Humans and their cousins, the apes, have been exceptional in facial recognition. Their life depended on it for the ability to recognize a friend from a foe was crucial for survival. Recent research from Caltech (1) appears to get closer to the neural code for face recognition. The trick appears to be specialization with singular neurons, focused on specific features. The idea appears to validate recent advances in deep learning for image recognition and it could provide further impetus to the acceleration of artificial intelligence.
The idea that single neurons encode specific features has been tantalizing for deep learning enthusiasts. It allows scalability in deep neural networks with increasing specialization in layers. The single feature specificity at the neuron level and its ability to make binary decisions provide further evidence of micro compartmentalization and voting based decision-making. Complementarity, communication and cooperation appear to dominate in goal seeking and that has implications for future research in computerized image recognition and more broadly, artificial intelligence.
The neural code developed by primates, an efficient and massively parallel processed algorithm that helped them survive and evolve, could be replicatable. Hopefully, those who get hold of this will use it wisely.