# Neuromorphic Analog Machine Vision Enabled by Nanoelectronic Memristive Devices
## 3 The System's Architecture  
The proposed system's architecture includes two main parts--sensory and neural. The sensory part is designed to capture and encode visual information. The signals from the sensory part, after being captured, are transmitted to the neural part for processing. The electrical circuit of the sensory part is presented in Figure 3 for two versions of ANNs--formal (Figure 3A) and spiking (Figure 3B). Figure 3: The electrical circuits of the sensory part (input channels) for the capturing and encoding of visual information in analog form and its subsequent transmission to an ANN without digitization. (**A**) An input channel for the formal ANNs. It consists of a photodiode PD, a load resistor R\({}_{\text{load}}\) (for converting photocurrent i\({}_{\text{ph}}\) to voltage), an operational amplifier U\({}_{\text{I}}\), and a bias voltage source V\({}_{\text{bias}}\) (reverse biasing is used in the photoconductive mode providing a wider bandwidth, higher sensitivity, and improved linearity (for sensitivity control at different weather conditions, day time, etc. ), but also increases noise and dark current). The SPDT and SPST switches control the operating modes of the circuit: the \"write mode\", when recording the ANN weights (by changing the memristor’s resistance from R\({}_{\text{init}}\) to the target R\({}_{\text{T}}\)), and the \"inference mode\". Visual information in this case is encoded using the voltage amplitude V\({}_{\text{i}}\) in each input channel. (**B**) An input channel for the spiking ANN. It consists of the same elements as the input channel for the formal ANN, except for the integrator with threshold U\({}_{2}\). The integrator accumulates charge and generates pulses of the same amplitude but at different frequencies, like an integrate-and-fire neuron (I&F). The multiplexer (MUX) U\({}_{3}\) and the SPDT switch control the operating modes of the circuit: for recording ANN weights, self-learning (by changing the memristor’s resistance from R\({}_{\text{init}}\) to R\({}_{\text{inf}}\)), and inference. Visual information is encoded according to signal frequency f\({}_{\text{i}}\). The input channel for a formal ANN operates in two stages. At the first stage (Figure 3A, on the left), the SPDT key breaks the connection of the input channel with the memristor ANN and connects it to the circuits for supplying initialization and programming pulses. At this stage, an initialization pulse V\({}_{\text{init}}\) is sent to the ANN, erasing information about the weights of neuron synapses. Next, synaptic weights are recorded by applying V\({}_{\text{write}}\) recording pulses. At this time, the shutter of the optical system is closed and no light enters the sensor part. It is necessary to avoid the influence of illumination on the unlearned model during the write mode. After the weights of the neuron synapses are written into the neural part, SPDT keys connect the ANN circuits to the circuit of input channels. The ANN then becomes ready to process visual information, and the entire system enters the second stage of operation. At the second stage of operation, visual information is processed. The shutter of the optical system opens at a given frequency (fps), and at this time, light falls on the photodiodes in the input channels and photocurrent i\({}_{\text{ph}}\) begins to flow in the circuit from the cathode to the anode. The sensitivity of the sensor can be increased by applying a voltage V\({}_{\text{bias}}\). During periods of shutter opening, the photocurrent is converted into voltage pulses with different amplitudes--the stronger the illumination, the higher the voltage amplitude. Thus, visual information is encoded with the amplitude of the signal. One impulse equals one interference. Voltage pulses are supplied to the neural part of the first layer of the ANN and so on through the entire network, thereby processing visual information completely in analog form. At the output of the neural part, a signal is generated in accordance with the ANN model recorded in the memristors.