Abstract
It is claimed that synaptic plasticity of neural controllers for autonomous robots can enhance the behavioral properties of these systems. Based on homeostatic properties of so called self-regulating neurons, the presented mechanism will vary the synaptic strength during the robot interaction with the environment, due to driving sensor inputs and motor outputs. This is exemplarily shown for an obstacle avoidance behavior in simulation.
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Zahedi, K., Pasemann, F. (2007). Adaptive Behavior Control with Self-regulating Neurons. In: Lungarella, M., Iida, F., Bongard, J., Pfeifer, R. (eds) 50 Years of Artificial Intelligence. Lecture Notes in Computer Science(), vol 4850. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77296-5_19
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DOI: https://doi.org/10.1007/978-3-540-77296-5_19
Publisher Name: Springer, Berlin, Heidelberg
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