Raymond JL, Lisberger SG, Mauk MD. The cerebellum: a neuronal learning machine? Science. 1996;272:1126–31.
Article CAS PubMed Google Scholar
Llinás R, Welsh JP. On the cerebellum and motor learning. Curr Opin Neurobiol. 1993;3:958–65.
Ito M, Itō M. The cerebellum and neural control. 1984;(Raven Press)
Marr D. A theory of cerebellar cortex. J Physiol. 1969;202:437–70.
Article CAS PubMed PubMed Central Google Scholar
Doya K. What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? Neural Netw. 1999;12:961–74.
Article CAS PubMed Google Scholar
Wolpert DM, Miall RC, Kawato M. Internal models in the cerebellum. Trends Cogn Sci. 1998;2:338–47.
Article CAS PubMed Google Scholar
Raymond JL, Medina JF. Computational principles of supervised learning in the cerebellum. Annu Rev Neurosci. 2018;41:233–53.
Article CAS PubMed PubMed Central Google Scholar
Caligiore D, Arbib MA, Miall RC, Baldassarre G. The super-learning hypothesis: integrating learning processes across cortex, cerebellum and basal ganglia. Neurosci Biobehav Rev. 2019;100:19–34.
Hull C. Prediction signals in the cerebellum: beyond supervised motor learning. eLife. 2020;9:e54073.
Article PubMed PubMed Central Google Scholar
Sendhilnathan N, Goldberg ME. The mid-lateral cerebellum is necessary for reinforcement learning. 2020. http://biorxiv.org/lookup/doi/10.1101/2020.03.20.000190
Sendhilnathan N, Semework M, Goldberg ME, Ipata AE. Neural correlates of reinforcement learning in mid-lateral cerebellum. Neuron. 2020;106:188-198.e5.
Article CAS PubMed PubMed Central Google Scholar
Sendhilnathan N, Ipata A, Goldberg ME. Mid-lateral cerebellar complex spikes encode multiple independent reward-related signals during reinforcement learning. Nat Commun. 2021;12:6475.
Article CAS PubMed PubMed Central Google Scholar
Larry N, Yarkoni M, Lixenberg A, Joshua M. Cerebellar climbing fibers encode expected reward size. eLife. 2019;8:e46870.
Article PubMed PubMed Central Google Scholar
Carta I, Chen CH, Schott AL, Dorizan S, Khodakhah K. Cerebellar modulation of the reward circuitry and social behavior. Science. 2019;363:eaav0581.
Article CAS PubMed PubMed Central Google Scholar
Heffley W, Hull C. Classical conditioning drives learned reward prediction signals in climbing fibers across the lateral cerebellum. eLife. 2019;8:e46764.
Article PubMed PubMed Central Google Scholar
Wagner MJ, Kim TH, Savall J, Schnitzer MJ, Luo L. Cerebellar granule cells encode the expectation of reward. Nature. 2017;544:96–100.
Article CAS PubMed PubMed Central Google Scholar
Heffley W, et al. Coordinated cerebellar climbing fiber activity signals learned sensorimotor predictions. Nat Neurosci. 2018;21:1431–41.
Article CAS PubMed PubMed Central Google Scholar
Kostadinov D, Beau M, Blanco-Pozo M, Häusser M. Predictive and reactive reward signals conveyed by climbing fiber inputs to cerebellar Purkinje cells. Nat Neurosci. 2019;22:950–62.
Article CAS PubMed PubMed Central Google Scholar
Ohmae S, Medina JF. Climbing fibers encode a temporal-difference prediction error during cerebellar learning in mice. Nat Neurosci. 2015;18:1798–803.
Article CAS PubMed PubMed Central Google Scholar
Therrien AS, Wolpert DM, Bastian AJ. Effective reinforcement learning following cerebellar damage requires a balance between exploration and motor noise. Brain J Neurol. 2016;139:101–14.
Doya K. Complementary roles of basal ganglia and cerebellum in learning and motor control. Curr Opin Neurobiol. 2000;10:732–9.
