TD models of reward predictive responses in dopamine neurons

By Roland E. Suri. 2002

150 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Molecular Biology

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, methodology): “Such a modulation of signals, or gated learning (Abbott, 1990), allows for the regulation and learning of behaviors as shown in numerous computational models (Baxter et al., 1999; Suri et al., 2001; Birmingham, 2001; Alexander and Sporns, 2002; Doya, 2002; Fujii et al., 2002; Suri, 2002; Ziemke and ”
An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning (2015, methodology): “eloped as models of animal learning (e.g., Sutton & Barto 1981, 1990, Klopf 1988, Ludvig, Sutton & Kehoe 2012) and as models of the brain’s reward systems (Schultz, Dayan & Montague 1997, Suri 2002), where they have been particularly in uential (e.g., Niv & Schoenbaum 2002, Dayan &am”
Dopamine: generalization and bonuses (2002, background): “In this paper (see also Suri & Schultz, 1999; Suri, 2002), we study various of these apparently anomalous activations of dopamine cells.”
Neuromodulation in a learning robot: interactions between neural plasticity and behavior (2003, unknown): “These observations have been modeled in the context of temporal difference learning [6,7] and have given rise to the hypothesis that midbrain dopamine neurons encode positive and negative prediction errors, i.”
Neuromodulation and plasticity in an autonomous robot (2002, background): “Both types of responses are found in various neural structures and task contexts (reviewed in Suri, 2002; Suri & Schultz, 2001).”
Metalearning and neuromodulation (2002, background): “Some of these responses can be explained by considering optimistic biases for promoting exploration, generalization in responses, and prediction using internal models (Kakade & Dayan, 2002; Suri, 2002).”
Actor-critic models of the basal ganglia: new anatomical and computational perspectives (2002, methodology): “Such a mechanism could provide another intra-striatal mechanism that controls exploration, in addition to those suggested by Suri et al. (2001). Our model reflects mainly the critic module of the actor– critic framework and consists only of an extremely simplistic actor.”
A unified framework for addiction: Vulnerabilities in the decision process (2008, background): “, Zilli & Hasselmo 2008); and habit learning systems (e.g., Daw 2003; Doya 2000b; Montague et al. 1996; Redish 2004; Suri 2002).”
An Embodied Model of Learning, Plasticity, and Reward (2002, background): “Computational models of the midbrain DA system (Montague, Dayan, & Sejnowski, 1996; Schultz, Dayan, & Montague, 1997; Schultz, 1998; Suri & Schultz, 2001; Suri, 2002) have suggested a strong link between observed dopaminergic responses and the theoretical framework of temporal difference learning (S”
Evolutionary and Computational Advantages of Neuromodulated Plasticity (2008, background): “Similarities between dopaminergic activities and temporal difference signals in reinforcement learning (Sutton and Barto, 1998) were outlined in (Suri, 2002; Dayan and Balleine, 2002; Niv et al., 2005), whereas the computational implications of dopamine in behaviour control and neural disorders are ”
Whole-brain mapping of direct inputs to midbrain dopamine neurons. (2012, unknown): “For example, various computational models of reinforcement learning posit an important role for direct projections of striatal neurons to dopamine neurons in calculating reward prediction error (Doya, 1999; Houk et al., 1996; Joel et al., 2002; Suri, 2002 ).”