The principles behind the connectivity between LGN and V1 are not well understood. Models have to explain two basic experimental trends: (i) the combination of thalamic responses is local and it gives rise to a variety of oriented Gabor-like receptive felds in V1 [1], and (ii) these filters are spatially organized in orientation maps [2]. Competing explanations of orientation maps use purely geometrical arguments such as optimal wiring or packing from LGN [3-5], but they make no explicit reference to visual function. On the other hand, explanations based on func- tional arguments such as maximum information transference (infomax) [6,7] usually neglect a potential contribution from LGN local circuitry. In this work we explore the abil- ity of the conventional functional arguments (infomax and variants), to derive both trends simultaneously assuming a plausible sampling model linking the retina to the LGN [8], as opposed to previous attempts operating from the retina.
Consistently with other aspects of human vi- sion [14-16], additional constraints should be added to plain infomax to understand the second trend of the LGN-V1 con- nectivity. Possibilities include energy budget [11], wiring constraints [8], or error minimization in noisy systems, ei- ther linear [16] or nonlinear [14, 15]. In particular, consideration of high noise (neglected here) would favor the redundancy in the prediction (which would be required to match the relations between spatially neighbor neurons in the same orientation domain)