Complementary roles of basal ganglia and cerebellum in learning and motor control
By Kenji Doya
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Michael J Frank
- Kenji Doya
- Karl J. Friston
- Gianluca Baldassarre
- Peter S. Dayan
- Nathaniel D. Daw
- Jun Zhu
- Matthew Botvinick
Citations in the corpus
- A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “In contrast, the neocortex-dependent memories are typically long-term and generalizable, where the representations can be progressively acquired in an unsupervised fashion [102], [103] and exhibit orthogonality [119], [463], consistent with the advantages of self-supervised and pretrained representa”
- Single Nigrostriatal Dopaminergic Neurons Form Widely Spread and Highly Dense Axonal Arborizations in the Neostriatum (2009, background): “Thus, it seems most likely that striosomes contribute to the production of a dopamine signal by SNc neurons (Schultz, 1998; Doya, 2000).”
- Cognition, motivation, emotion and action: a dynamic and vulnerable interdependence (2004, background): “It appears that incoming signals trigger the nigrostriatal DA system which modulates the efficacy with which candidate signals from the cortex are able to gain expression in behaviour, a learning function (Doya, 2000).”
- Dynamic Dopamine Modulation in the Basal Ganglia: A Neurocomputational Account of Cognitive Deficits in Medicated and Nonmedicated Parkinsonism (2005, background): “Reinforcement learning accounts of DA in the BG have been suggested by others (e.g., Doya, 2000; Suri & Schultz, 1999), and allow Frank 65 D ow nloaded from http://direct.m it.edu/jocn/article-pdf/17/1/51/1757052/0898929052880093.pdf by guest on 16 June 2021 f lexible learning of rewarding and…”
- Optimal feedback control and the neural basis of volitional motor control (2004, background): “The brain does not implement the formal mathematical methods that are available to compute gains of optimal feedback controllers, but how neural networks create and learn these properties is an interesting and important proces”
- Perspectives and problems in motor learning. (2001, background): “There is evidence that different neural structures might be particularly adapted for different computational forms of learning 26 .”
- Metalearning and neuromodulation (2002, background): “A different mechanism for temporal difference computation is possible if we consider an alternative form of the TD error (Doya, 2000c): which is the sum of the reward, immediate inhibition, and temporal difference of the value functions (Figure 6 b).”
- Recent Advances in Physical Reservoir Computing: A Review (2018, background): “In terms of learning styles, the cerebellum and the basal ganglia can be characterized by supervised learning and reinforcement learning, respectively, which are complementary to each other (Doya, 2000).”
- Habits, rituals, and the evaluative brain. (2008, background): “In line with this possibility, striosomes may represent state value in actor-critic architectures (Doya 2000, 2002) and thus may be critical components of striatum-based learning circuits.”
- Dopamine-dependent plasticity of corticostriatal synapses (2002, background): “Recent electrophysiological studies of the basal ganglia have provided the framework for a number of computational models of reward-related learning (Doya, 2000; Montague, Dayan, & Sejnowski, 1996; Suri & Schultz, 1999).”
- The Cyber Rodent Project: Exploration of Adaptive Mechanisms for Self-Preservation and Self-Reproduction (2005, methodology): “The RL framework was conceived as a model of animal behavioral learning and it has been extremely helpful in understanding the functions of the basal ganglia and neuromodulators such as dopamine (Schultz, Dayan & Montague, 1997; Doya, 2000, 2002).”
- The basal ganglia: learning new tricks and loving it. (2005, background): “For example, in the motor learning field, the cerebellum has been associated with supervised learning and the basal ganglia with reinforcement learning [55], the cerebellum has been credited with developing internal models of motor action space.”
- The basal ganglia communicate with the cerebellum (2010, background): “of the basal ganglia and supervised learning in the case of the cerebellum (29, 30).”