ABSTRACT Neurons in the brain are submerged into oscillating extracellular Local Field Potential (LFP) created by synchronized synaptic currents. The dynamics of these oscillations is one of the principal characteristics of the brain activity at all levels: from the individual neurons’ spiking to the activity of networks that underlie high-level cognitive processes. However, our interpretation of the LFP structure and functions depend on the techniques that we use for data analyses. The oscillatory nature of LFP motivates using Fourier methods, which have dominated LFP research for decades and currently constitute the only systematic framework for understanding the “brain rhythms.” Yet these methods poorly handle two fundamental attributes of biological signals: noise and non-stationarity, and may therefore obscure the structure of the LFP data and its physiological meaning. We have recently adapted a powerful technique that previously applied to studying complex physical signals (e.g., gravitational waves, magnetic resonances, etc.) for nuanced analysis of the LFP oscillations. By applying this method, we discovered that hippocampal and cortical LFPs recorded in rodents consist of a few frequency-modulated waves, which we call Oscillons. We hypothesize that these objects represent the actual, physical structure of the brain waves and hence may hold keys to better understanding of the circuit mechanisms of learning and memory. Another principal feature of our method is an impartial marker of the noise component, which allows us to identify and remove the “noise shell” from the signal and then to investigate not only the noise itself, but also the interplays between the noise and the regular, oscillatory part of the signal, their interactions with neuronal spiking, etc. Since Alzheimer’s Disease (AD) is characterized by alterations in both the oscillatory and stochastic activity in the hippocampal network, the quest of better understanding of AD-induced pathologies fits ideally the strengths of our approach. Our goal is to use it for studying the circuit mechanisms of AD and to learn to manipulate the network activity through our methodology.