Dopaminergic regulation of spatial learning

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Summary In neural networks that store information in their connection weights, there is a tradeoff between sensitivity and stability. Connections must be plastic to incorporate new information, but if they are too plastic, stored information can be corrupted. Therefore, it would be useful if learning rates in the brain were regulated by a “when-to-learn” signal that varies with the current availability of new information. In reward learning, dopamine is known to serve this function, by rapidly upregulate synaptic plasticity in response to reward prediction errors. The overarching hypothesis of this proposal is that dopamine also provides a when-to-learn signal for spatial learning. During spatial learning, new information is generally available when an organism is moving through space. Thus, we hypothesize that spatial learning is modulated by dopamine release that is specifically linked to active movements. This idea is attractive because it can provide an explanation for why so many dopamine neurons are time-locked to movements. This proposal outlines three projects, all focusing on spatial learning in the central complex, the primary center for spatial navigation in the Drosophila brain. In each project, there is anatomical evidence from the Drosophila connectome that implies a role for dopamine neurons. Moreover, in each project, there is already evidence that the dopamine neurons in question are active when the fly is locomoting. This motivates our hypothesis that dopamine links movement to spatial learning. Although these projects are linked conceptually, they each focus on a distinct dopamine cell type, and a distinct form of spatial learning. First, we will determine how dopamine modulates learning about spatial position cues in the head direction system. Second, we will investigate the hypothesis that dopamine modulates learning about rotational velocity cues in the head direction system. Third, we will investigate the hypothesis that a feedback circuit integrates information over time to discount the influence of environmental wind shifts on head direction neurons. In all three projects, we use connectome analyses and computational modeling to generate testable predictions about specific networks in the brain. Then, we test these predictions using in vivo calcium imaging and/or electrophysiology as flies navigate in virtual reality environments. Our results should shed light on the fundamental mechanisms underlying navigation behaviors in all complex species, including ring attractor networks, Hebbian learning rules, and feedback loops. Broadly speaking, we think that dopamine provides a control knob for modulating these mechanisms up or down. As such, we see dopaminergic neurons as an entry point for an integrative understanding of network dynamics during complex cognitive processes.