This thesis develops a new method for time-frequency signal processing and examines the relevance of the new representation in studies of neural coding in songbirds. The method groups together associated regions of the time-frequency plane into objects defined by time-frequency contours. By combining information about structurally stable contour shapes over multiple time-scales and angles, a signal decomposition is produced that distributes resolution adaptively. As a result, distinct signal components are represented in their own most parsimonious forms.  Next, through neural recordings in singing birds, it was found that activity in song premotor cortex is significantly correlated with the objects defined by this new representation of sound. In this process, an automated way of finding sub-syllable acoustic transitions in birdsongs was first developed, and then increased spiking probability was found at the boundaries of these acoustic transitions. Finally, a new approach to study auditory cortical sequence processing more generally is proposed. In this approach, songbirds were trained to discriminate Morse-code-like sequences of clicks, and the neural correlates of this behavior were examined in primary and secondary auditory cortex. It was found that a distinct transformation of auditory responses to the sequences of clicks exists as information transferred from primary to secondary auditory areas. Neurons in secondary auditory areas respond asynchronously and selectively -- in a manner that depends on the temporal context of the click. This transformation from a temporal to a spatial representation of sound provides a possible basis for the songbird's natural ability to discriminate complex temporal sequences.

The mammalian auditory system generates representations of the physical world in terms of auditory objects. To decide which object class a particular sound belongs to, the auditory system must recognise the patterns of acoustic components that form the acoustic “fingerprint” of the sound’s auditory class. Where in the central auditory system such patterns are detected and what form the neural processing takes that underlies their detection are unanswered questions in sensory neurophysiology. In the research conducted for this thesis I used artificial vowel sounds to explore the neural and perceptual characteristics of auditory object recognition in rats. I recorded cortical responses from the primary auditory cortex (A1) in anaesthetised rats and determined how well the spiking responses, evoked by artificial vowels, resolve the spectral components that define vowel classes in human perception. The recognition of an auditory class rests on the ability to detect the combination of spectral components that all member sounds of the class share. I generated and evaluated models of the integration by A1 responses of the acoustic components that define human vowels classes. The hippocampus is a candidate area for neural responses that are specific to particular object classes. In this thesis I also report the results of a collaboration during which we investigated how the hippocampus responds to vowels in awake behaving animals. Finally, I explored the processing of vowels behaviourally, testing the perceptual ability of rats to discriminate and classify vowels and in particular whether rats use combinations of spectral components to recognise members of vowel classes. For the behavioural training I built a novel integrated housing and training cage that allows rats to train themselves in auditory recognition tasks. Combining the results and methods presented in this thesis will help reveal how the mammalian auditory system recognises auditory objects.