human brain is decoded by spatial and temporal occurrences of spikes within the neurons network. Therefore, to mimic this behavior, it is essential that the device emulates both space and time domains, simultaneously with direct optical inputs, and can conduct processing and memory within a single unit. Previously available visual systems contained separate image sensors, memory, and processing units, which in turn made it more complex and presented serious challenges in terms of large area device integration and power consumption. A simple photodetector that, for instance, could intrinsically show the bio-neuron equivalent desirable dynamics with direct optical pulses, and store the memory simultaneously, would be an effective approach. However, unlike the widely reported artificial synapses, the development of artificial photonic neurons--to the best of our knowledge--has not yet been demonstrated.  
Herein, we report a novel approach to design a purely photonic-triggered artificial neuron and synapse with the same architecture and material. The device shows all essential functions of the neuron (such as leaky integration, fire) and short- and long-term memory storage, such as synapse, together in response with direct optical input. This functioning relies on the photo-induced electron-hole pair generation and its trapping via oxygen vacancies in the host material. Further, an array of such devices is capable of pattern classification and supervised learning based on spatiotemporal optical inputs. The present device emulates both the processes and memory functions of the brain, and will be a foundation of the next-generation neuromorphic computing system.