  Due to rapid advances in multielectrode recording technology, the local field
potential (LFP) has again become a popular measure of neuronal activity in both
basic research and clinical applications. Proper understanding of the LFP
requires detailed mathematical modeling incorporating the anatomical and
electrophysiological features of neurons near the recording electrode, as well
as synaptic inputs from the entire network. Here we propose a hybrid modeling
scheme combining the efficiency of commonly used simplified point-neuron
network models with the biophysical principles underlying LFP generation by
real neurons. The scheme can be used with an arbitrary number of point-neuron
network populations. The LFP predictions rely on populations of
network-equivalent, anatomically reconstructed multicompartment neuron models
with layer-specific synaptic connectivity. The present scheme allows for a full
separation of the network dynamics simulation and LFP generation. For
illustration, we apply the scheme to a full-scale cortical network model for a
$\sim$1 mm$^2$ patch of primary visual cortex and predict laminar LFPs for
different network states, assess the relative LFP contribution from different
laminar populations, and investigate the role of synaptic input correlations
and neuron density on the LFP. The generic nature of the hybrid scheme and its
publicly available implementation in \texttt{hybridLFPy} form the basis for LFP
predictions from other point-neuron network models, as well as extensions of
the current application to larger circuitry and additional biological detail.
