Efficient transformation of an auditory population code in a small sensory system.

Abstract

Optimal coding principles are implemented in many large sensory systems. They include the systematic transformation of external stimuli into a sparse and decorrelated neuronal representation, enabling a flexible readout of stimulus properties. Are these principles also applicable to size-constrained systems, which have to rely on a limited number of neurons and may only have to fulfill specific and restricted tasks? We studied this question in an insect system--the early auditory pathway of grasshoppers. Grasshoppers use genetically fixed songs to recognize mates. The first steps of neural processing of songs take place in a small three-layer feed-forward network comprising only a few dozen neurons. We analyzed the transformation of the neural code within this network. Indeed, grasshoppers create a decorrelated and sparse representation, in accordance with optimal coding theory. Whereas the neuronal input layer is best read out as a summed population, a labeled-line population code for temporal features of the song is established after only two processing steps. At this stage, information about song identity is maximal for a population decoder that preserves neuronal identity. We conclude that optimal coding principles do apply to the early auditory system of the grasshopper, despite its size constraints. The inputs, however, are not encoded in a systematic, map-like fashion as in many larger sensory systems. Already at its periphery, part of the grasshopper auditory system seems to focus on behaviorally relevant features, and is in this property more reminiscent of higher sensory areas in vertebrates.

DOI: 10.1073/pnas.1104506108

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Cite this paper

@article{Clemens2011EfficientTO, title={Efficient transformation of an auditory population code in a small sensory system.}, author={Jan Clemens and Olaf Kutzki and Bernhard Ronacher and Susanne Schreiber and Sandra Wohlgemuth}, journal={Proceedings of the National Academy of Sciences of the United States of America}, year={2011}, volume={108 33}, pages={13812-7} }