One of the central goals in neuroscience is to understand the function of neural circuits in the brain and how they are related to behavior. As a result, a great effort has been dedicated to developing techniques which enable the recording of the brain’s electrical activity at different spatial scales. This consists of measures ranging from the localized spiking activity of individual neurons and/ or their local eld potential (LFP), all the way up to more global measures such as the electroencephalogram (EEG). When recorded in a well-controlled experimental paradigm, the assumption is that modulations in these measured signals reect changes in cortical processing. This information can then be used to better understand the neural mechanisms underlying specic cognitive functions (e.g., memory, learning, perception, etc.). However, each recording method is limited in the sense that the signals in which they measure are not representative of all the processes that occur in the brain, making it difcult to isolate specic neural events. The complex structural and functional architecture of the brain can thus lead to a biased interpretation of such signals depending on the manner in which they are acquired (e.g., EEG vs. LFP). Knowledge of the physiological properties giving rise to neural signals is therefore essential for better interpreting the neural correlates of a particular cognitive function, and reconciling results obtained across different recording modalities.