How do biological neural networks learn to predict their environment?

生物神经网络如何学习预测其环境？

• 批准号: 2610330
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2610330
• 关键词: How; do; biological; neural; networks

The most popular theory of how the brain works states that animals learn a predictive model of their environment. Amongst other things, such predictive model allows them to efficiently navigate and to choose the appropriate actions to reach their goals. The neuroscience community strongly believes that the highly complex network of neurons found in the brain is responsible for encoding such a predictive model. However, it is not yet clear how the connectivity structure in this network emerges. At birth, this network is severely underdeveloped and cannot predict much, but as the animal explores more of it's environment, the connectivity structure of such network changes and the animal starts being able to predict and appropriately act upon the environment. The physical mechanisms underlying this adaptation remain poorly understood. In this project, we try to gain a better understanding of such adaptability mechanisms by simulating models of biological neural networks and carefully observing how they learn to predict events in simple synthetic environments.The topic of learning predictive models of the environment is not only relevant to neuroscience but also for any scientific discipline trying to model some phenomena. Machine learning, in particular, has been pursuing such ideas for many years and has produced an extensive mathematical formalism about predictive modeling. Most useful practical models are crafted by hand but a main research goal of machine learning is to produce a single algorithm that can output an arbitrary model from some data. In fact, a said algorithm is believed to be operating in the brain and changing its connections to match the true predictive model of the environment. The most popular algorithm in machine learning right now operates in networks of neurons that are loosely modeled after the brain, also called Artificial Neural Networks (ANNs). Such models, however, do not match observations made by neurophysiology and neuroanatomy and are a very poor approximation of the biological neural networks operating in the brain. Furthermore, ANNs cannot cope very well with temporal correlations, especially for video perception and prediction, and cannot explain how the brain copes so well with them. This also poses a fundamental issue in the field of machine learning and a solution could produce advances in fields like robotics, since it is very hard nowadays to make robots that can learn the predictive and casual (predict with actions) structure of the environment.In this research, we will focus on understanding the brain but also on building predictive systems which can be evaluated under a machine learning framework. We simulate biologically plausible networks in simple synthetic environments and try to understand how they learn the spatio-temporal factors of those simple environments, as well as its causal dynamics. Instead of taking an optimization approach, which changes the connectivity structure based on an objective function, we aim to understand how simple biologically plausible rules perturb the connectivity of the network and how such perturbations aid correct learning. Following with the principles of computational neuroscience we will analyse the models studied under the frameworks of linear algebra and probability theory to come up with understandable hypothesis about the physical behaviour of general biological neural networks.

大脑工作原理的最流行理论指出，动物学习了其环境的预测模型。除其他事项外，这种预测模型使他们能够有效地导航并选择适当的动作以达到目标。神经科学社区坚信，在大脑中发现的高度复杂的神经元网络负责编码这种预测模型。但是，尚不清楚该网络中的连接结构如何出现。出生时，该网络严重欠发达，无法预测太多，但是随着动物探索更多环境，这种网络变化的连通性结构，动物开始能够预测和适当地对环境采取行动。这种适应性的物理机制仍然很少理解。在这个项目中，我们试图通过模拟生物神经网络的模型并仔细观察他们如何在简单的合成环境中进行预测事件来更好地了解这种适应性机制。学习环境的预测模型的主题不仅与神经科学，也适用于任何科学学科，试图建模某些现象。尤其是机器学习多年来一直在追求这种想法，并就预测建模产生了广泛的数学形式主义。最有用的实用模型是手工制作的，但机器学习的主要研究目标是生成一种单个算法，该算法可以从某些数据中输出任意模型。实际上，据信上述算法在大脑中运行并改变其连接以匹配环境的真实预测模型。现在，机器学习中最流行的算法在神经元的网络中运行，这些神经元的网络被宽松地模拟大脑，也称为人工神经网络（ANN）。然而，这样的模型不匹配神经生理学和神经解剖学的观察结果，并且是在大脑中运作的生物神经网络的近似值。此外，ANN不能很好地应对时间相关性，尤其是对于视频感知和预测，也无法解释大脑如何对其进行很好的应对。这也在机器学习领域提出了一个基本问题，解决方案可以在机器人技术等领域产生进步，因为如今很难制作可以学习环境的预测性和休闲（通过行动）结构的机器人。这项研究，我们将专注于理解大脑，还致力于构建可以在机器学习框架下评估的预测系统。我们在简单的合成环境中模拟了生物学上合理的网络，并试图了解它们如何学习这些简单环境的时空因素及其因果动力学。我们的目的不是采用基于目标函数改变连接结构的优化方法，而是旨在了解简单在生物学上合理的规则如何扰动网络的连接以及这种扰动如何帮助正确的学习。遵循计算神经科学的原理，我们将分析在线性代数和概率理论框架下研究的模型，以提出有关一般生物神经网络的身体行为的可理解假设。