Rational neural networks are feedforward neural networks with a rational activation function. These networks found their applications in approximating the solutions of PDE, as they are able to learn the poles of meromorphic functions. In this talk, we are going to consider the simplest rational activation function, sigma = 1 / x, and study the geometry of family such architectures. We will show that the closure of all possible shallow (one hidden layer) networks is an algebraic variety, which called a neurovariety.