A memoryless, stochastic mechanism of timing of phases of behavior by a neural network controller
© Contractor et al; licensee BioMed Central Ltd. 2008
Published: 11 July 2008
For a sensorimotor network to generate adaptive behavior in the environment, the phases of the behavior must be appropriately timed. When the behavior is driven simply by the sensory stimuli from the environment, these can supply the timing. But when the behavior is driven by an internal "goal" that ignores and perhaps even opposes the immediate sensory stimuli, the timing must be generated internally by the network. We have modeled a realistic behavioral scenario that requires such internal timing.
When the sea slug Aplysia feeds, it incrementally ingests long strips of seaweed, driven by ingestive stimuli emanating from the seaweed. But if, having ingested a strip, the animal fails to break the strip off the substrate, it must incrementally egest the entire strip again. To do this, it must ignore the inherent ingestiveness of the seaweed and generate the opposite, egestive behavior, driven by an internal egestive goal, for a length of time that is appropriate for the length of the strip to be egested.
In previous work [1, 2], we found that a differential-equation model of the Aplysia feeding network, with dynamics like those experimentally observed , performed this task extremely well. In this model, the goal-driven egestion was appropriately timed by a slowly decaying dynamical transient that "remembered" the time elapsed since the beginning of the egestion.
Supported by NS41497.
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