# Neuromorphic Spatiotemporal Information Processing Using Neuro-Photodetector Systems
## 3 Results and Discussion
The overall change in the \(I_{\rm p}\) decreases with increasing the time duration between each stimulus. These two figures in fact confirm that, for a selected UV intensity, the \(I_{\rm p}\) generation can be modulated highly by varying the pulse intervals and/or number, providing an opportunity to choose several parameters to achieve the firing threshold value. Following the integration capabilities, if threshold for firing condition was set to 10 \(\mu\)A, then the output \(I_{\text{P}}\) above the threshold current (\(I\)w) as a function of pulse number and pulse duration are plotted in Figure 3d,e, respectively. Note that for a small number of pulses (\(n<3\), \(\Delta t=0.2\) s, \(\Delta d=3\) s), the \(I\)w is close to zero, indicating no firing condition. In contrast, above it, not only the \(I\)w is non-zero, but also increases with increasing the pulse number, which is because of the integration nature of \(I_{\text{P}}\). Further, Figure 3e can systematically summarize the change in the \(I\)w as a function pulse duration with six modulatory spiking inputs (intensity = 4 mW cm\({}^{-2}\), \(\Delta t=0.2\) s). These plots again confirmed that the firing properties of the presented neuro-photodetector can be modulated by pulse number and/or pulse duration. These figures present that the threshold nature can be modulated by the applied spikes. For instance, six pulses (intensity = 4 mW cm\({}^{-2}\), \(\Delta t=0.2\) s, \(\Delta d=9\) s) cannot generate the current above the threshold, while it can be achieved with short duration, like \(\Delta d=6\) s. Such a result is one of the great significances for information processing in neuromorphic computing. Overall, the photocurrent integration, very similar to the bio-neuron, can be performed using neuro-photodetector, resulting in providing a unique possibility to mimic temporal information processing. Another fundamental element, e.g., synapse, connects to two adjacent neurons and its communication strength (synaptic strength/weight) can be modulated by the concentrations of Ca\({}^{2+}\), Na\({}^{+}\), Mg\({}^{+}\), ionic species [1, 2, 3, 6, 28]. The device based on ZnO shows PPC, which has also been used to emulate the light-induced synaptic functions. In general, origin of learning and memory function of the human brain is believed to depend on the experience-dependent change in the synaptic plasticity, which can be altered by neuronal activities. Therefore, to realize the photo-triggered brain-like memory storage, the \(I_{\text{P}}\) is measured, corresponding to different number of pulses, see Figure 4a. Unlike conventional memory technologies, depending to the retention time, the human memory is classified into two types viz. short (STM) and long-term memory (LTM) [4, 6, 7, 29]. Fast current decay is considered as short-term memory (STM), whereas the slow decay is described as long-term memory  
Figure 3: Mimic bio-neuron behavior. (**a**) Probability of firing as a function of pulse intensity or duration. The insets show the signal behavior for different pulse duration. (**b**) Three-dimensional (3D) map of the photocurrent with a function of pulse number and UV intensity; (**c**) 3D map of the photocurrent with a function of pulse duration and UV intensity. (**d**) Pulse number-dependent photocurrent generation above the threshold. (**e**) Current above the threshold as a function of pulse duration. (**e**) The inset shows photoresponse for a six number of pulses, showing a continuous increasing behavior with increasing number of pulses, the threshold property of the device is depicted by red dotted line. (LTM). In general, there is no well-defined time frame for STM and LTM. A sequential transformation can be achieved by so-called rehearsal. Since, the device shows functionality of photon sensor, signal processing (via integration) and memory units together; therefore, we utilize an array of the devices for pattern recognition via associative memory.