Random wiring, ganglion cell mosaics, and the functional architecture of the visual cortex

TL;DR

This paper investigates whether the invariant architecture of the primary visual cortex can be explained by random wiring models or if it requires an optimization principle, concluding that invariants are signatures of cortical optimization rather than random wiring.

Contribution

The study provides a detailed analysis showing that V1 invariants cannot be explained by random feedforward wiring, supporting the idea of an optimization-driven architecture.

Findings

V1 invariants are specific signatures of cortical optimization.

Random wiring models do not account for the observed invariants.

Closed-form expressions for cortical receptive fields were derived.

Abstract

The architecture of iso-orientation domains in the primary visual cortex of placental carnivores and primates apparently follows species invariant quantitative laws. Dynamical optimization models assuming that neurons coordinate their stimulus preferences throughout cortical circuits linking millions of cells specifically predict these invariants. This might indicate that V1's intrinsic connectome and its functional architecture adhere to a single optimization principle with high precision and robustness. To validate this hypothesis, it is critical to closely examine the quantitative predictions of alternative candidate theories. Random feedforward wiring within the retino-cortical pathway represents a conceptually appealing alternative to dynamical circuit optimization because random dimension-expanding projections are believed to generically exhibit computationally favorable properties for stimulus representations. Here, we ask whether the quantitative invariants of V1 architecture can be explained as a generic emergent property of random wiring. We generalize and examine the stochastic wiring model proposed by Ringach and coworkers, in which iso-orientation domains in the visual cortex arise through random feedforward connections between semi-regular mosaics of retinal ganglion cells (RGCs) and visual cortical neurons. We derive closed-form expressions for cortical receptive fields and domain layouts predicted by the model for perfectly hexagonal RGC mosaics [...] We conclude that V1 layout invariants are specific quantitative signatures of visual cortical optimization, which cannot be explained by generic random feedforward-wiring models. *See pdf for the full abstract.*