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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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