Neural mechanism for stochastic behaviour during a competitive game

By Alireza Soltani, Daeyeol Lee, Xiao-Jing Wang. 2006

76 citations recorded by the source corpus.

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In Cognitive Neuroscience

Citations in the corpus

Learning to reinforcement learn (2016, background): “…in neuroscience but only in a narrow sense, according to which meta-learning adjusts scalar hyperparameters such as the learning rate or softmax inverse temperature (Khamassi et al., 2011; 2013; Kobayashi et al., 2009; Lee and Wang, 2009; Schweighofer and Doya, 2003; Soltani et al., 2006).”
A neural circuit model of flexible sensorimotor mapping: learning and forgetting on multiple timescales. (2007, background): “Memory performances are higher with modifiable learning rates than with a static distribution of learning rates because each synapse changes adaptively the rules by which it is modified (metalearning) (Schweighofer and Doya, 2003; Soltani et al., 2006).”
Decision making in recurrent neuronal circuits. (2008, background): “Therefore, a decision maker does not have a goal to play randomly, but simply tries to play at its best, given the environment or other decision agents in an interactive game (Soltani et al., 2006; Lee and Wang, 2008).”
Neuronal Reward and Decision Signals: From Theories to Data. (2015, background): “Dopamine-mediated three-factor Hebbian reinforcement learning may set the decision threshold in caudate nucleus or cortex (554).”
Neural basis of reinforcement learning and decision making. (2012, background): “Nevertheless, value-dependent action selection might also involve attractor dynamics in a similar network of neurons, provided that their input synapses are adjusted in a reward-dependent manner (Soltani & Wang 2006, Soltani et al. 2006).”