Computer-implemented method for ascertaining a suitable network configuration for a neural network for a predefined application for realizing functions of a technical system, wherein the technical system is a robot, a vehicle, a tool or a factory machine, wherein the predefined application is determined in the form of training data; wherein the network configuration indicates the architecture of the neural network, wherein the neural network configured in this way is used in a control device of the technical system in order to determine output variables depending on input variables, wherein the input variables are image data or sensor data of a video sensor and/or of a radar sensor and/or of a LiDAR sensor and/or of an ultrasonic sensor, wherein at least one actuator of the technical system is controlled by a control signal depending on the output variables of the neural network by means of a computing unit of the control device of the technical system, comprising the following steps: f) proceeding from a current network configuration, generating (S1, S2) a plurality of network configurations to be evaluated, a portion of which differs from the current network configuration, by applying approximative network morphisms, wherein a network morphism is an operator T which maps a neural network N onto a varied neural network TN, wherein it holds true that: N w x = TN w ̃ x for x ∈ X in this case, w are network parameters of the neural network N and w̃ are network parameters of the varied neural network TN and X corresponds to a space to which the neural network is applied, wherein approximative network morphisms are rules for changes in the existing network configuration, wherein the resulting performance of the modified neural network is permitted to deviate from the performance of the underlying neural network by a specific measure, and then a network morphism is an approximative network morphism if the following holds true for training data X train made available: min w ̃ 1 X train ∑ x ∈ X train ‖ N w x − TN w ̃ x ‖ < ε where ε > 0; g) ascertaining affected network portions of the network configurations; h) multiphase training of each of the network configurations to be evaluated, under predetermined training conditions, wherein, in a first phase, network parameters of a portion not changed by the application of the respective approximative network morphism are in each case disregarded during the training and wherein, in at least one further phase, all network parameters are trained, i) determining a resulting prediction error ( error ( TN j)) for each of the network configurations to be evaluated; j) selecting the suitable network configuration depending on the determined prediction errors. f) proceeding from a current network configuration, generating (S1, S2) a plurality of network configurations to be evaluated, a portion of which differs from the current network configuration, by applying approximative network morphisms, wherein a network morphism is an operator T which maps a neural network N onto a varied neural network TN, wherein it holds true that: N w x = TN w ̃ x for x ∈ X in this case, w are network parameters of the neural network N and w̃ are network parameters of the varied neural network TN and X corresponds to a space to which the neural network is applied, wherein approximative network morphisms are rules for changes in the existing network configuration, wherein the resulting performance of the modified neural network is permitted to deviate from the performance of the underlying neural network by a specific measure, and then a network morphism is an approximative network morphism if the following holds true for training data X train made available: min w ̃ 1 X train ∑ x ∈ X train ‖ N w x − TN w ̃ x ‖ < ε where ε > 0; g) ascertaining affected network portions of the network configurations; h) multiphase training of each of the network configurations to be evaluated, under predetermined training conditions, wherein, in a first phase, network parameters of a portion not changed by the application of the respective approximative network morphism are in each case disregarded during the training and wherein, in at least one further phase, all network parameters are trained, i) determining a resulting prediction error ( error ( TN j)) for each of the network configurations to be evaluated; j) selecting the suitable network configuration depending on the determined prediction errors.