  A method is presented for the reduction of morphologically detailed
microcircuit models to a point-neuron representation without human
intervention. The simplification occurs in a modular workflow, in the
neighborhood of a user specified network activity state for the reference
model, the "operating point". First, synapses are moved to the soma, correcting
for dendritic filtering by low-pass filtering the delivered synaptic current.
Filter parameters are computed numerically and independently for inhibitory and
excitatory input using a Green's function approach. Next, point-neuron models
for each neuron in the microcircuit are fit to their respective morphologically
detailed counterparts. Here, generalized integrate-and-fire point neuron models
are used, leveraging a recently published fitting toolbox. The fits are
constrained by currents and voltages computed in the morphologically detailed
partner neurons with soma corrected synapses at three depolarizations about the
user specified operating point. The result is a simplified circuit which is
well constrained by the reference circuit, and can be continuously updated as
the latter iteratively integrates new data. The modularity of the approach
makes it applicable also for other point-neuron and synapse models. The
approach is demonstrated on a recently reported reconstruction of a neocortical
microcircuit around an in vivo-like working point. The resulting simplified
network model is benchmarked to the reference morphologically detailed
microcircuit model for a range of simulated network protocols. The simplified
network is found to be slightly more sub-critical than the reference, with
otherwise good agreement for both quantitative and qualitative validations.
