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Volume 17, Number 15, Issue of August 1, 1997 pp. 5956-5971
Copyright ©1997 Society for Neuroscience

Muscle Response to Changing Neuronal Input in the Lobster (Panulirus interruptus) Stomatogastric System: Spike Number- versus Spike Frequency-Dependent Domains

Received April 7, 1997; revised May 8, 1997; accepted May 12, 1997.

Lee G. Morris and Scott L. Hooper

Neurobiology Program, Department of Biological Sciences, Ohio University, Athens, Ohio 45701

We aimed to determine the neuronal parameters controlling the contraction of slowly contracting, non-twitch ("tonic") muscles driven by rhythmic neuronal activity. These muscles are almost completely absent in mammals but are common in lower vertebrates and invertebrates. Slow muscles are often believed to function primarily in tonic motor patterns. However, previous research and data presented here indicate that slow muscles are also driven by rhythmic neuronal inputs.

In rapidly contracting "twitch" muscles, motor unit force is believed to be primarily determined by motor neuron spike frequency. What determines slow muscle output is less well understood. We present a simple model that suggests that when motor neuron burst duration is brief compared with muscle summation time, spike number, not spike frequency, determines slow muscle contraction amplitude.

We present analyses that distinguish between spike number and spike frequency dependence in two slow muscles in the lobster stomatogastric system. Our analysis shows that, functionally, one muscle is spike number dependent, whereas the other is primarily spike frequency dependent. Thus, both of these parameters can determine slow muscle output. To predict the movements elicited by neuronal activity in preparations in which slow muscles are common, it may be necessary to determine spike number versus spike frequency dependence for each muscle.

Spike number dependence couples motor neuron burst duration and spike frequency in that changing either parameter alone alters spike number (and hence muscle contraction amplitude). Neural networks innervating spike number-dependent muscles may therefore have specific properties to compensate for the complexity intrinsic to spike number coding.

Key words: Panulirus interruptus; lobster; crustacea; stomatogastric; pylorus; gastric mill; pyloric network; gastric network; slow muscle; muscle contraction amplitude; spike number; spike frequency; rate coding; number coding




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