Abstract
Neural networks which initiate and control the behavior of animals embody several features. One concerns the role of sensory input. At one extreme are networks which mediate actions that are direct responses to sensory input, i.e., reactive or sensory driven actions. At the other are networks which themselves generate the basic activation or movement parameters for the behavior, i.e., activity controlled by central pattern generators. Biological control systems which must produce suitable actions in unpredictable environments, however, usually contain elements of both kinds. A second feature concerns the structure of the control network. In most complex biological networks, control functions, whether sensory-driven or centrally-driven, are dispersed among several subsystems which interact more or less strongly. Both aspects are especially true for walking. Although walking is sometimes regarded as quite a simple behavior, it involves a very strong and complex interaction with the physical environment. Typical control systems involve central pattern generators as well as simple reflexes and more complex sensory-driven modulations of central activity (Cruse et al. 1990). The combination makes the walking system independent of particular stimulus inputs but at the same time enables the walking system to adapt to changes in the environment. The flexible control appears to arise from the cooperation of several control centers.
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Cruse, H. et al. (2000). A Modular Artificial Neural Net for Controlling a Six-Legged Walking System. In: Cruse, H., Dean, J., Ritter, H. (eds) Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic, Volume 1, Volume 2 Prerational Intelligence: Interdisciplinary Perspectives on the Behavior of Natural and Artificial Systems, Volume 3. Studies in Cognitive Systems, vol 26. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0870-9_27
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DOI: https://doi.org/10.1007/978-94-010-0870-9_27
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