Beta-amyloid plaque excess is one of the main markers of Alzheimer.s disease (AD). AD can cause major memory deficits and cognitive decline. Experiments using EEG and LFP recordings have found an increase in the brain.s theta (4-7 Hz) band power as AD progresses. Despite intense research, the biophysical mechanisms underlying this change remain unclear. This computational study will focus on the effects of ionic currents potentially affected by beta-amyloid. Our model consists of Hodgkin- Huxley type neurons, with one excitatory (pyramidal) and three inhibitory (basket, OLM and MSGABA) neuronal populations based on electrophysiological properties of neurons in the hippocampal CA1 and medial septal regions. We vary four ionic currents that are potentially affected by beta-amyloid, and found only one current (IA) to significantly change the network.s theta band power. Interestingly, we found that as IA decreases (due to beta-amyloid blockage), the network's theta band power first increases before it decreases. We use two approaches to understand the mechanisms underlying this trend. To understand the mechanisms underlying this increase in theta band power, we reduce the pyramidal neuronal model to a lower dimension for dynamical systems analysis. For the decrease in theta band power, we systematically study the network in a noiseless and heterogeneous-free condition. Our work accounts for heterogeneity in some experimental findings, and suggests possible neural mechanisms for theta rhythm changes that can be experimentally verified. This is joint work with Xin Zou, Damien Coyle and Liam Maguire at the Intelligent Systems Research Centre, University of Ulster.