Title |
Internally generated sequences in learning and executing goal-directed behavior
|
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Published in |
Trends in Cognitive Sciences, August 2014
|
DOI | 10.1016/j.tics.2014.06.011 |
Pubmed ID | |
Authors |
Giovanni Pezzulo, Matthijs A.A. van der Meer, Carien S. Lansink, Cyriel M.A. Pennartz |
Abstract |
A network of brain structures including hippocampus (HC), prefrontal cortex, and striatum controls goal-directed behavior and decision making. However, the neural mechanisms underlying these functions are unknown. Here, we review the role of 'internally generated sequences': structured, multi-neuron firing patterns in the network that are not confined to signaling the current state or location of an agent, but are generated on the basis of internal brain dynamics. Neurophysiological studies suggest that such sequences fulfill functions in memory consolidation, augmentation of representations, internal simulation, and recombination of acquired information. Using computational modeling, we propose that internally generated sequences may be productively considered a component of goal-directed decision systems, implementing a sampling-based inference engine that optimizes goal acquisition at multiple timescales of on-line choice, action control, and learning. |
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Geographical breakdown
Country | Count | As % |
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United States | 4 | 27% |
Japan | 2 | 13% |
United Kingdom | 2 | 13% |
Indonesia | 1 | 7% |
Germany | 1 | 7% |
Turkey | 1 | 7% |
Unknown | 4 | 27% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 8 | 53% |
Scientists | 6 | 40% |
Science communicators (journalists, bloggers, editors) | 1 | 7% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 10 | 2% |
France | 5 | 1% |
Germany | 2 | <1% |
Netherlands | 2 | <1% |
Italy | 2 | <1% |
Spain | 2 | <1% |
Canada | 2 | <1% |
Switzerland | 1 | <1% |
South Africa | 1 | <1% |
Other | 1 | <1% |
Unknown | 436 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 121 | 26% |
Researcher | 96 | 21% |
Student > Master | 53 | 11% |
Student > Bachelor | 39 | 8% |
Professor | 27 | 6% |
Other | 73 | 16% |
Unknown | 55 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Neuroscience | 112 | 24% |
Psychology | 92 | 20% |
Agricultural and Biological Sciences | 91 | 20% |
Computer Science | 27 | 6% |
Medicine and Dentistry | 13 | 3% |
Other | 44 | 9% |
Unknown | 85 | 18% |