  In recent times, neural networks have been gaining increasing importance in
fields such as pattern recognition and computer vision. However, their usage
entails significant energy and hardware costs, limiting the domains in which
this technology can be employed.
  In this context, the feasibility of utilizing analog circuits based on
memristors as efficient alternatives in neural network inference is being
considered. Memristors stand out for their configurability and low power
consumption.
  To study the feasibility of using these circuits, a physical model has been
adapted to accurately simulate the behavior of commercial memristors from
KNOWM. Using this model, multiple neural networks have been designed and
simulated, yielding highly satisfactory results.
