Abstract
Sensory neurons encode information using multiple nonlinear and dynamical transformations. For instance, auditory receptor neurons in Drosophila adapt to the mean and the intensity of the stimulus, change their frequency tuning with sound intensity, and employ a quadratic nonlinearity. While these computations are considered advantageous in isolation, their combination can lead to a highly ambiguous and complex code that is hard to decode. Combining electrophysiological recordings and computational modelling, we investigate how the different computations found in auditory receptor neurons in Drosophila combine to encode behaviorally-relevant acoustic signals like the courtship song.
The computational model consists of a quadratic filter followed by a divisive normalization stage and reproduces population neural responses to artificial and natural sounds. For general classes of sounds, like band-limited noise, the representation resulting from these highly nonlinear computations is highly ambiguous and does not allow for a recovery of information about the frequency content and amplitude pattern. However, for courtship song, the code is simple and efficient: The quadratic filter improves the representation of the song envelope while preserving information about the song’s fine structure across intensities. Divisive normalization renders the presentation of the song envelope robust to the relatively slow fluctuations in intensity that arise during social interactions, while preserving information about the species-specific fast fluctuations of the envelope.
Overall, we demonstrate how a sensory system can benefit from adaptive and nonlinear computations while minimizing concomitant costs arising from ambiguity and complexity of readouts by adapting the code for behaviorally-relevant signals.
Competing Interest Statement
The authors have declared no competing interest.
