On reducing the nonlinear depth of multidimensional convolutional neural schemes

The paper considers multidimensional convolutional schemes in the McCulloch-Pitts basis. It is shown that the considered schemes can be implemented by a scheme from the a priori and dynamic parts, in which the calculations in the a priori part are independent of the input data. In this case, the a priori and dynamic parts have a nonlinear depth equal to $ 2 $.