Health Data Science CDT

理解学习和学分分配（CA）如何在大脑中发生仍然是一个悬而未决的问题。人工神经网络中用于CA的反向传播和其他技术基于生物学上不切实际的要求，例如，对称的向后和向前突触权重[1]。神经科学和深度学习（DL）交叉领域的最新进展导致了DL模型的发展，该模型结合了已知的神经结构和大脑机制，以生物学上真实的方式实现了CA [2]。然而，这些模型的进步以及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DL框架，将生物学上合理的神经网络（BPNN）与神经成像数据集对齐。此外，我们的目标是通过引入对皮层模型中的认知下降进行建模的数学修改来推进BPNN [2]，并通过开发一种捕捉海马学习的自适应性质的模型来推进BPNN，海马学习的中断在认知下降中被观察到[3]。我们将合理地将BPNN建模认知衰退映射到案例，例如，老年痴呆症功能磁共振成像数据目标1：开发神经AI-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，目标2：测试神经AI-临床框架：将NN建模认知衰退与fMRI数据对齐。然后，在BPNN神经反应和真实的fMRI数据之间的对齐工作，我们将专注于建模认知下降机制作为修改/调制，或没有它，在BPNN。这将使用DL框架进行验证，以将BPNN的激活模式与病例的fMRI数据以及BPNN的修改版本与对照的fMRI数据进行比对。目标3：开发海马学习的自适应学习模型。最后，我们将开发一个海马学习的自适应学习（ADAM样）模型，即，将数学框架或调制引入用于对海马体进行建模的架构，描述海马体基于经验和变化的条件来适应其学习过程的方式，例如，通过海马体前向重放的调解。这种类型的模型将捕捉海马学习的动态性质，例如，它形成、更新和恢复记忆的能力，这些过程的中断发生在认知衰退中[3]。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）中的中断。这项研究采用了一种新的方法，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学科结合在一起。BPNN激活模式与临床数据的对齐将突出其有效性。在皮层网络中引入调制和海马学习的建模将允许对病例的fMRI数据进行不同的映射，这将是朝着建模认知衰退迈出的一步。该项目福尔斯生物信息学和人工智能技术EPSRC研究领域的交叉领域，这种方法具有巨大的潜力，首次将细胞神经科学与在一系列条件下观察到的认知衰退联系起来。[1]Lillicrap，Timothy P.，Backpropagation and the brain.“Nature Reviews Neuroscience 21.6（2020）[2] Greedy，Will，et al.“Single-phase deep learning in corticosteroid networks”Advances in neural information processing systems 35（2022）[3] Pedamonti，Dabal，et al.“Hippocampus networks support reinforcement learning in partially observable environments.“bioRx

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