A recurrent network mechanism of time integration in perceptual decisions Kong-Fatt Wong and Xiao-Jing Wang The study investigates the cellular and circuit basis of time integration in perceptual decisions, focusing on the role of NMDA receptors in slow time integration. The authors developed a simplified two-variable version of a biophysically realistic cortical network model of decision making. This model shows that slow time integration can be achieved robustly if excitatory reverberation is primarily mediated by NMDA receptors. The model with only fast AMPA receptors at recurrent synapses produces decision times that are not comparable with experimental observations. The study found two distinct modes of network behavior, in which decision computation by winner-take-all competition is instantiated with or without attractor states for working memory. Decision process is closely linked to the local dynamics, in the "decision space" of the system, in the vicinity of an unstable saddle steady state that separates the basins of attraction for the two alternative choices. This picture provides a rigorous and quantitative explanation for the dependence of performance and response time on the degree of task difficulty, and the reason for which reaction times are longer in error trials than in correct trials as observed in the monkey experiment. The reduced two-variable neural model offers a simple yet biophysically plausible framework for studying perceptual decision making in general. The study shows that the ramp-to-threshold dynamics is reminiscent of the "diffusion" model, a popular mathematical model used in the study of reaction time tasks. The diffusion model consists of a one-dimensional system that integrates over time the difference between two noisy stimulus inputs. When it reaches one of two thresholds, the choice is made and the decision time is recorded. An important characteristic of the model is that it integrates sensory evidence without any "leakage" (i.e., it is a perfect integrator). The diffusion model fits well to many psychophysical data, is mathematically tractable for analysis, and thus has been a benchmark for other models. Furthermore, it has been shown that the diffusion model can be approximately realized by "connectionist models," which may include a leak term; time integration becomes nearly perfect when fine-tuning of parameters cancels out the leakage by network recurrent dynamics. Although the diffusion-type model has also been applied to fit neuronal as well as behavioral data, its abstract nature does not permit a direct exploration of the cellular and circuit mechanisms that give rise to long integration times in decision processes. In contrast, Wang (2002) investigated a biophysically based cortical microcircuit model for decision making. The model is endowed with slow excitatory reverberation between spiking neurons that produces attractor dynamics, and recurrent feedback inhibition via interneurons underlies winner-take-all behavior. The model replicated most of the psychophysical and physiological results in Shadlen and Newsome (2001) and Roitman and Shadlen (2002). However, theA recurrent network mechanism of time integration in perceptual decisions Kong-Fatt Wong and Xiao-Jing Wang The study investigates the cellular and circuit basis of time integration in perceptual decisions, focusing on the role of NMDA receptors in slow time integration. The authors developed a simplified two-variable version of a biophysically realistic cortical network model of decision making. This model shows that slow time integration can be achieved robustly if excitatory reverberation is primarily mediated by NMDA receptors. The model with only fast AMPA receptors at recurrent synapses produces decision times that are not comparable with experimental observations. The study found two distinct modes of network behavior, in which decision computation by winner-take-all competition is instantiated with or without attractor states for working memory. Decision process is closely linked to the local dynamics, in the "decision space" of the system, in the vicinity of an unstable saddle steady state that separates the basins of attraction for the two alternative choices. This picture provides a rigorous and quantitative explanation for the dependence of performance and response time on the degree of task difficulty, and the reason for which reaction times are longer in error trials than in correct trials as observed in the monkey experiment. The reduced two-variable neural model offers a simple yet biophysically plausible framework for studying perceptual decision making in general. The study shows that the ramp-to-threshold dynamics is reminiscent of the "diffusion" model, a popular mathematical model used in the study of reaction time tasks. The diffusion model consists of a one-dimensional system that integrates over time the difference between two noisy stimulus inputs. When it reaches one of two thresholds, the choice is made and the decision time is recorded. An important characteristic of the model is that it integrates sensory evidence without any "leakage" (i.e., it is a perfect integrator). The diffusion model fits well to many psychophysical data, is mathematically tractable for analysis, and thus has been a benchmark for other models. Furthermore, it has been shown that the diffusion model can be approximately realized by "connectionist models," which may include a leak term; time integration becomes nearly perfect when fine-tuning of parameters cancels out the leakage by network recurrent dynamics. Although the diffusion-type model has also been applied to fit neuronal as well as behavioral data, its abstract nature does not permit a direct exploration of the cellular and circuit mechanisms that give rise to long integration times in decision processes. In contrast, Wang (2002) investigated a biophysically based cortical microcircuit model for decision making. The model is endowed with slow excitatory reverberation between spiking neurons that produces attractor dynamics, and recurrent feedback inhibition via interneurons underlies winner-take-all behavior. The model replicated most of the psychophysical and physiological results in Shadlen and Newsome (2001) and Roitman and Shadlen (2002). However, the