Reinforcement learning by Hebbian synapses with adaptive thresholds.

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Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “RL in neural networks (Pennartz, 1997) makes use of robotic or agent-environment problems with a reward signal that affects plasticity in the network (Lin, 1993; Niv et al.”
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation (2012, unknown): “As observed by Pennartz (1997), models in which synaptic connections change sign are not biologically implausible and can be interpreted as two such parallel excitatory and inhibitory weights (Alger and Nicoll, 1982; Finch et al., 1988; Pennartz and Kitai, 1991).”