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Learning mechanisms for perceptual decisions in biological and artificial neural systems

生物和人工神经系统中感知决策的学习机制

基本信息

批准号：
BB/X013235/1
负责人：
Yashar Ahmadian Tehrani
金额：
$ 25.66万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FX013235%2F1
关键词：
Learning mechanisms perceptual decisions biological

项目摘要

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.

熟能生巧是常识;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，在忙碌的场景中识别物体，或者在人群中找到朋友--看起来似乎天衣无缝--对大脑提出了很高的要求，大脑需要1）从杂乱中检测和选择目标，以及（2）区分相似的特征是属于相同还是不同的物体。训练和经验提高了我们准确而迅速地做出这些感知判断的能力，从而导致成功的行动。然而，我们的日常经验改变大脑的方式是复杂的，大脑根据以前的经验解决新问题的精确机制在很大程度上仍然未知。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，并更好地理解它是如何学习的。在我们的第一项研究中，我们将使用人工智能中开发的推理方法来推断大脑回路的变化，这些变化是我们从高分辨率大脑成像数据中识别杂乱场景中的物体的能力的基础。这将使我们能够识别大脑回路的各个方面（例如，抑制或兴奋连接），当我们训练以提高我们的感知判断时，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，类似于人工神经网络，通过优化其内部连接从经验中学习。与人工网络不同，我们提出的模型受到我们对大脑连接的知识的启发，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，我们将对大脑机制进行预测，这些机制是大脑改善其判断能力的基础。我们使用最先进的磁共振成像技术收集现有数据，对这些模型进行测试和验证，以跟踪大脑如何通过学习以比以前更精细的分辨率改变其功能。此外，我们还利用代谢物MR成像的进展来测量GABA，GABA是大脑用于抑制而不是兴奋其神经元的主要神经递质。我们以前已经表明，GABA在学习提高我们的感知技能中起着关键作用。我们将使用开发的模型来了解大脑功能变化与训练引起的神经化学变化之间的联系。我们尤其要问：a）大脑神经化学的变化与大脑功能的变化有关，B）学习改变了大脑化学信号的平衡（兴奋与抑制），以提高大脑的灵活性和执行日常任务的能力。理解这些关键的大脑可塑性过程反过来将为更好的人工系统的设计提供信息。这些系统将使我们能够对大脑的工作方式做出新的预测，推进我们对大脑如何支持我们在整个生命周期中学习和适应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和推进数字技术（例如脑机接口解决方案），用于神经系统疾病患者，这些患者与环境互动的能力受损。