  Deep convolutional artificial neural networks (ANNs) are the leading class of
candidate models of the mechanisms of visual processing in the primate ventral
stream. While initially inspired by brain anatomy, over the past years, these
ANNs have evolved from a simple eight-layer architecture in AlexNet to
extremely deep and branching architectures, demonstrating increasingly better
object categorization performance, yet bringing into question how brain-like
they still are. In particular, typical deep models from the machine learning
community are often hard to map onto the brain's anatomy due to their vast
number of layers and missing biologically-important connections, such as
recurrence. Here we demonstrate that better anatomical alignment to the brain
and high performance on machine learning as well as neuroscience measures do
not have to be in contradiction. We developed CORnet-S, a shallow ANN with four
anatomically mapped areas and recurrent connectivity, guided by Brain-Score, a
new large-scale composite of neural and behavioral benchmarks for quantifying
the functional fidelity of models of the primate ventral visual stream. Despite
being significantly shallower than most models, CORnet-S is the top model on
Brain-Score and outperforms similarly compact models on ImageNet. Moreover, our
extensive analyses of CORnet-S circuitry variants reveal that recurrence is the
main predictive factor of both Brain-Score and ImageNet top-1 performance.
Finally, we report that the temporal evolution of the CORnet-S "IT" neural
population resembles the actual monkey IT population dynamics. Taken together,
these results establish CORnet-S, a compact, recurrent ANN, as the current best
model of the primate ventral visual stream.
