• S Nakanishi, T Hikida, and S Yawata.
• Department of Systems Biology, Osaka Bioscience Institute, 6-2-4 Furuedai, Suita, Osaka 565-0874, Japan. Electronic address: snakanis@obi.or.jp.
• Neuroscience. 2014 Dec 12; 282: 49-59.

AbstractThe nucleus accumbens (NAc) plays a pivotal role in reward and aversive learning and learning flexibility. Outputs of the NAc are transmitted through two parallel routes termed the direct and indirect pathways and controlled by the dopamine (DA) neurotransmitter. To explore how reward-based and avoidance learning is controlled in the NAc of the mouse, we developed the reversible neurotransmission-blocking (RNB) technique, in which transmission of each pathway could be selectively and reversibly blocked by the pathway-specific expression of transmission-blocking tetanus toxin and the asymmetric RNB technique, in which one side of the NAc was blocked by the RNB technique and the other intact side was pharmacologically manipulated by a transmitter agonist or antagonist. Our studies demonstrated that the activation of D1 receptors in the direct pathway and the inactivation of D2 receptors in the indirect pathway are key determinants that distinctly control reward-based and avoidance learning, respectively. The D2 receptor inactivation is also critical for flexibility of reward learning. Furthermore, reward and aversive learning is regulated by a set of common downstream receptors and signaling cascades, all of which are involved in the induction of long-term potentiation at cortico-accumbens synapses of the two pathways. In this article, we review our studies that specify the regulatory mechanisms of each pathway in learning behavior and propose a mechanistic model to explain how dynamic DA modulation promotes selection of actions that achieve reward-seeking outcomes and avoid aversive ones. The biological significance of the network organization consisting of two parallel transmission pathways is also discussed from the point of effective and prompt selection of neural outcomes in the neural network. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

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