Health Data Science CDT

健康数据科学 CDT

• 批准号: 2873909
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 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=studentship-2873909
• 关键词: Health; Data; Science; CDT

Understanding how learning and credit assignment (CA) occur in the brain remains an open question. Backpropagation and other techniques used for CA in artificial neural networks, are based on biologically unrealistic requirements, e.g., symmetrical backward and forward synapses weights [1]. Recent advances at the intersection of neuroscience and deep learning (DL), have led to the development of DL models, which incorporate known neural structures and brain mechanisms, enabling CA in a biologically realistic manner [2]. However, the advancement of these models and the bridging of the gap between them and clinical neuroscience data is necessary. Towards this direction, we will develop a DL framework to align biologically plausible neural networks (BPNN) with neuroimaging datasets. Also, we aim to advance BPNNs by introducing mathematical modifications modelling cognitive decline in cortex models [2], and by developing a model capturing the adaptive nature of hippocampal learning, whose disruption is observed in cognitive decline [3]. We will distinguishably map BPNNs modelling cognitive decline to cases', e.g., Alzheimer's, fMRI data. Aim 1: Develop neuroAI-clinical alignment framework. First, we will develop a DL autoencoding framework enabling the comparison between the activation patterns of different networks, as well as, their mapping to real fMRI data. We aim to compare the activation patterns of BPNNs with the ones of other conventional models to explore if they are more meaningful, i.e., resembling more the way the brain encodes information.Aim 2: Test neuroAI-clinical framework: align NN modelling cognitive decline with fMRI data. Then, working upon the alignment between BPNN neural responses and real fMRI data, we will focus on modelling cognitive decline mechanisms as a modification/modulation, or the absence of it, in BPNNs. This will be validated using the DL framework to align the activation patterns of BPNNs to cases' fMRI data and the ones of the modified version of BPNNs to controls' fMRI data. Aim 3: Develop adaptive learning model of hippocampal learning. Finally, we will develop an adaptive learning (ADAM-like) model of hippocampal learning, i.e., introduce a mathematical framework or modulations to architectures used to model the hippocampus, describing the way the hippocampus adapts its learning processes based on experience and changing conditions, e.g., through mediation by hippocampal forward replays. This type of model would capture the dynamic nature of hippocampal learning, e.g., its ability to form, update, and retrieve memories, processes whose disruption occurs in cognitive decline [3]. All the aims above are focused on understanding and modelling learning processes in the brain and their disruption in neurological conditions, such as Alzheimer's disease. This research employs a novel methodology, as it brings different disciplines together by integrating advances in DL models, theoretical neuroscience and clinical data. The alignment of BPNNs activation patterns with clinical data will highlight their validity. The introduction of modulations in cortex networks and the modelling of hippocampal learning that will allow the distinct mapping to cases' fMRI data will be a step towards modelling cognitive decline. This project falls within the intersection of the Biological informatics and Artificial Intelligence technologies EPSRC research areas and such an approach has the enormous potential to link, for the first time, cellular neuroscience and the cognitive decline observed across a range of conditions.[1] Lillicrap, Timothy P., et al. "Backpropagation and the brain." Nature Reviews Neuroscience 21.6 (2020)[2] Greedy, Will, et al. "Single-phase deep learning in cortico-cortical networks" Advances in neural information processing systems 35 (2022)[3] Pedamonti, Dabal, et al. "Hippocampal networks support reinforcement learning in partially observable environments." bioRx

了解学习和学分分配（CA）如何在大脑中发生仍然是一个悬而未决的问题。在人工神经网络中用于CA的反向传播和其他技术，是基于生物学上不现实的要求，例如，对称的向后和向前突触权值[1]。神经科学和深度学习（DL）交叉领域的最新进展导致了深度学习模型的发展，这些模型结合了已知的神经结构和大脑机制，使CA以生物学上真实的方式实现[10]。然而，这些模型的进步和弥合它们与临床神经科学数据之间的差距是必要的。朝着这个方向，我们将开发一个深度学习框架，将生物学上可信的神经网络（BPNN）与神经成像数据集结合起来。此外，我们的目标是通过引入数学修正来模拟皮层模型[2]中的认知衰退，并通过开发一个捕捉海马学习的适应性本质的模型来推进bpnn，海马学习的破坏在认知衰退[3]中被观察到。我们将明显地将bpnn模型的认知衰退映射到病例中，例如阿尔茨海默氏症，fMRI数据。目标1：开发神经人工智能临床对齐框架。首先，我们将开发一个DL自动编码框架，使不同网络的激活模式之间的比较，以及它们映射到真实的fMRI数据。我们的目标是将bpnn的激活模式与其他传统模型的激活模式进行比较，以探索它们是否更有意义，即更像大脑编码信息的方式。目的2：测试神经人工智能临床框架：将神经网络建模认知衰退与fMRI数据相结合。然后，在BPNN神经反应和真实fMRI数据之间的一致性工作中，我们将重点关注认知衰退机制的建模，作为BPNN中的修改/调制或缺乏它。这将通过DL框架进行验证，将bpnn的激活模式与病例的fMRI数据对齐，并将修改后的bpnn的激活模式与控制的fMRI数据对齐。目的3：建立海马学习的适应性学习模型。最后，我们将开发海马体学习的适应性学习（类似adam）模型，即引入用于模拟海马体的数学框架或调制，描述海马体基于经验和不断变化的条件（例如，通过海马体前向重放的调解）适应其学习过程的方式。这种类型的模型将捕捉海马体学习的动态本质，例如，海马体形成、更新和检索记忆的能力，这些过程在认知能力下降时发生中断。上述所有目标都集中在理解和模拟大脑中的学习过程及其在神经系统疾病（如阿尔茨海默病）中的破坏。这项研究采用了一种新颖的方法，因为它通过整合DL模型、理论神经科学和临床数据的进展，将不同的学科结合在一起。bpnn激活模式与临床数据的一致性将突出其有效性。皮层网络调节的引入和海马体学习的建模将允许对病例的功能磁共振成像数据进行不同的映射，这将是模拟认知衰退的一步。该项目属于EPSRC研究领域的生物信息学和人工智能技术的交叉领域，这种方法具有巨大的潜力，首次将细胞神经科学和在一系列条件下观察到的认知能力下降联系起来蒂莫西·P·利利克拉等人。“反向传播和大脑。”Nature Reviews Neuroscience 21.6 (2020) b[2] Greedy, Will, et al.。“皮质-皮质网络中的单相深度学习”，《神经信息处理系统进展》35 (2022)[3]Pedamonti, Dabal，等。海马体网络在部分可观察的环境中支持强化学习