Intrinsic Dynamics Enhance Decodability of Neurons in a Model of Avian Auditory Cortex

Birdsong is a complex vocalization that bears important similarities to human speech. Critical to recognizing speech or birdsong is the ability to discriminate between similar sequences of sound that may carry different meanings. The caudal mesopallium (CM) is a secondary area in the auditory system of songbirds that is a potential site for song identification, displaying both between-category selectivity and within-category tolerance to conspecific song. Electrophysiological studies of CM have identified a population of neurons with intrinsically phasic firing patterns in addition to the more typical tonic and fast-spiking neurons. The function of these phasic neurons in processing spectrotemporally complex conspecific vocalizations is not known. We investigated the auditory response properties of phasic and tonic neurons using computational modeling with particular focus on the selectivity and entropy of the simulated responses to birdsong. When biophysical models of phasic and tonic neurons were presented with identical inputs, the phasic models were more selective among syllables and more robust to noise-induced variability, potentially providing an advantage for song identification. Additionally, the overall responsiveness of a model to the stimulus set determined which decoding metric better captured the coding strategy of the model's response. The relationships between measures of decodability found in the model simulations are consistent with extracellular data from zebra finch CM.