Kohitij Kar

TL;DR: The internal representations of early deep artificial neural networks were found to be remarkably similar to the internal neural representations measured experimentally in the primate brain, and a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brain’s mechanisms for core object recognition is developed.

TL;DR: Using model- and primate behavior-driven image selection with large-scale electrophysiology in monkeys performing core recognition tasks, Kar et al. provide evidence that automatically engaged recurrent circuits are critical for rapid object identification.

TL;DR: The results show that current DCNNIC models cannot account for the image-level behavioral patterns of primates and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision.

TL;DR: An artificial neural network built to model the behavior of the target visual system was used to construct images predicted to either broadly activate large populations of neurons or selectively activate one population while keeping the others unchanged, demonstrating that these models can partially generalize and provide a shareable way to embed collective knowledge of visual processing.

TL;DR: The results argue that automatically-evoked recurrent circuits are critical even for rapid object identification, by precisely comparing current DCNNs, primate behavior and IT population dynamics, and provide guidance for future recurrent model development.