Health Data Science CDT

了解学习和学分分配(CA)是如何在大脑中发生的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，例如对称的后向和前向突触权重[1]。神经科学和深度学习交叉领域的最新进展导致了深度学习模型的发展，该模型结合了已知的神经结构和大脑机制，使CA能够以生物现实的方式进行[2]。然而，这些模型的进步和它们与临床神经科学数据之间的差距的弥合是必要的。朝着这个方向，我们将开发一个DL框架来使生物学上可信的神经网络(BPNN)与神经成像数据集保持一致。此外，我们的目标是通过在皮质模型中引入数学修改来模拟认知下降[2]，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，其干扰在认知下降中观察到[3]。我们将可区分地将建模认知下降的BPNN映射到病例，例如阿尔茨海默病、功能磁共振数据。目的1：建立神经AI-临床对齐框架。首先，我们将开发一个DL自动编码框架，能够比较不同网络的激活模式，以及它们到真实fMRI数据的映射。我们的目标是将BPNN的激活模式与其他传统模型进行比较，以探索它们是否更有意义，即更类似于大脑编码信息的方式。目标2：测试神经AI-临床框架：将NN模型的认知下降与fMRI数据保持一致。然后，在BPNN神经反应和真实fMRI数据之间的比对工作中，我们将专注于将认知衰退机制建模为BPNN中的修改/调制或不存在。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的修改版本的激活模式与对照的fMRI数据进行匹配。目的3：建立海马区学习的适应性学习模型。最后，我们将开发一个海马体学习的自适应学习(ADAM样)模型，即引入一个数学框架或调制到用于建模海马体的体系结构，描述海马体基于经验和变化的条件适应其学习过程的方式，例如，通过海马体前向回放的中介。这种类型的模型将捕捉到海马体学习的动态本质，例如，它形成、更新和检索记忆的能力，这些过程发生在认知衰退的中断[3]。上面的所有目标都集中在理解和模拟大脑中的学习过程及其对阿尔茨海默病等神经疾病的破坏。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步，将不同的学科结合在一起。BPNN激活模式与临床数据的对准将突出它们的有效性。在大脑皮层网络中引入调制，并对海马体学习进行建模，使其能够清楚地映射到病例的fMRI数据，这将是对认知能力下降进行建模的一步。该项目属于生物信息学和人工智能技术EPSRC研究领域的交叉，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能力下降联系起来。“反向传播和大脑。”《自然评论》神经科学21.6(2020)[2]贪婪，威尔等人。《皮层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等人。“在部分可观察的环境中，海马区网络支持强化学习。”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