Article CAS PubMed Google Scholar
King M, Hernandez-Castillo CR, Poldrack RA, Ivry RB, Diedrichsen J. Functional boundaries in the human cerebellum revealed by a multi-domain task battery. Nat Neurosci. 2019;22:1371–8.
Article CAS PubMed PubMed Central Google Scholar
Volkow ND, et al. Expectation enhances the regional brain metabolic and the reinforcing effects of stimulants in cocaine abusers. J Neurosci Off J Soc Neurosci. 2003;23:11461–8.
Grant S, et al. Activation of memory circuits during cue-elicited cocaine craving. Proc Natl Acad Sci U S A. 1996;93:12040–5.
Article CAS PubMed PubMed Central Google Scholar
Ramnani N, Elliott R, Athwal BS, Passingham RE. Prediction error for free monetary reward in the human prefrontal cortex. Neuroimage. 2004;23:777–86.
Article CAS PubMed Google Scholar
Sutton RS, Barto AG. Reinforcement learning: an introduction. 352.
Houk JC, Adams JL, Barto AG. A model of how the basal ganglia generate and use neural signals that predict reinforcement. in Models of information processing in the basal ganglia 249–270 (The MIT Press, 1995).
Rescorla RA, Wagner AR. 3 A theory of Pavlovian conditioning : variations in the effectiveness of reinforcement and nonreinforcement. in 1972
Schultz W, Dayan P, Montague PR. A Neural substrate of prediction and reward. Science. 1997;275:1593–9.
Article CAS PubMed Google Scholar
Kuo S-H. Ataxia. Contin Minneap Minn. 2019;25:1036–54.
Duncan K, Semmler A, Shohamy D. Modulating the use of multiple memory systems in value-based decisions with contextual novelty. J Cogn Neurosci. 2019;1–13. https://doi.org/10.1162/jocn_a_01447
Nicholas J, Daw ND, Shohamy D. Uncertainty alters the balance between incremental learning and episodic memory. eLife. 2022;11:e81679.
Article CAS PubMed PubMed Central Google Scholar
Hariri AR. The emerging importance of the cerebellum in broad risk for psychopathology. Neuron. 2019;102:17–20.
Article CAS PubMed Google Scholar
Bellebaum C, Daum I. Cerebellar involvement in executive control. Cerebellum. 2007;6:184–92.
Beuriat P-A et al. A new insight on the role of the cerebellum for executive functions and emotion processing in adults. Front Neurol. 2020;11
Mannarelli D, et al. The cerebellum modulates attention network functioning: evidence from a cerebellar transcranial direct current stimulation and attention network test study. Cerebellum. 2019;18:457–68.
Litman L, Robinson J, Abberbock T. TurkPrime.com: a versatile crowdsourcing data acquisition platform for the behavioral sciences. Behav Res Methods. 2017;49:433–42.
Hoffman MD, Gelman A. The no-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. 31.
Team SD. Stan Reference Manual.
Vehtari A, Gelman A, Gabry J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat Comput. 2017;27:1413–32.
Kalman RE. A new approach to linear filtering and prediction problems. J Basic Eng. 1960;82:35–45.
Nassar MR, Wilson RC, Heasly B, Gold JI. An approximately Bayesian delta-rule model explains the dynamics of belief updating in a changing environment. J Neurosci. 2010;30:12366–78.
Article CAS PubMed PubMed Central Google Scholar
Collins AGE, Frank MJ. How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis. Eur J Neurosci. 2012;35:1024–35.
Article PubMed PubMed Central Google Scholar
Yoo AH, Collins AGE. How working memory and reinforcement learning are intertwined: a cognitive, neural, and computational perspective. J Cogn Neurosci. 2022;34:551–68.
Hoche F, Guell X, Vangel MG, Sherman JC, Schmahmann JD. The cerebellar cognitive affective/Schmahmann syndrome scale. Brain. 2018;141:248–70.
Chirino-Pérez A, et al. Mapping the cerebellar cognitive affective syndrome in patients with chronic cerebellar strokes. Cerebellum. 2022;21:208–18.
McDougle SD et al. Continuous manipulation of mental representations is compromised in cerebellar degeneration. Brain J Neurol. 2022;awac072.
Comments (0)