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1Department of Physiology and Biophysics and Fishberg Research Center for Neurobiology, Mount Sinai School of Medicine, New York, New York; and 2Monell Chemical Senses Center, Philadelphia, Pennsylvania
Submitted 7 May 2004; accepted in final form 4 October 2004
Recent work in computational neuroethology has emphasized that "the brain has a body": successful adaptive behavior is not simply commanded by the nervous system, but emerges from interactions of nervous system, body, and environment. Here we continue our study of these issues in the accessory radula closer (ARC) neuromuscular system of Aplysia. The ARC muscle participates in the animal's feeding behaviors, a set of cyclical, rhythmic behaviors driven by a central pattern generator (CPG). Patterned firing of the ARC muscle's two motor neurons, B15 and B16, releases not only ACh to elicit the muscle's contractions but also peptide neuromodulators that then shape the contractions through a complex network of actions on the muscle. These actions are dynamically complex: some are fast, but some are slow, so that they are temporally uncoupled from the motor neuron firing pattern in the current cycle. Under these circumstances, how can the nervous system, through just the narrow channel of the firing patterns of the motor neurons, control the contractions, movements, and behavior in the periphery? In two earlier papers, we developed a realistic mathematical model of the B15/B16-ARC neuromuscular system and its modulation. Here we use this model to study the functional performance of the system in a realistic behavioral task. We run the model with two kinds of inputs: a simple set of regular motor neuron firing patterns that allows us to examine the entire space of patterns, and the real firing patterns of B15 and B16 previously recorded in a 21/2-h-long meal of 749 cycles in an intact feeding animal. These real patterns are extremely irregular. Our main conclusions are the following. 1) The modulation in the periphery is necessary for superior functional performance. 2) The components of the modulatory network interact in nonlinear, context- and task-dependent combinations for best performance overall, although not necessarily in any particular cycle. 3) Both the fast and the slow dynamics of the modulatory state make important contributions. 4) The nervous system controls different components of the periphery to different degrees. To some extent the periphery operates semiautonomously. However, the structure of the peripheral modulatory network ensures robust performance under all circumstances, even with the irregular motor neuron firing patterns and even when the parameters of the functional task are randomly varied from cycle to cycle to simulate a variable feeding environment. In the variable environment, regular firing patterns, which are fine-tuned to one particular task, fail to provide robust performance. We propose that the CPG generates the irregular firing patterns, which nevertheless are guaranteed to give robust performance overall through the actions of the peripheral modulatory network, as part of a trial-and-error feeding strategy in a variable, uncertain environment.
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