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J Neurophysiol. 2013 Jul;110(2):344-57. doi: 10.1152/jn.00079.2013. Epub 2013 Apr 24.

Five types of nonspiking interneurons in local pattern-generating circuits of the crayfish swimmeret system.

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Institute of Zoology, University of Cologne, Cologne, Germany.


We conducted a quantitative analysis of the different nonspiking interneurons in the local pattern-generating circuits of the crayfish swimmeret system. Within each local circuit, these interneurons control the firing of the power-stroke and return-stroke motor neurons that drive swimmeret movements. Fifty-four of these interneurons were identified during physiological experiments with sharp microelectrodes and filled with dextran Texas red, Neurobiotin, or both. Five types of neurons were identified on the basis of combinations of physiological and anatomical characteristics. Anatomical categories were based on 16 anatomical parameters measured from stacks of confocal images obtained from each neuron. The results support the recognition of two functional classes: inhibitors of power stroke (IPS) and inhibitors of return stroke (IRS). The IPS class of interneuron has three morphological types with similar physiological properties. The IRS class has two morphological types with physiological properties and anatomical features different from the IPS neurons but similar within the class. Three of these five types have not been previously identified. Reviewing the evidence for dye coupling within each type, we conclude that each type of IPS neuron and one type of IRS neuron occur as a single copy in each local pattern-generating circuit. The last IRS type includes neurons that might occur as a dye-coupled pair in each local circuit. Recognition of these different interneurons in the swimmeret pattern-generating circuits leads to a refined model of the local pattern-generating circuit that includes synaptic connections that encode and decode information required for intersegmental coordination of swimmeret movements.


graded transmission; local circuit; motor pattern generation; nonspiking neurons; quantitative morphology

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