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Corticobasal ganglia circuit mechanism for a decision threshold in reaction time tasks
 

Summary: Cortico­basal ganglia circuit mechanism for a decision
threshold in reaction time tasks
Chung-Chuan Lo & Xiao-Jing Wang
Growing evidence from primate neurophysiology and modeling indicates that in reaction time tasks, a perceptual choice is made
when the firing rate of a selective cortical neural population reaches a threshold. This raises two questions: what is the neural
substrate of the threshold and how can it be adaptively tuned according to behavioral demands? Using a biophysically based
network model of spiking neurons, we show that local dynamics in the superior colliculus gives rise to an all-or-none burst
response that signals threshold crossing in upstream cortical neurons. Furthermore, the threshold level depends only weakly on
the efficacy of the cortico-collicular pathway. In contrast, the threshold and the rate of reward harvest are sensitive to, and hence
can be optimally tuned by, the strength of cortico-striatal synapses, which are known to be modifiable by dopamine-dependent
plasticity. Our model provides a framework to describe the main computational steps in a reaction time task and suggests that
separate brain pathways are critical to the detection and adjustment of a decision threshold.
Decision making proceeds from deliberation to choice selection.
Deliberation is a gradual process, usually taking a longer time when
a decision is harder or when more choice options must be considered,
whereas choosing one of the possible alternatives is categorical, often in
the form of an overt action. For decades, psychologists have used
reaction time tasks to probe the process of accumulation of informa-
tion in perceptual decisions. Extensive behavioral analyses have led to
mathematical models in which sensory information is integrated

  

Source: Andrzejak, Ralph Gregor - Departament de Tecnologia, Universitat Pompeu Fabra
Lo, Chung-Chuan - Department of Life Science, National Tsing Hua University
Wang, Xiao-Jing - Kavli Institute for Neuroscience & Department of Neurobiology, Yale University

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences; Physics