Representational similarity precedes category selectivity in the developing ventral visual pathway.

Many studies have investigated the development of face-, scene-, and body-selective regions in the ventral visual pathway. This work has primarily focused on comparing the size and univariate selectivity of these neural regions in children versus adults. In contrast, very few studies have investigated the developmental trajectory of more distributed activation patterns within and across neural regions. Here, we scanned both children (ages 5-7) and adults to test the hypothesis that distributed representational patterns arise before category selectivity (for faces, bodies, or scenes) in the ventral pathway. Consistent with this hypothesis, we found mature representational patterns in several ventral pathway regions (e.g., FFA, PPA, etc.), even in children who showed no hint of univariate selectivity. These results suggest that representational patterns emerge first in each region, perhaps forming a scaffold upon which univariate category selectivity can subsequently develop. More generally, our findings demonstrate an important dissociation between category selectivity and distributed response patterns, and raise questions about the relative roles of each in development and adult cognition.

Deep neural network models of sensory systems: windows onto the role of task constraints.

Sensory neuroscience aims to build models that predict neural responses and perceptual behaviors, and that provide insight into the principles that give rise to them. For decades, artificial neural networks trained to perform perceptual tasks have attracted interest as potential models of neural computation. Only recently, however, have such systems begun to perform at human levels on some real-world tasks. The recent engineering successes of deep learning have led to renewed interest in artificial neural networks as models of the brain. Here we review applications of deep learning to sensory neuroscience, discussing potential limitations and future directions. We highlight the potential uses of deep neural networks to reveal how task performance may constrain neural systems and behavior. In particular, we consider how task-optimized networks can generate hypotheses about neural representations and functional organization in ways that are analogous to traditional ideal observer models.