scholarly journals Cerebellar damage limits reinforcement learning

Brain ◽  
2016 ◽  
Vol 139 (1) ◽  
pp. 4-7 ◽  
Author(s):  
R. Chris Miall ◽  
Joseph Galea
Keyword(s):  
Reinforcement Learning ◽  
Cerebellar Damage

scholarly journals Effective reinforcement learning following cerebellar damage requires a balance between exploration and motor noise

Brain ◽  
2015 ◽  
Vol 139 (1) ◽  
pp. 101-114 ◽  
Author(s):  
Amanda S. Therrien ◽  
Daniel M. Wolpert ◽  
Amy J. Bastian
Keyword(s):  
Reinforcement Learning ◽  
Motor Noise ◽  
Cerebellar Damage ◽  
Effective Reinforcement

Ultrastorcture of Cerebellar Purkinje Cells in Rats Subjected To Partial Global Cerebral Ischemia

1985 ◽  
Vol 43 ◽  
pp. 674-675
Author(s):  
R.V.W. Dimlich ◽  
M.H. Biros
Keyword(s):  
Cerebral Ischemia ◽  
Purkinje Cells ◽  
Cell Types ◽  
Microscopic Level ◽  
Global Cerebral Ischemia ◽  
Light Microscopic Level ◽  
Cerebellar Damage ◽  
Cerebellar Injury ◽  
Cerebellar Purkinje Cells ◽  
Cell Changes

In severe cerebral ischemia, Purkinje cells of the cerebellum are one of the cell types most vulnerable to anoxic damage. In the partial (forebrain) global ischemic (PGI) model of the rat, Paljärvi noted at the light microscopic level that cerebellar damage is inconsistant and when present, milder than in the telencephalon, diencephalon and rostral brain stem. Cerebellar injury was observed in 3 of 4 PGI rats following 5 minutes of reperfusion but in none of the rats after 90 min of reperfusion. To evaluate a time between these two extremes (5 and 90 min), the present investigation used the PGI model to study the effects of ischemia on the ultrastructure of cerebellar Purkinje cells in rats that were sacrificed after 30 min of reperfusion. This time also was chosen because lactic acid that is thought to contribute to ischemic cell changes in PGI is at a maximum after 30 min of reperfusion.

Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reinforcement Learning ◽  
Iowa Gambling Task ◽  
Bayes Factors ◽  
Gambling Task ◽  
Learning Models ◽  
Reinforcement Learning Models
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