A modified model of the Rosenblatt perceptron and learning method leading to a significant increase in the intelligence and learning speed of the Rosenblatt perceptron itself, as well as arbitrary neural network topologies based on it, based on the modified error correction method and the modified “Back propagation” algorithm. To increase the manageability of the perceptron topology, as well as the processes of recognition and learning, the cost of connection is added at the synapses in addition to the weighting factor. To increase the speed of the perceptron learning, first, at least at the initial stage of learning, the process is moved from nested recursive iterations of learning on different layers of synapses of perceptron to successive learning for different layers; and, second, the process is moved from the constant rate of learning parameter, which requires a sequence of small iterations when adjusting the weights, to the functional parameter of the learning speed, correcting the weight so that the error for the current image is compensated completely in one step.