Computer-implemented method for classifying input signals (x), which were ascertained dependent on an output signal (S) of a sensor , by means of a neural network , wherein the sensor is an imaging sensor, in particular an optical sensor such as an image sensor or a video sensor, or a radar sensor or an ultrasonic sensor or a LiDAR sensor or an acoustic sensor or a position sensor or a kinematic sensor, wherein a control signal (A) is ascertained dependent on an output signal (y) of the neural network , where an actuator is controlled on the basis of the control signal (A), wherein the neural network comprises a scaling layer ( S 4 ), wherein the scaling layer maps an input signal ( z 4 ) present at the input of the scaling layer ( S 4 ) onto an output signal ( z 5 ) present at the output of the scaling layer ( S 4 ), in such a way that this map corresponds to a projection of the input signal ( z 4 ) onto a predeterminable value range, characterized in that parameters ( ρ , c ) that characterize the map are predeterminable, and in that the map is represented by the equation z 5 = argmin N 1 ( z 5 - c )≤ ρ N 2 ( z 4 - z 5 ) with a first norm ( N 1 ) and a second norm ( N 2 ), and in that in a training phase the predeterminable parameters ( ρ , c ) were adapted on the basis of a method for training the neural network , and in that during training these predeterminable parameters ( ρ , c) , were adapted on the basis of an output signal (y) of the neural network when an input signal (x) of the neural network and an associated desired output signal ( y T ) were supplied, and the predeterminable parameters were adapted on the basis of an ascertained gradient (g) that depended on the output signal (y) of the neural network and the associated desired output signal ( y T ). wherein the sensor is an imaging sensor, in particular an optical sensor such as an image sensor or a video sensor, or a radar sensor or an ultrasonic sensor or a LiDAR sensor or an acoustic sensor or a position sensor or a kinematic sensor, wherein a control signal (A) is ascertained dependent on an output signal (y) of the neural network , where an actuator is controlled on the basis of the control signal (A), wherein the neural network comprises a scaling layer ( S 4 ), wherein the scaling layer maps an input signal ( z 4 ) present at the input of the scaling layer ( S 4 ) onto an output signal ( z 5 ) present at the output of the scaling layer ( S 4 ), in such a way that this map corresponds to a projection of the input signal ( z 4 ) onto a predeterminable value range, characterized in that parameters ( ρ , c ) that characterize the map are predeterminable, and in that the map is represented by the equation z 5 = argmin N 1 ( z 5 - c )≤ ρ N 2 ( z 4 - z 5 ) with a first norm ( N 1 ) and a second norm ( N 2 ), and in that in a training phase the predeterminable parameters ( ρ , c ) were adapted on the basis of a method for training the neural network , and in that during training these predeterminable parameters ( ρ , c) , were adapted on the basis of an output signal (y) of the neural network when an input signal (x) of the neural network and an associated desired output signal ( y T ) were supplied, and the predeterminable parameters were adapted on the basis of an ascertained gradient (g) that depended on the output signal (y) of the neural network and the associated desired output signal ( y T ). <EOS>