Contribution of linear mechanisms to the specification of local motion by simple cells in areas 17 and 18 of the cat

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Abstract
A reverse correlation technique, which permits estimation of three-dimensional first-order properties of receptive fields (RFs), was applied to simple cells in areas 17 and 18 of cat. Two classes of simple cells were found. For one class, the spatial and temporal RF characteristics were Separable, i.e. they could be synthesized as the product of spatial and temporal weighting functions. RFs in the other class were Inseparable, i.e. bright and dark subregions comprising each field were obliquely oriented in space-time. Based on a linear superposition model, these observations led to testable hypotheses: (1) simple cells with separable space-time characteristics should be speed but not direction selective and (2) simple cells with inseparable space-time characteristics should be direction selective and the optimal velocity of moving stimuli should be predictable from the slope of the oriented subregions. These hypotheses were tested by comparing responses to moving bars with those predicted by application of the convolution integral. Linear predictions accounted for waveforms of responses to moving bars in detail. For cells with oriented space-time characteristics, the preferred direction was always predicted correctly and the optimal speed was predicted quite well. Most cells with separable space-time characteristics were not direction selective as predicted. The major discrepancies between measured and predicted behavior were twofold. First, 8/32 cells with separable space-time RFs were direction selective. Second, predicted directional indices were weakly correlated with actual measurements. These conclusions hold for simple cells in both areas 17 and 18. The major difference between simple RFs in these areas is the coarser spatial scale seen in area 18. These results demonstrate a significant linear contribution to the speed and direction selectivity of simple cells in areas 17 and 18. Where additional, nonlinear mechanisms are inferred, they appear to act synergistically with the linear mechanism.