Elsevier

Neural Networks

Volume 15, Issues 4–6, June–July 2002, Pages 561-572
Neural Networks

2002 Special issue
The computational role of dopamine D1 receptors in working memory

https://doi.org/10.1016/S0893-6080(02)00049-7Get rights and content

Abstract

The prefrontal cortex (PFC) is essential for working memory, which is the ability to transiently hold and manipulate information necessary for generating forthcoming action. PFC neurons actively encode working memory information via sustained firing patterns. Dopamine via D1 receptors potently modulates sustained activity of PFC neurons and performance in working memory tasks. In vitro patch-clamp data have revealed many different cellular actions of dopamine on PFC neurons and synapses. These effects were simulated using realistic networks of recurrently connected assemblies of PFC neurons. Simulated D1-mediated modulation led to a deepening and widening of the basins of attraction of high (working memory) activity states of the network, while at the same time background activity was depressed. As a result, self-sustained activity was more robust to distracting stimuli and noise. In this manner, D1 receptor stimulation might regulate the extent to which PFC network activity is focused on a particular goal state versus being open to new goals or information unrelated to the current goal.

Section snippets

The prefrontal cortex and working memory

The prefrontal cortex (PFC) is critically involved in the ability to integrate previously acquired information with recent sensory input to guide action according to a goal state (Fuster, 1997, Goldman-Rakic, 1996). Such processes have been termed working memory and allow forthcoming actions to be planned in a contextually relevant and flexible manner. In animal experiments, working memory is often invoked by introducing a delay between the presentation of a relevant stimulus and a choice

Dopaminergic modulation of prefrontal neurons and working memory

One important source of extrinsic modulation in the PFC is via dopamine. Dopaminergic input to the PFC originates from a small group of mesencephalic neurons (in the ventral tegmentum and substantia nigra) that projects to various cortical areas, flooding them with dopamine in a diffuse manner (Cass and Gerhardt, 1995, Garris et al., 1993, Wightman and Zimmerman, 1990). Dopamine levels in the PFC specifically rise during working memory tasks (Watanabe, Kodama, & Hikosaka, 1997) and dopaminergic

Computational analysis of dopamine action in prefrontal cortex

To assess the impact of dopamine on neural network dynamics, a series of PFC network models with varying levels of abstractness were developed (Durstewitz et al., 1999, Durstewitz et al., 2000a). The basic idea underlying this approach was to start with a model that captured essential electrophysiological characteristics of neural networks probed in behaving monkeys during the performance of working memory tasks and that implemented basic biophysical properties of PFC neurons derived from in

Experimental evidence for the model

Although the behavioral, in vivo and in vitro findings summarized in Section 2 are consistent with the model described here, direct evidence is still lacking. In favor of the model, behavioral findings indicate that animals where the dopaminergic input to the PFC has been lesioned by 6-OHDA are in fact not only more susceptible to distraction (as postulated by the model), they actually show improved performance on tasks requiring high response flexibility (Crofts et al., 2001, Roberts et al.,

Conclusions

Based on the simulation results and the available experimental evidence, the dopamine system might have a somewhat different function than commonly believed. Dopaminergic midbrain neurons typically exhibit a phasic burst in response to a stimulus indicating (or predicting) behaviorally important events or at the onset of a working memory task (Schultz, 1998). Yet it is important to note that although dopamine midbrain neurons respond transiently to a novel or important event, dopamine levels in

Acknowledgements

DD was supported by a research grant (DU 354/2-1) from the Deutsche Forschungsgemeinschaft. The authors would like to thank Dr Terrence Sejnowski for providing the stimulating environment under which many of the ideas presented here were developed, and for his input and discussions. We furthermore thank Dr Kirsten Weibert for helpful comments on the manuscript.

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