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Examples of QCN research @ Princeton: Computational models

Computational models

Computational models of neural networks for cognition

Architecture of a computational model of the attentional blink

In the Cohen laboratory, neural network models are developed as a way of articulating precise hypotheses about the function of particular brain systems, and their role in cognitive control. This work seeks to bridge between the traditionally disparate levels of analysis of neurophysiology, systems neuroscience, and cognitive psychology. Projects focus on the function of systems considered to be critical for cognitive control, including: a) the role of prefrontal cortex in biasing attention and response selection in posterior structures; b) the role of brainstem dopamine systems in regulating learning and updating of representations in prefrontal cortex; c) the role of the anterior cingulate cortex in monitoring performance, and its influence on adaptations in control; and d) the influence of locus coeruleus and norepinephrine on attentional state. In many cases, modeling work has led to novel predictions about neurophysiolgical mechanisms underlying systems-level function, such as: a) gain control as a mechanism for dopaminergic neuromodulation; b) the role of dopamine in coordinating reinforcement learning and the gating of information into prefrontal cortex; c) the influence of electrotonic coupling on population dynamics within the locus coeruleus; and d) the effects of changes in locus coeruleus physiological state on attentional mode. In other cases, this work has led to novel hypotheses about system level function, such as the response of anterior cinglulate cortex to conflict in processing and its influence on adaptive changes in cognitive control. This work has also predicted, and led to the discovery of new anatomic relationships, such as projections from the anterior cingulate cortex to locus coeruleus.


Nieuwenhuis S, Gilzenrat MS, Holmes BD, & Cohen JD. (2005). The role of the locus coeruleus in mediating the attentional blink: A neurocomputational theory. Journal of Experimental Psychology: General, 134, 291-307.