Auditory forebrain neurons track temporal features of time-warped natural stimuli.

A fundamental challenge for sensory systems is to recognize natural stimuli despite stimulus variations. A compelling example occurs in speech, where the auditory system can recognize words spoken at a wide range of speeds. To date, there have been more computational models for time-warp invariance than experimental studies that investigate responses to time-warped stimuli at the neural level. Here, we address this problem in the model system of zebra finches anesthetized with urethane. In behavioral experiments, we found high discrimination accuracy well beyond the observed natural range of song variations. We artificially sped up or slowed down songs (preserving pitch) and recorded auditory responses from neurons in field L, the avian primary auditory cortex homolog. We found that field L neurons responded robustly to time-warped songs, tracking the temporal features of the stimuli over a broad range of warp factors. Time-warp invariance was not observed per se, but there was sufficient information in the neural responses to reliably classify which of two songs was presented. Furthermore, the average spike rate was close to constant over the range of time warps, contrary to recent modeling predictions. We discuss how this response pattern is surprising given current computational models of time-warp invariance and how such a response could be decoded downstream to achieve time-warp-invariant recognition of sounds.