Intensity invariance properties of auditory neurons compared to the statistics of relevant natural signals in grasshoppers
- 104 Downloads
The temporal pattern of amplitude modulations (AM) is often used to recognize acoustic objects. To identify objects reliably, intensity invariant representations have to be formed. We approached this problem within the auditory pathway of grasshoppers. We presented AM patterns modulated at different time scales and intensities. Metric space analysis of neuronal responses allowed us to determine how well, how invariantly, and at which time scales AM frequency is encoded. We find that in some neurons spike-count cues contribute substantially (20–60%) to the decoding of AM frequency at a single intensity. However, such cues are not robust when intensity varies. The general intensity invariance of the system is poor. However, there exists a range of AM frequencies around 83 Hz where intensity invariance of local interneurons is relatively high. In this range, natural communication signals exhibit much variation between species, suggesting an important behavioral role for this frequency band. We hypothesize, just as has been proposed for human speech, that the communication signals might have evolved to match the processing properties of the receivers. This contrasts with optimal coding theory, which postulates that neuronal systems are adapted to the statistics of the relevant signals.
KeywordsSpike-train metric Decoding Acoustic communication Optimal coding Evolution
We thank Matthias Hennig as well as the anonymous reviewers for helpful comments on previous versions of the manuscript and M. Bauer and O. von Helversen for providing the grasshopper song recordings. The study was supported by grants from the Bundesministerium für Bildung und Forschung (Bernstein Center for Computational Neuroscience) and the Deutsche Forschungsgemeinschaft (Sonderforschungsbereich 618) to B.R. The experiments comply with the current laws on “Principles of animal care” in Germany.
- Barlow HB (1961) Possible principles underlying the transformations of sensory messages. In: Rosenblith WA (ed) Sensory communication. The MIT Press, Cambridge, pp 217–234Google Scholar
- Gerhardt CH, Huber F (2002) Acoustic communication in insects and anurans. University of Chicago Press, LondonGoogle Scholar
- Koch C (1998) Biophysics of computation: information processing in single neurons (computational neuroscience). Oxford University Press, New YorkGoogle Scholar
- Lawley DN, Maxwell AE (1971) Factor analysis as a statistical method, 2nd edn. Elsevier, New YorkGoogle Scholar
- Martinez WL (2004) Exploratory data analysis with MATLAB (computer science and data analysis). Chapman and Hall/CRC, LondonGoogle Scholar
- Rieke F, Warland D, van Steveninck R, Bialek W (1999) Spikes: exploring the neural code (computational neuroscience). The MIT Press, CambridgeGoogle Scholar
- Stumpner A, Ronacher B (1991) Auditory interneurones in the metathoracic ganglion of the grasshopper Chorthippus biguttulus: I. Morphological and physiological characterization. J Exp Biol 158(1):391–410Google Scholar
- Stumpner A, Ronacher B (1994) Neurophysiological aspects of song pattern recognition and sound localization in grasshoppers. Am Zool 34(6):696–705Google Scholar
- Stumpner A, Ronacher B, von Helversen O (1991) Auditory interneurones in the metathoracic ganglion of the grasshopper Chorthippus biguttulus: II. Processing of temporal patterns of the song of the male. J Exp Biol 158(1):411–430Google Scholar
- Uchida N, Mainen ZF (2007) Odor concentration invariance by chemical ratio coding. Front Syst Neurosci 1:3Google Scholar
- van Alphen JJM, Seehausen O, Galis F (2004) Speciation and radiation in African haplochromine cichlids. In: Diekmann U, Doebeli M, Metz JAJ, Tautz D (eds) Adaptive speciation (Cambridge studies in adaptive dynamics). Cambridge University Press, Cambridge, pp 54–75Google Scholar