Alzheimer’s disease (AD), the leading cause of dementia, progressively impairs cognitive functions, particularly memory. Amyloid-beta plaques, a key hallmark of AD, contribute to neural dysfunction, yet their impact on hippocampal circuits, essential for memory, is not fully understood. Translating animal model findings into human therapies has been challenging due to limited knowledge of AD’s effect on hippocampal-cortical networks. This thesis investigates amyloid-beta pathology’s influence on spatial memory circuits in the 5xFAD mouse model. Using two-photon calcium imaging, serial two-photon tomography (STPT), and neural manifold learning (NML), this research explores how plaques impact hippocampal network structure and function during memory encoding and recall. By mapping neural activity at single-cell resolution, the study compares wild-type and AD mouse models, revealing how plaques affect neural excitability, spatial coding, and network stability. Key findings show disrupted hippocampal circuit dynamics in AD mice, evidenced by abnormal neural excitability and altered spatial memory patterns. NML techniques highlight differences in the neural manifolds between healthy and AD-affected mice, with AD mice showing reduced stability and decodability of memory-related representations. These changes correlate with spatial and working memory deficits, connecting circuit disruptions to cognitive impairment. This study enhances understanding of amyloid-beta’s network-level effects, bridging molecular pathology with neural circuit dynamics. It provides a foundation for developing targeted therapies and early diagnostic biomarkers for AD. Additionally, it demonstrates neural manifold analysis as a promising tool to quantitatively assess circuit dysfunction, supporting future research and clinical trials aimed at alleviating AD’s impact.