Deep learning

Control engineering

Pattern recognition (psychology)

Neuroscience

Feed forward

Cognitive neuroscience of visual object recognition

Biological neural network

Recurrent neural network

Engineering

Artificial intelligence

Psychology

Computer science

Object (grammar)

Artificial neural network

Convolutional neural network

Nature Neuroscience

Non-recurrent deep convolutional neural networks (CNNs) are currently the best at modeling core object recognition, a behavior that is supported by the densely recurrent primate ventral stream, culminating in the inferior temporal (IT) cortex. If recurrence is critical to this behavior, then primates should outperform feedforward-only deep CNNs for images that require additional recurrent processing beyond the feedforward IT response. Here we first used behavioral methods to discover hundreds of these ‘challenge’ images. Second, using large-scale electrophysiology, we observed that behaviorally sufficient object identity solutions emerged ~30 ms later in the IT cortex for challenge images compared with primate performance-matched ‘control’ images. Third, these behaviorally critical late-phase IT response patterns were poorly predicted by feedforward deep CNN activations. Notably, very-deep CNNs and shallower recurrent CNNs better predicted these late IT responses, suggesting that there is a functional equivalence between additional nonlinear transformations and recurrence. Beyond arguing that recurrent circuits are critical for rapid object identification, our results provide strong constraints for future recurrent model development. 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.

Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior