Learning mechanisms for perceptual decisions in biological and artificial neural systems

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It is common wisdom that practice makes perfect; that is, training improves our ability to solve difficult tasks and acquire new skills. For example, recognising objects in busy scenes or finding a friend in the crowd-seamless as it may seem- poses significant demands on the brain that is called to 1) detect and select targets from clutter, and (2) discriminate whether similar features belong to the same or different objects. Training and experience improve our ability to make these perceptual judgements accurately and rapidly resulting in successful actions. Yet, the way in which our everyday experiences change the brain is complex and the precise mechanisms that the brain employs to solve new problems based on previous experience remain largely unknown. Here we propose to build models and artificial systems based on state-of-the-art mathematical algorithms that allow us to simulate the workings of the brain and understand better how it learns. In our first study, we will use an inference method developed in artificial intelligence to infer changes in the brain circuits underlying our ability to recognise objects in cluttered scenes, from high-resolution brain imaging data. This will allow us to identify aspects of the brain circuits (for example, suppressive or exciting connections) that change when we train to improve our perceptual judgments. In our second study, we will construct a model of the brain's visual system that, similarly to artificial neural networks, learns from experience by optimising its internal connections. Unlike artificial networks, our proposed model is inspired by our knowledge of the brain's connections and integrates key biological aspects of brain circuitry. By training this network in various perceptual judgement tasks we will make predictions for the brain mechanisms that underlie the brain's ability to improve its judgements. We test and validate these models against existing data that we collected using state-of-the-art magnetic resonance imaging to trace how the brain changes its functions with learning at much finer resolution than previously possible. Further, we have exploited advances in MR imaging of metabolites to measure GABA, the primary neurotransmitter that the brain uses for suppressing rather than exciting its neurons. We have previously shown that GABA plays a critical role in learning to improve our perceptual skills. We will use the developed models to understand the link between changes in the brain's function and neurochemistry due to training. In particular, we ask how: a) changes in the brain's neurochemistry link with changes in brain function, b) learning alters the balance in the brain's chemical signals (excitation vs. inhibition) to boost the brain's flexibility and capacity to perform in everyday tasks. Understanding these key brain processes of plasticity will, in turn, inform the design of better artificial systems. These systems will allow us to make new predictions about how the brain works, advancing our understanding of how the brain supports our ability to learn and adapt to change in our environment across the lifespan. Finally, these brain-inspired artificial systems may improve in their learning and advance digital technologies (e.g. brain-computer interface solutions) for patients with neurological disorders that are impaired in their ability to interact with the environment.