These depths were determined by the soma positions and layer boundaries, as described in the Tables S2–S4. The probability for a synapse being placed on a specific compartment was proportional to the relative membrane area of that compartment compared to the total membrane area within the allowed cortical depths, resulting in homogeneous synapse densities with respect to the membrane area of the dendrites. No synapses were placed on the soma. Distributions and number of synapses

onto the dendrites http://www.selleckchem.com/products/INCB18424.html of the neurons were different in simulations with uncorrelated input spike trains or spike trains using the common-input model (Figure 2, Figure 3, Figure 4, Figure 5 and Figure 7) than in the simulations for the laminar network model (Figure 6). See Supplemental Experimental Procedures for details. We considered three different types of input spike-train ensembles: uncorrelated stationary Poisson input, correlated stationary Poisson input generated by a shared-input model, and input from a laminar-network model. Details and parameters are given in Tables S2–S7. We computed the unipolar LFP, i.e., LFP recorded with

reference to a ground electrode positioned far way, using the line-source method described by Holt and Koch (1999) (see also Holt, 1998, for method description). This involves summing over all transmembrane currents weighted inversely with the distance between the recording

electrode and the compartments in the multi-compartment neuron model. The Ulixertinib population LFP was computed by first calculating the contributions from single neurons separately and then summing over these contributions from all cells within the Rebamipide population. Cells were assumed to be surrounded by a purely resistive infinite extracellular medium with conductivity σcondσcond = 0.3 S/m. No filtering was applied to the resulting LFP signal. The amplitude σ of the LFP signal from a population was computed through the variance over time in the 1,000 ms simulation time interval: equation(11) σ2(R)=Et[2(ϕ(t)−Et[ϕ(t)])]σ2(R)=Et[(ϕ(t)−Et[ϕ(t)])2]where Et[⋅]Et[⋅] denotes time average and ϕ=∑ri