Time: 17:10 - 17:30
Recordings of extracellular electrical, and later also magnetic, brain signals have been the dominant technique for measuring brain activity for decades. The interpretation of such signals is however nontrivial [1], as the measured signals result from both local and distant neuronal activity. In volume-conductor theory the recorded extracellular potentials stem from a complicated sum of contributions from transmembrane currents of neurons near the measurement site. Further, given the same transmembrane currents the contributions to the magnetic field recorded outside the brain can be computed [2]. This allows for the development of computational tools implementing forward models grounded in the biophysics underlying the different measurement modalities [1].
LFPy ([3], LFPy.github.io) incorporated a now well-established scheme for predicting extracellular potentials of individual neurons with arbitrary levels of biological detail. It relies on NEURON ([4], neuron.yale.edu) to compute transmembrane currents of multicompartment neurons which is then used in conjunction with an electrostatic forward model [5]. We have now extended its functionality to populations and networks of multicompartment neurons with concurrent calculations of extracellular potentials and current-dipole moments. The current-dipole moments are used to compute non-invasive measures of neuronal activity, like magnetoencephalographic (MEG) signals [2,6] and, when combined with an appropriate head-model, electroencephalogram (EEG) scalp potentials. One such built-in head-model is the 4-sphere model including the different electric conductivities of brain, cerebral spinal fluid, skull and scalp [6].
The version of LFPy presented here is thus a true multi-scale simulator, capable of simulating electric neuronal activity at the level of cell-membrane dynamics, individual synapses, neurons, networks, extracellular potentials within neuronal populations and macroscopic EEG and MEG signals. The present implementation is equally suitable for execution on laptops and in parallel on high-performance computing (HPC) facilities.
1. Einevoll GT et al. Nat Rev Neurosci 2013, 14:770-785
2. Hämäläinen M et al. Rev Mod Phys 1993, 65:413-487
3. Lindén H et al. Front Neuroinf 2014, 7(41):1-15
4. Hines ML et al. Front Neuroinf 2009, 3(1):1-12
5. Holt G & Koch C. J Comp Neurosci 1999, 6:169-184
6. Nunez PL & Srinivasan R. Electric Fields of the Brain. Oxford University Press 2006
