1. A method for extracting auditory features from a time-varying signal using computer-implemented neural networks comprising: encoding a computer-implemented artificial neural network of layers of computer-represented neurons implemented on a computer system to have an auditory filter bank receiving an input audio signal, an auditory nerve layer coupled to the auditory filter bank and a cepstral coefficient layer coupled to the auditory nerve layer; decomposing by a computer processor the input audio signal into frequency spectrum data using an auditory filter bank that detects power at a set of predetermined frequencies; representing the detected power at each of the predetermined frequencies with the auditory nerve layer; decorrelating by a computer processor the detected power at each of the predetermined frequencies and representing the decorrelated information with the cepstral coefficient layer by determining cepstral coefficients for each frequency; computing by the computer system the derivative of cepstral coefficients from the cepstral layer; generating by a computer processor an auditory feature vector from a concatenation of decoded vector outputs of the cepstral coefficient layer and the derivative layer; feeding said auditory feature vector to an audio signal recognizer to obtain auditory recognition results; wherein coupling weights between the auditory nerve layer and the cepstral coefficient layer decorrelate decoded information in the auditory nerve layer, approximating an inverse discrete cosine transform.