Readers who do not have strong schooling in physics can consult this book chapter for an introduction to key concepts such as ion fluxes, electric fields, electric potentials, and electric currents as well as for definitions of the ohmic, electrodiffusive, and capacitive currents that govern the electrodynamics of brain tissue. Building on the biophysical principles and approximations introduced here, we explain how the electric potential surrounding neurons can be computed based on the principles of current conservation and electroneutrality, and wegive a brief overview of modeling schemes designed to perform such computations on computers. Towards the end of the chapter, we show how the standard theory for computing extracellular potentials relates to Maxwell’s equations and list the approximations that we typically make when we apply these equations in a complex medium like brain tissue.

When a neuron fires an action potential, it causes a rapid fluctuation in the extracellular potential. This fluctuation is referred to as a spike and is normally “visible” only close to the neuron it originates from. Spikes are typically studied experimentally by high-pass filtering the extracellular potential. Here, we use computer simulations and approximate analytical formulas of spikes to explore how the amplitude and shape of spikes depend on various factors such as (i) the morphology of the neuron, (ii) the presence of active ion channels in the neuron’s dendrites, (iii) the part of the neuron (soma vs. dendrite) where the spike is recorded, (iv) the distance from the neuron the spike is recorded, and (v) the location in the neuron that the action potential is initiated. We also briefly discuss how the presence of the electrode can affect spike recordings as well as how to analyze data containing overlapping spikes from several neurons simultaneously.

We here round off a book on biophysical foundations and computational modeling of electric and magnetic signals in the brain. We summarize some key insights from such modeling, and we clear up some common misconceptions about extracellular potentials. We address the main limitations with the standard modeling framework used to compute extracellular potentials, discussing the uncertainty in model parameters and its neglect of ephaptic interactions between active neurons. We identify what we believe are key areas of future applications and give an outlook for future modeling challenges.

The standard two-step scheme for modeling extracellular signals is to first compute the neural membrane currents using multicompartment neuron models (step 1) and next use volume-conductor theory to compute the extracellular potential resulting from these membrane currents (step 2). Here, we present the volume-conductor theory used in step 2. The neural output from step 1 can be represented in terms of (i) a set of point sources, (ii) a set of line sources, (iii) a current-source density, or (iv) one or several current dipoles. We derive equations for the extracellular potential under the approximations (i–iv), discuss the validity and applicability of the different approximations, and explain how they are related. We also discuss how to model the effects that the electrode itself can have on the measured extracellular potential.

The electrocorticographic (ECoG) signal is the electric potential recorded above the cortical surface and reflects the combined activity of large populations of neurons. As ECoG recordings are closer to the neuronal sources than the EEG recordings and further away than LFP recordings, approximations used when modeling LFPs and EEG signals can not a priori be used to model ECoG signals. Here, we give a brief overview of the challenges involved when modeling the ECoG signal and give an overview of previous modeling studies.

The electroencephalographic (EEG) signal is the electric potential recorded on the scalp, and it is believed to originate from the combined activity of large populations of neurons. In forward models of EEG signals, one typically (i) represents neuronal sources in terms of effective current dipoles, (ii) defines a head model, which is a specification of the conductivity profile for the medium between the sources and the recording position (brain tissue, cerebrospinal fluid, skull, scalp), and (iii) uses volume-conductor theory to compute the resulting electric potential at the scalp. In this chapter, we introduce the key theory and computational frameworks for modeling EEG signals. We illustrate how biophysically detailed models of neurons can be reduced to approximate equivalent dipoles, and we discuss further ways to simplify neural simulations in order to reduce the computational cost. Using a combination of computational modeling and analytical approximations, we analyze how various factors are involved in shaping the EEG signal.

The nervous system consists of not only neurons, but also of other cell types such as glial cells. They can be modelled using the same principles as for neurons. The extracellular space (ECS) contains ions and molecules that affect the activity of both neurons and glial cells, as does the transport of signalling molecules, oxygen and cell nutrients in the irregular ECS landscape. This chapter shows how to model such diffusive influences involving both diffusion and electrical drift. This formalism also explains the formation of dense nanometre-thick ion layers around membranes (Debye layers). When ion transport in the ECS stems from electrical drift only, this formalism reduces to the volume conductor theory, which is commonly used to model electrical potentials around cells in the ECS. Finally, the chapter outlines how to model ionic and molecular dynamics not only in the ECS, but also in the entire brain tissue comprising neurons, glial cells and blood vessels.

Potentials are recorded in the clinic using extracellular electrodes. These are near-field potentials as opposed to the far-field potentials recorded during studies of evoked potentials. Signals which approach, pass beneath an extracellular electrode and then continue onwards register a triphasic potential. Signals which do not pass beyond the electrode are characterised by a diphasic potential in which the initial positive wave is followed by a slow return to the baseline. Potentials which arise immediately beneath the recording electrode are diphasic, the initial phase being negative. These properties can be exploited to increase the size of recorded nerve action potentials.