Computational principles of learning in the neocortex and hippocampus

By Randall C. O’Reilly, J. Rudy. 2000

206 citations recorded by the source corpus.

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

How researchers see this paper

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

In Cell Biology

1. Randall C. O’Reilly
2. Lynn Nadel
3. Paul W. Frankland
4. Christopher Kanan

In Cellular and Molecular Neuroscience

1. Michael J Frank
2. Lynn Nadel
3. Randall C. O’Reilly
4. Paul W. Frankland
5. B. Bontempi
6. B. Loughry
7. Stefan Wermter
8. Jose L. Part

In Cognitive Neuroscience

1. Michael J Frank
2. Randall C. O’Reilly
3. German Ignacio Parisi
4. Christopher Kanan
5. Stefan Wermter
6. Lynn Nadel
7. Matthew Botvinick
8. B. Bontempi

Citations in the corpus

• Continual Lifelong Learning with Neural Networks: A Review (2018, background): “, driven by the maximization of the learning progress (Oudeyer et al. (2007) and Schmidhuber (1991), see Fig.”
• Naps Promote Abstraction in Language-Learning Infants (2006, background): “A third is that sleep contributes to an abstraction process by protracting the learning-dependent processing necessary for extraction of a more general pattern (e.g., see McNaughton et al., 2003; O’Reilly & Rudy, 2000).”
• Interactions between frontal cortex and basal ganglia in working memory: A computational model (2001, background): “There has been considerable discussion along these lines of the ways in which the neural structure of the hippocampus is optimized for subserving a particular kind of weight-based memory (e.g., McClelland, McNaughton, & O’Reilly, 1995; O’Reilly & McClelland, 1994; O’Reilly & Rudy, 2000, 2001).”
• The organization of recent and remote memories (2005, background): “This has led to the idea that the hippocampus is a fast learner and the cortex is a slow on”