Representation of acoustic communication signals by insect auditory receptor neurons

Despite their simple auditory systems, some insect species recognize certai n temporal aspects of acoustic stimuli with an acuity equal to that of vert ebrates; however, the underlying neural mechanisms and coding schemes are o nly partially understood. In this study, we analyze the response characteri stics of the peripheral auditory system of grasshoppers with special emphas is on the representation of species-specific communication signals. We use both natural calling songs and artificial random stimuli designed to focus on two low-order statistical properties of the songs: their typical time sc ales and the distribution of their modulation amplitudes. Based on stimulus reconstruction techniques and quantified within an inform ation-theoretic framework, our data show that artificial stimuli with typic al time scales of >40 msec can be read from single spike trains with high a ccuracy. Faster stimulus variations can be reconstructed only for behaviora lly relevant amplitude distributions. The highest rates of information tran smission (180 bits/sec) and the highest coding efficiencies (40%) are obtai ned for stimuli that capture both the time scales and amplitude distributio ns of natural songs. Use of multiple spike trains significantly improves the reconstruction of s timuli that vary on time scales <40 msec or feature amplitude distributions as occur when several grasshopper songs overlap. Signal-to-noise ratios ob tained from the reconstructions of natural songs do not exceed those obtain ed from artificial stimuli with the same low-order statistical properties. We conclude that auditory receptor neurons are optimized to extract both th e time scales and the amplitude distribution of natural songs. They are not optimized, however, to extract higher-order statistical properties of the song-specific rhythmic patterns.