If, on the other hand, response variability in LGN relay cells were perfectly correlated, then variability in a simple cell’s Vm responses would be the same as in its presynaptic LGN cells. Simultaneous recording in groups of nearby LGN cells shows that the correlation coefficient for variability between pairs of cells falls in the range of 0.25, with little variation as a function of stimulus contrast (Sadagopan and Ferster, 2012). With measurements

of LGN response variability, its dependence on contrast, and its cell-to-cell correlation, it is possible to construct a highly constrained feedforward model of a V1 simple cell. Sadagopan and Ferster (2012) simulated simple cells with input from between 8 and 32 individual LGN cells arranged in

two subfields with aspect ratios between 2 and 4. Each presynaptic LGN cell generated a change in conductance in the model simple cell in proportion to its spike rate, mTOR inhibitor Everolimus after application of rate-dependent synaptic depression (Boudreau and Ferster, 2005). The simple cell’s resting conductance and the peak conductance evoked by an optimal stimulus were taken from intracellular measurements (Anderson et al., 2000). Orientation tuning curves for the mean Vm response in the modeled simple cell are shown in Figure 4E at two different contrasts (solid curves), along with the responses on individual trials (points). The model’s response variability compares well to that of real simple cells (Figure 4D) in both the relative amplitude of the mean responses and the contrast-dependent—and relatively orientation-independent—change enough in trial-to-trial variability. These features of the responses are relatively robust to changes in the two free parameters of the model, the number of LGN inputs, and the aspect ratio of the simple cell receptive field. One surprising aspect of the model is that the match with the data requires the nonlinearities of synaptic depression and of the relationship between conductance and Vm (“driving force nonlinearity”).

When these are removed from the model, the trial-to-trial variability becomes less dependent on contrast and more dependent on orientation. In other words, it is a combination of biophysical mechanisms that contribute to the contrast invariance of V1 simple cell responses. If orientation tuning were derived solely from the spatial organization of LGN input, it should be possible to predict the orientation tuning curve of a simple cell from a detailed map of its receptive field. That is, if both the orientation tuning curve and the receptive field map arise from the spatial organization of the thalamic input, there should be a direct correspondence between the two. Indeed, predictions derived from the receptive field map and measured orientation tuning curves match closely in orientation preference.