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Grasshopper love songs give insight into sensory tuning

As anyone whose nerves have been jangled by a baby's howl or who have been riveted by the sight of an attractive person knows, nature has evolved sensory systems to be exquisitely tuned to relevant input. A major question in neurobiology is how neurons tune the strength of their interconnections to optimally respond to such inputs.

Neuronal circuitry consists of a web of neurons, each triggering others by launching bursts of neurotransmitters at targets on receiving neurons to produce nerve impulses in those targets. Neurons adjust the strength of those connections adaptively, to amplify or suppress connections. Some four decades ago, a general principle called the "efficient coding hypothesis" was formulated, holding that sensory systems adjust to efficiently represent the complex, dynamic sounds, sights, and other sensory input from the environment.

Writing in the August 4, 2005, issue of Neuron, researchers led by Christian K. Machens of Cold Spring Harbor Laboratory and Andreas Herz of Humboldt-University Berlin describe experiments with grasshopper auditory neurons that reveal new details of such sensory coding. Their findings show that "optimal stimulus ensembles" that trigger the neurons differ from those the grasshopper hears in the natural environment but largely overlap with components of natural sounds found in mating and mate-location calls.

In their experiments, the researchers first played various snippets of white noise to isolated grasshopper auditory nerves and measured the electrophysiological signals reflecting the reactions of the auditory neurons to those sounds. These experiments revealed the distribution of stimuli called the "optimal stimulus ensemble" (OSE) that allowed the neurons in the system to perform optimally.

Once the researchers had characterized the OSE, they then analyzed how this measure compared to the neuronal response to natural sounds--including environmental sounds like the rustling of grass an
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Source:Cell Press


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