Orientation selectivity from very sparse LGN inputs in a comprehensive model of macaque V1 cortex

A new computational model of the primary visual cortex (V1) of the macaque monkey was constructed to reconcile the visual functions of V1 with anatomical data on its LGN input, the extreme sparseness of which presented serious challenges to theoretically sound explanations of cortical function. We demonstrate that, even with such sparse input, it is possible to produce robust orientation selectivity, as well as continuity in the orientation map. We went beyond that to find plausible dynamic regimes of our new model that emulate simultaneously experimental data for a wide range of V1 phenomena, beginning with orientation selectivity but also including diversity in neuronal responses, bimodal distributions of the modulation ratio (the simple/complex classification), and dynamic signatures, such as gamma-band oscillations. Intracortical interactions play a major role in all aspects of the visual functions of the model.