Feature analysis of natural sounds in the songbird auditory forebrain

Although understanding the processing of natural sounds is an important goa l in auditory neuroscience, relatively little is known about the neural cod ing of these sounds. Recently we demonstrated that the spectral temporal re ceptive field (STRF), a description of the stimulus-response function of au ditory neurons, could be derived from responses to arbitrary ensembles of c omplex sounds including vocalizations. In this study, we use this method to investigate the auditory processing of natural sounds in the birdsong syst em. We obtain neural responses from several regions of the songbird auditor y forebrain to a large ensemble of bird songs and use these data to calcula te the STRFs, which are the best linear model of the spectral-temporal feat ures of sound to which auditory neurons respond. We find that these neurons respond to a wide variety of features in songs ranging from simple tonal c omponents to more complex spectral-temporal structures such as frequency sw eeps and multi-peaked frequency stacks. We quantify spectral and temporal c haracteristics of these features by extracting several parameters from the STRFs. Moreover, we assess the linearity versus nonlinearity of encoding by quantifying the quality of the predictions of the neural responses to song s obtained using the STRFs. Our results reveal successively complex functio nal stages of song analysis by neurons in the auditory forebrain. When we m ap the properties of auditory forebrain neurons, as characterized by the ST RF parameters, onto conventional anatomical subdivisions of the auditory fo rebrain, we find that although some properties are shared across different subregions, the distribution of several parameters is suggestive of hierarc hical processing.