Learning mechanisms for perceptual decisions in biological and artificial neural systems

熟能生巧是一种普遍的智慧，也就是说，训练提高了我们解决困难任务和获得新技能的能力。例如，在繁忙的场景中识别物体或在人群中寻找朋友--尽管看起来是无缝的--对大脑提出了巨大的要求，要求大脑1)从杂乱中检测和选择目标，以及(2)区分相似的特征属于相同还是不同的物体。训练和经验提高了我们准确而快速地做出这些感性判断的能力，从而导致了成功的行动。然而，我们的日常经历改变大脑的方式是复杂的，大脑根据以前的经验用来解决新问题的确切机制在很大程度上仍不清楚。在这里，我们建议建立基于最先进的数学算法的模型和人工系统，使我们能够模拟大脑的工作方式，并更好地了解它是如何学习的。在我们的第一项研究中，我们将使用人工智能中开发的一种推理方法，从高分辨率的大脑成像数据中推断出大脑电路的变化，这是我们从高分辨率大脑成像数据中识别混乱场景中物体的能力的基础。这将使我们能够识别大脑回路的某些方面(例如，抑制或兴奋的连接)，当我们训练以改善我们的感知判断时，这些方面会发生变化。在我们的第二项研究中，我们将构建一个大脑视觉系统的模型，它类似于人工神经网络，通过优化其内部连接来从经验中学习。与人工网络不同，我们提出的模型的灵感来自于我们对大脑连接的知识，并整合了大脑电路的关键生物学方面。通过在各种感知判断任务中训练这个网络，我们将对大脑改善其判断能力的大脑机制做出预测。我们使用最先进的磁共振成像技术收集的现有数据来测试和验证这些模型，以跟踪大脑如何在比以前可能的分辨率更高的分辨率下进行学习时改变其功能。此外，我们利用代谢产物磁共振成像的进展来测量GABA，这是大脑用来抑制而不是兴奋神经元的主要神经递质。我们之前已经证明，GABA在学习提高我们的感知技能方面发挥着关键作用。我们将使用开发的模型来了解由于训练而导致的大脑功能变化和神经化学之间的联系。特别是，我们问：a)大脑神经化学的变化与大脑功能的变化有关；b)学习如何改变大脑化学信号(兴奋与抑制)的平衡，以提高大脑在日常任务中执行任务的灵活性和能力。了解这些关键的大脑可塑性过程，反过来将为设计更好的人工系统提供信息。这些系统将允许我们对大脑如何工作做出新的预测，促进我们对大脑如何支持我们的学习和适应一生中环境变化的能力的理解。最后，这些受大脑启发的人工系统可能会改善他们的学习，并为与环境互动能力受损的神经疾病患者推进数字技术(例如脑机接口解决方案)。

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.