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.

实践是完美的是普遍的智慧。也就是说，培训提高了我们解决艰巨任务并获得新技能的能力。例如，在繁忙的场景中识别对象或在人群中找到朋友，因为它似乎对大脑提出了重大需求，即被称为1）从混乱中检测并选择目标，并且（2）区分类似的功能是否属于到相同或不同的对象。培训和经验提高了我们对这些感知判断进行准确，迅速导致成功行动的能力。然而，我们的日常经历改变大脑的方式是复杂的，并且大脑基于以前的经验来解决新问题的确切机制仍然很大未知。在这里，我们建议基于最先进的数学算法来构建模型和人造系统，以使我们能够模拟大脑的工作原理并更好地理解其学习方式。在我们的第一项研究中，我们将使用在人工智能中开发的推理方法来推断我们从高分辨率的脑成像数据中识别物体在混乱场景中识别物体的能力的基础的变化。这将使我们能够确定大脑电路的各个方面（例如，抑制性或令人兴奋的连接）在我们训练以改善感知判断时会发生变化。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，该模型与人工神经网络相似，通过优化其内部连接从经验中学习。与人工网络不同，我们提出的模型的灵感来自我们对大脑连接的了解，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该网络，我们将对大脑改善其判断能力的大脑机制进行预测。我们测试并验证了这些模型，以使用最先进的磁共振成像收集的现有数据，以追踪大脑如何通过比以前可能更好的分辨率学习来改变其功能。此外，我们利用了代谢物的MR成像的进步来测量大脑用于抑制而不是激发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能力方面起着至关重要的作用。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与神经化学的变化之间的联系。特别是，我们询问：a）大脑神经化学的变化与大脑功能的变化联系，b）学习改变了大脑化学信号的平衡（激发与抑制），以提高大脑的灵活性和在日常任务中执行的能力。了解这些可塑性的关键大脑过程将依次为更好的人工系统设计。这些系统将使我们能够对大脑的运作方式做出新的预测，并促进我们对大脑如何支持我们学习能力和适应整个生命周围环境改变能力的理解。最后，这些受脑启发的人工系统可能会改善其学习和推进数字技术（例如，针对神经系统疾病的患者的大脑计算机界面解决方案），这些患者与环境相互作用的能力受到损害。