A mouse learns to spot a faint visual pattern, and scientists watch its brain rewire from the very first mistake to expert performance. This matters because the brain does not simply absorb rewards—it tests strategies, discards failures, and discovers solutions under uncertainty. Decades of research have studied dopamine and its targets in the striatum and frontal cortex, but only after learning was already underway. No one has tracked these circuits from the first trial, when an animal is still guessing. This project fills that gap by combining new behavioural tasks with tools that measure and manipulate neural activity across weeks of learning. If successful, the work will reveal how dopamine signals shift as an animal transitions from random exploration to reliable decision-making. It will also show whether dopamine in different brain regions plays distinct causal roles—knowledge that could eventually inform treatments for conditions where learning under uncertainty breaks down, such as Parkinson’s disease, addiction, or schizophrenia. This is fundamental science. It does not promise an immediate application, but understanding how the brain discovers strategies under uncertainty could, over time, reshape how we design artificial learning systems or rehabilitate damaged neural circuits.
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How does the brain learn to make efficient decisions in an uncertain world? A hallmark of biological learning is strategy discovery: over long periods of time, animals can transition through a range of behaviors on their way from naïve to expert performance. Decades of research have highlighted the role of midbrain dopamine (DA) neurons as well as major DA-receiving brain regions, striatum and frontal cortex, in learning. However, previous studies did not probe these neural circuits from the very first trial of the task, while animals test and select among possible strategies for gaining reward under perceptual uncertainty. Moreover, past studies often examined these neural circuits at a small scale and in isolation, and could not systematically investigate how neural signals across these brain regions underlie learning. A large gap thus remains between our understanding of neural computations and the long-term process of strategy discovery under uncertainty. This proposal will combine novel behavioral tasks and advanced neural circuit tools to fill this gap. We will longitudinally measure and manipulate neural signals from naïve to expert performance in mice learning a visual decision task that admits multiple behavioral strategies, and will use computational tools to formalize the relation between neural activity and learning dynamics. The work will address these questions: 1. How do DA neural signals develop during learning under uncertainty, and how do they relate to behavioral strategies? 2. What does DA release across striatum and frontal cortex encode during learning, and does DA in different brain regions play distinct causal roles in learning? 3. How do neural signals across striatum and frontal cortex develop and relate to behavioral strategies during learning? This work will rectify the paucity of data on the neural bases of learning, generating a step change in our understanding of neural circuits regulating learning under perceptual uncertainty.
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