The origin of orientation selectivity in visual cortical responses is a central problem for understanding cerebral cortical circuitry. In cats, many experiments suggest that orientation selectivity arises from the arrangement of LGN inputs to layer 4 cortical simple cells. We have shown how such an arrangement of "feedforward" inputs can self-organize through correlation-based mechanisms of synaptic plasticity. However, such a "feedforward" explanation appears insufficient to account for the contrast-invariance of cortical orientation tuning.
We propose a new model consistent with a wide range of experimental data. We demonstrate that the LGN input to cat cortical simple cells has two components: a phase-specific component, which is tuned for orientation; and a phase-nonspecific component, which is untuned. Both components grow with contrast. Contrast-dependent inhibition is required to suppress the untuned input component, and to prevent the broadening of the tuning of spiking responses with increasing contrast. A simple circuit using correlation-based intracortical connectivity (cross-phase inhibition and same-phase excitation, local in orientation), accomplishes this to achieve well-tuned, contrast-invariant orientation tuning. Unlike previous models, this circuit agrees with experimental evidence showing spatial opponency between, and similar orientation tuning of, the excitatory and inhibitory inputs received by a cell. Orientation tuning is primarily input driven, naturally accounting for the observed equality of input tuning and full circuit tuning as well as for the dependence of orientation tuning width on stimulus spatial frequency.
The model predicts that inhibitory neurons in cat layer 4 should respond in a contrast-dependent manner to stimuli of all orientations, although the width of their orientation tuning is similar to that of excitatory neurons. The model demonstrates that fundamental response properties of cortical layer 4 can be explained by circuitry that would be expected to develop under correlation-based rules of synaptic plasticity, and shows how such circuitry allows the cortex to distinguish stimulus intensity from stimulus form.
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