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A neural model of sequential movement planning and control of eye movements: Item-Order-Rank working memory and saccade selection by the supplementary eye fields

Author(s)
Silver, Matthew R.; Grossberg, Stephen; Bullock, Daniel; Histed, Mark H.; Miller, Earl K.
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Abstract
How does working memory store multiple spatial positions to control sequences of eye movements, particularly when the same items repeat at multiple list positions, or ranks, during the sequence? An Item-Order-Rank model of working memory shows how rank-selective representations enable storage and recall of items that repeat at arbitrary list positions. Rank-related activity has been observed in many areas including the posterior parietal cortices (PPC), prefrontal cortices (PFC) and supplementary eye fields (SEF). The model shows how rank information, originating in PPC, may support rank-sensitive PFC working memory representations and how SEF may select saccades stored in working memory. It also proposes how SEF may interact with downstream regions such as the frontal eye fields (FEF) during memory-guided sequential saccade tasks, and how the basal ganglia (BG) may control the flow of information. Model simulations reproduce behavioral, anatomical and electrophysiological data under multiple experimental paradigms, including visually- and memory-guided single and sequential saccade tasks. Simulations reproduce behavioral data during two SEF microstimulation paradigms, showing that their seemingly inconsistent findings about saccade latency can be reconciled.
Date issued
2012-02
URI
http://hdl.handle.net/1721.1/102488
Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences; Picower Institute for Learning and Memory
Journal
Neural Networks
Publisher
Elsevier
Citation
Silver, Matthew R., Stephen Grossberg, Daniel Bullock, Mark H. Histed, and Earl K. Miller. “A Neural Model of Sequential Movement Planning and Control of Eye Movements: Item-Order-Rank Working Memory and Saccade Selection by the Supplementary Eye Fields.” Neural Networks 26 (February 2012): 29–58.
Version: Author's final manuscript
ISSN
08936080

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