Topographic maps are a prominent feature of brain organization, reflecting local and large-scale representation of the sensory surface. ​​Traditionally, such representations in early visual areas are conceived as retinotopic maps preserving ego-centric retinal spatial location while ensuring that other features of visual input are uniformly represented for every location in space. I will discuss our recent findings of a striking departure from this simple mapping in the secondary visual area (V2) of the tree shrew that is best described as a sinusoidal transformation of the visual field. This sinusoidal topography is ideal for achieving uniform coverage in an elongated area like V2 as predicted by mathematical models designed for wiring minimization, and provides a novel explanation for stripe-like patterns of intra-cortical connections and functional response properties in V2. Our findings suggest that cortical circuits flexibly implement solutions to sensory surface representation, with dramatic consequences for large-scale cortical organization. Furthermore our work challenges the framework of relatively independent encoding of location and features in the visual system, showing instead location-dependent feature sensitivity produced by specialized processing of different features in different spatial locations. In the second part of the talk, I will propose that location-dependent feature sensitivity is a fundamental organizing principle of the visual system that achieves efficient representation of positional regularities in visual input, and reflects the evolutionary selection of sensory and motor circuits to optimally represent behaviorally relevant information. The relevant papers can be found here: V2 retinotopy (Sedigh-Sarvestani et al. Neuron, 2021) Location-dependent feature sensitivity (Sedigh-Sarvestani et al. Under Review, 2022)

To prevail in a dynamic and noisy environment, the brain must create reliable and meaningful representations from sensory inputs that are often ambiguous or corrupt. Since only information that permeates the cortical hierarchy can influence sensory perception and decision-making, it is critical that noisy external stimuli are encoded and propagated through different processing stages with minimal signal degradation. Here we hypothesize that stimulus-specific pathways akin to cortical topographic maps may provide the structural scaffold for such signal routing. We investigate whether the feature-specific pathways within such maps, characterized by the preservation of the relative organization of cells between distinct populations, can guide and route stimulus information throughout the system while retaining representational fidelity. We demonstrate that, in a large modular circuit of spiking neurons comprising multiple sub-networks, topographic projections are not only necessary for accurate propagation of stimulus representations, but can also help the system reduce sensory and intrinsic noise. Moreover, by regulating the effective connectivity and local E/I balance, modular topographic precision enables the system to gradually improve its internal representations and increase signal-to-noise ratio as the input signal passes through the network. Such a denoising function arises beyond a critical transition point in the sharpness of the feed-forward projections, and is characterized by the emergence of inhibition-dominated regimes where population responses along stimulated maps are amplified and others are weakened. Our results indicate that this is a generalizable and robust structural effect, largely independent of the underlying model specificities. Using mean-field approximations, we gain deeper insight into the mechanisms responsible for the qualitative changes in the system’s behavior and show that these depend only on the modular topographic connectivity and stimulus intensity. The general dynamical principle revealed by the theoretical predictions suggest that such a denoising property may be a universal, system-agnostic feature of topographic maps, and may lead to a wide range of behaviorally relevant regimes observed under various experimental conditions: maintaining stable representations of multiple stimuli across cortical circuits; amplifying certain features while suppressing others (winner-take-all circuits); and endow circuits with metastable dynamics (winnerless competition), assumed to be fundamental in a variety of tasks.