as a Robot Controller Neuromodulation as a Robot Controller A Brain-Inspired Strategy for Controlling Autonomous Robots

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Short-term plasticity as cause–effect hypothesis testing in distal reward learning (2014, background): “The utility of modulatory dynamics in models of reward learning and behavior is also validated by closed-loop robotic neural controllers (Ziemke and Thieme, 2002; Sporns and Alexander, 2002; Alexander and Sporns, 2002; Sporns and Alexander, 2003; Soltoggio et al, 2008; Cox and Krichmar, 2009).”
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation (2012, background): “, 2010), making neuromodulation particularly suitable for modeling and implementing learning processes (Sporns and Alexander, 2002; Doya, 2002; Doya and Uchibe, 2005; Soula et al., 2005; Farries and Fairhall, 2007; Krichmar, 2008; Cox and Krichmar, 2009).”
Rare Neural Correlations Implement Robotic Conditioning with Delayed Rewards and Disturbances (2013, background): “The utility of synaptic tags in the solution of the distal ; Doya, 1999, 2002) and neural robotic controllers (Krich-mar, 2008; Cox and Krichmar, 2009), in particular with focus on the role of neuromodulation in attention (Avery et al., 2012).”