Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control.

By Mitsuo Kawato, Shinya Kuroda, Nicolas Schweighofer. 2011

58 citations recorded by the source corpus.

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

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Josh Tenenbaum
2. Samuel J. Gershman

In Cognitive Neuroscience

1. Tomer D. Ullman
2. Josh Tenenbaum
3. Samuel J. Gershman
4. Brenden M. Lake
5. Ramitha Sundar
6. Nikolaus Kriegeskorte
7. Pamela K. Douglas
8. Nathaniel D. Daw

In Neuroscience

1. Josh Tenenbaum
2. Samuel J. Gershman
3. Brenden M. Lake
4. Tomer D. Ullman
5. Ramitha Sundar
6. Pamela K. Douglas
7. Nikolaus Kriegeskorte
8. Marco Baroni

Citations in the corpus

• What can the brain teach us about building artificial intelligence? (2016, background): “, 1995; Graybiel, 2005), the cerebellum to implement fast time-scale computations possibly acquired with supervised learning (Wolpert et al., 1998; Kawato et al., 2011), and the limbic brain structures interfacing the brain to the body and generating motivations, emotions, and the value of things (M”