Abstract
Synaptic plasticity for recurrent neural networks is derived by introducing neurons as self-regulating units. These neurons have homeostatic properties for certain parameter domains. Depending on its underlying connectivity a neurocontroller endowed with the derived synaptic plasticity rule can generate a variety of different behaviors. The structure of these networks can be developed by evolutionary techniques. For demonstration, examples are given generating a walking behavior for a 3-joint single leg of a walking machine.
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Pasemann, F. (2013). Self-regulating Neurons in the Sensorimotor Loop. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_48
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DOI: https://doi.org/10.1007/978-3-642-38679-4_48
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