# Neuromorphic Analog Machine Vision Enabled by Nanoelectronic Memristive Devices
## 4 Experiments and Results  
Several computer models have been developed to verify the architectures proposed here. The operation of the sensory part of a formal ANN can be demonstrated using a SPICE model (Figure 6). This model contains the following main components: an equivalent photodiode circuit, containing a photocurrent source; a memristor model [76]; an initialization and recording signal generator; and switching circuits. The signal generator allows you to supply an initialization signal to the memristor in the form of a rectangular pulse with a given amplitude and duration (init) and a recording signal in the form of a sequence of rectangular pulses with increasing amplitudes (write). For inference, a signal from the sensor is generated at the output of the U\({}_{3}\) microcircuit in the form of rectangular pulses with a given duration. The switching of generator signals is performed by switches S\({}_{1}\)-S\({}_{6}\) by supplying a high level of control signals V\({}_{\rm ki}\) for initialization, V\({}_{\rm kp}\) for recording, and V\({}_{\rm kws}\) for processing visual information. The stages of the circuit operation described in Section 3 are switched by control signals V\({}_{\rm ks}\), V\({}_{\rm ki}\) (high), and V\({}_{\rm kw}\) (low) for initialization, and V\({}_{\rm ks}\), V\({}_{\rm kw}\) (high), and V\({}_{\rm ksi}\) (low) for recording the ANN model (stage one) and for inference (stage two). The current in the circuit with the memristor is converted by the transimpedance amplifier U\({}_{5}\) into voltage to control the recording and transferal of processed data to subsequent layers of the ANN. The graph in Figure 6 is plotted for the current in the memristor circuit at point TE. When an initialization signal of duration t\({}_{\rm init}\) is applied, the memristor is transferred from an LRS to an HRS (RESET). Next, when a write signal is applied during the time t\({}_{\rm write}\), the memristor is programmed to the target resistance R\({}_{\rm T}\). This ends stage one. In the next stage, the current source I\({}_{2}\) produces photocurrent. To simulate different illumination levels, the current source I\({}_{2}\) produces five photocurrent values, from 10 to 100 \(\mu\)A. This current is converted into voltage pulses with different amplitudes and a fixed duration t\({}_{\rm exp}\), which are then applied to the memristor and cause current to flow in the circuit. Figure 5: Architectures of the neuromorphic analog machine vision systems based on memristive devices. (**A**) For formal ANNs, weights are mapped to a crossbar array of memristive devices in cases where the bipolar weights are obtained due to a differential input. The input sensory circuits are connected directly to the inputs of neurons without digitalization. Visual information is encoded via the voltage amplitude, depending on illumination. (**B**) For spiking ANNs, presynaptic neurons generate spikes, the frequency of which depends on illumination. The value of the synaptic weights changes during the learning process. A postsynaptic neuron generates spikes when the charge on the membrane exceeds a threshold “Th”. So, the entire analog machine vision system is a spiking ANN without analog-to-digital and digital-to-analog converters. From Figure 6 it can be seen that a brighter illumination level corresponds to a higher current in the circuit with the memristor. This is then converted into voltage pulses with a coefficient R\({}_{\rm f}\) and is processed in the following layers. The maximum voltage (corresponding to the maximum current I\({}_{2}\)) does not change the memristor's resistance due to the fact that R\({}_{\rm f}\) is calculated at the memristor threshold voltage. The operation of the sensory part of the spiking ANN can be demonstrated using the following SPICE model (Figure 7). This model contains, in addition to the model in Figure 6, an integrator with a threshold U\({}_{4}\) and an amplifier U\({}_{1}\).