# Neuromorphic Analog Machine Vision Enabled by Nanoelectronic Memristive Devices
## 2 State-of-the-Art Studies
### Memristor-Based Spiking ANNs  
Throughout the history of its development, the theory of the ANN has been inspired by the results of studies on the principles of the functioning of biological neural networks (BNNs) [47]. The most powerful tool for solving computer vision problems currently is the convolutional ANN. Despite the fact that when developing the concept of convolutional ANNs, the peculiarities of the functioning of the visual cortex of the cerebral hemispheres, which has a parallel analogue nature of processing signals from the optic nerve, were taken into account, the same strict mathematical transformations occur in convolutional ANNs as in any other formal ANN architecture-matrix multiplication and activation. The weights of synapses in such ANNs are calculated using the backpropagation method, and visual information is encoded using the amplitude of the signal. Inside the BNNs, information is transmitted through a network of neurons that have some activation potential [48], using signals called spikes. Spikes are transmitted from one neuron to another using an axon and are characterized by their frequency, duration, and amplitude. Contacts between neurons are formed at strictly defined points called synapses. It is currently believed that the phenomena of memory and learning in living organisms arise due to the mechanisms of synaptic plasticity, which consists of the possibility of changing the strength of the synapse and, accordingly, changing the parameters of transmitted signals. The existence of synaptic plasticity leads to the fact that the human nervous system can independently configure individual groups of neurons to perform various functions. The resistance of memristive devices to changes within the boundaries of the minimum and maximum possible values is referred to as a LRS (low resistance state) or a HRS (high resistance state). The curve describing the characteristics of the transition of a memristive device from one state to another under the influence of voltage pulses with different shapes, amplitudes, and frequencies is similar in appearance to that of experimental measurements of synaptic plasticity in the BNNs [49]. Therefore, memristive devices are the most biosimilar artificial analogues to the synapses of neurons in living systems, and thus make it possible to implement neuromorphic ANN architectures in hardware. In spiking ANNs (Figure 2A), memristive devices connect presynaptic and postsynaptic neurons. The presynaptic neuron in the input layer of the network acts in this case as a generator of spikes, the frequency or timings of which encode the input information. For example, to process grayscale images, the brightness values of each pixel, represented by numbers from 0 to 255, can be represented by different frequencies ranging from 1 to 100 kHz. Spikes applied to a memristive device over a period of time change its resistance (Figure 2B)--this is a type of self-learning based on the local rules for each synapse. A postsynaptic neuron is a device capable of accumulating charge from all presynaptic neurons (Figure 2C), taking into account the voltage drop across the memristors (the so-called neuron membrane). It has a certain threshold charge value, exceeding which leads to the generation of a spike by the postsynaptic neuron. The final distribution of resistances of memristive devices determines the functionality of such a network of presynaptic and postsynaptic neurons and allows the solving of problems in the field of robotics, prosthetics, telecommunications, etc. In neuroscience, there are currently several mathematical models of spiking neurons [50]: Hodgkin-Huxley [51], FitzHugh-Nagumo [52], [PERSON] [53], [PERSON] [54], [PERSON] [55], [PERSON] [56], etc. The differences between these models lie in their degree of biological realism, the type of object being modeled (presynaptic neuron, neuronal membrane, neuron's morphology, etc. ), and in their level of computational complexity.