Dopamine neurons can represent context-dependent prediction error.

By Hiroyuki Nakahara, Hideaki Itoh, Reiko Kawagoe, Yoriko Takikawa, Okihide Hikosaka. 2004

283 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology

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Citations in the corpus

Theory of Mind: A Neural Prediction Problem (2013, neutral): “…example of a neural “error” code: the rate of firing corresponds to any currently “new” (i.e., previously unpredictable) information about the value of coming reward, not to the actual value of any currently perceived stimulus (Bayer and Glimcher, 2005; Nakahara et al., 2004; Tobler et al., 2005).”
Computational models of reinforcement learning: the role of dopamine as a reward signal (2010, background): “…groups have correlated the activity of DA neurons with the TD error originally proposed by the algorithm (Bayer and Glimcher 2005; Morris et al. 2004; Montague et al. 1996; Nakahara et al. 2004; Roesch et al. 2007; Schultz et al. 1997) and found that it indeed followed the model at many levels.”
Neuronal Reward and Decision Signals: From Theories to Data. (2015, methodology): “Advanced data analyses from these complex tasks reveal inclusion of model-based temporal predictions into the dopamine error response (382) and compatibility with TD models (144).”
Behavioral theories and the neurophysiology of reward. (2006, background): “…neurons (Amador et al. 2000, Apicella et al. 1991, Bowman et al. 1996, Hikosaka et al. 1989, Ljungberg et al. 1992, Markowitsch & Pritzel 1976, Nakamura et al. 1992, Nishijo et al. 1988, Pratt & Mizumori 1998, Ravel et al. 1999, Shidara et al. 1998, Thorpe et al. 1983, Tremblay & Schultz…”
Dopamine signals for reward value and risk: basic and recent data (2010, background): “The error response varies quantitatively with the difference between the received reward value and the expected reward value [18-23].”
Dopamine, uncertainty and TD learning (2005, unknown): “Indeed, there is a continuous updating of predictions such that after a rewarded trial, there is a higher expectation of reward (and thus the next reward incurs a smaller prediction error), and conversely after a non-rewarded trial [18] (see Figure 2a).”
What is reinforced by phasic dopamine signals? (2008, background): “(ii) Striatal neurones are importantly influenced by contextual variables related to the general sensory, metabolic and cognitive state of the animal (Apicella et al., 1997; Nakahara et al., 2004; Samejima et al., 2005; Schultz et al., 1995) (Fig.”
Dopamine in motivational control: rewarding, aversive, and alerting (2010, background): “…even in unconventional environments where rewards paired with a stimulus cause a decrease in the value of that stimulus (Satoh et al., 2003; Nakahara et al., 2004; Bromberg-Martin et al., 2010c) or cause a change in the value of an entirely different stimulus (Bromberg-Martin et al.,…”
Updating dopamine reward signals (2013, background): “Indeed, dopamine error responses follow precisely this scheme, decreasing with increasing numbers of trials since the last reward [12], suggesting that they process modelbased predictions.”
Burst timing-dependent plasticity of NMDA receptor-mediated transmission in midbrain dopamine neurons: A putative cellular substrate for reward learning (2009, unknown): “One way this may occur is through temporal difference (TD) learning (Brown et al., 1999; Nakahara et al., 2004; Niv et al., 2005; Pan et al., 2005, 2008; Suri and Schultz, 1999), a supervised form of learning by which predictions are refined through an iterative process of comparison between an inte”