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Analysis of a biologically realistic model for saccade-countermanding tasks

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Wong, Kong-Fatt, Eckhoff, Philip, Holmes, Philip and Cohen, Jonathan (2007) Analysis of a biologically realistic model for saccade-countermanding tasks. In: Society for Neuroscience 2007, San Diego, CA, USA. Society for Neuroscience. 1 pp. [Conference contribution]

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URL: http://www.abstractsonline.com/viewer/viewAbstractPrintFriendly.asp?CKey={6AC38966-4121-4B76-BDAA-5421E4138B16}&SKey={0C56E5A7-8D36-4069-82E7-F0709A27FD9B}&MKey={FF8B70E5-B7F9-4D07-A58A-C1068FDE9D25}&AKey={3A7DC0B9-D787-44AA-BD08-FA7BB2FE9004}

Abstract

In saccade-countermanding tasks, fixation neurons in the frontal eye fields or superior colliculus have high activity during fixation period, sending inhibition to the (build-up) movement neurons. Upon fixation offset and a simultaneous presentation of a Go signal or target, the movement neurons, with response fields where the target lies, exhibit ramping activity. If this ramping activity reaches a certain threshold, a saccade will be made toward the target. However, when a Stop signal appears briefly after target onset, the reactivation of the fixation neurons may suppress the activity of the movement neurons before it reaches threshold, thus producing successful suppression of saccade. The time between onset of the Go and Stop signals is called the stop-signal delay, which is varied by the experimentalists.In this work, we construct a biologically realistic model of saccade-countermanding tasks. The model consists of a population of movement neurons and a population of fixation neurons. Using phase-plane analysis, we show, under certain sets of network parameters, that even if the stop signal occurs before the movement neuronal activity reaches threshold, a saccade can still subsequently occur. We further investigate how inhibitory control of saccades optimizes reward rate, and how this depends on the stop-signal delay, the probability of target presentation, and the probability of having a stop signal within a block of trials.

Item Type:Conference contribution (Poster)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:21441
Deposited By:Dr Kongfatt Wong-Lin
Deposited On:15 Mar 2012 15:06
Last Modified:15 Mar 2012 15:06

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