A neural model of the frontal eye fields with reward-based learning.
Decision-making is a flexible process dependent on the accumulation of various kinds of information; however, the corresponding neural mechanisms are far from clear. We extended a layered model of the frontal eye field to a learning-based model, using computational simulations to explain the cognitive process of choice tasks. The core of this extended model has three aspects: direction-preferred populations that cluster together the neurons with the same orientation preference, rule modules that control different rule-dependent activities, and reward-based synaptic plasticity that modulates connections to flexibly change the decision according to task demands. After repeated attempts in a number of trials, the network successfully simulated three decision choice tasks: an anti-saccade task, a no-go task, and an associative task. We found that synaptic plasticity could modulate the competition of choices by suppressing erroneous choices while enhancing the correct (rewarding) choice. In addition, the trained model captured some properties exhibited in animal and human experiments, such as the latency of the reaction time distribution of anti-saccades, the stop signal mechanism for canceling a reflexive saccade, and the variation of latency to half-max selectivity. Furthermore, the trained model was capable of reproducing the re-learning procedures when switching tasks and reversing the cue-saccade association.
School of Mathematics, South China University of Technology, Guangzhou, 510640, China.
Weijie
W
Ye
School of Mathematics, South China University of Technology, Guangzhou, 510640, China. Electronic address: mashqliu@scut.edu.cn.
Shenquan
S
Liu
School of Mathematics, South China University of Technology, Guangzhou, 510640, China.
Xuanliang
X
Liu
Center for Computational Systems Biology, The State Key Laboratory of Medical Neurobiology and Institutes of Brain Science, Fudan University, School of Life Sciences, Shanghai, 200433, China.
Yuguo
Y
Yu
D000818:Animals; D002755:Choice Behavior / Q000502:physiology; D003657:Decision Making / Q000502:physiology; D005625:Frontal Lobe / Q000502:physiology; D006801:Humans; D000069550:Machine Learning; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D009949:Orientation / Q000502:physiology; D011597:Psychomotor Performance / Q000502:physiology; D011930:Reaction Time / Q000502:physiology; D012201:Reward; D012438:Saccades / Q000502:physiology; D014794:Visual Fields / Q000502:physiology
Decision-making; Direction-preferred population; Reward-based Hebbian learning; Task switching