Dissecting AI to Better Understand the Human Brain

At the 25th annual meeting of the Cognitive Neuroscience Society (CNS) (Davis, CA, USA), researchers presented their work in which cognitive neuroscientists are increasingly using emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. Among the work presented at the symposium at the CNS annual meeting held in Boston from March 24-27, 2018, was “Human and machine cognition: The deep learning challenge” by Aude Oliva of MIT. In Oliva’s work, neuroscientists are learning much about the role of contextual clues in human image recognition. By using “artificial neurons” – essentially lines of code, software – with neural network models, they can parse out the various elements that go into recognizing a specific place or object.

In a recent study of more than 10 million images, Oliva and colleagues taught an artificial network to recognize 350 different places, such as a kitchen, bedroom, park, living room, etc. While, they expected the network to learn objects such as a bed associated with a bedroom, they hardly expected the network to learn to recognize people and animals, such as dogs at parks and cats in the living rooms.


When given lots of data, machine intelligence programs learn very quickly, enabling them to parse contextual learning at such a fine level, according to Oliva. It is not possible to dissect human neurons at such a level, although the computer model performing a similar task is entirely transparent. The artificial neural networks serve as “mini-brains that can be studied, changed, evaluated, and compared against responses given by human neural networks, so the cognitive neuroscientists have some sort of sketch of how a real brain may function.”

“The fundamental questions cognitive neuroscientists and computer scientists seek to answer are similar. They have a complex system made of components – for one, it’s called neurons and for the other, it’s called units – and we are doing experiments to try to determine what those components calculate,” said Oliva. “Human cognitive and computational neuroscience is a fast-growing area of research, and knowledge about how the human brain is able to see, hear, feel, think, remember, and predict is mandatory to develop better diagnostic tools, to repair the brain, and to make sure it develops well.”

“The brain is a deep and complex neural network,” says Nikolaus Kriegeskorte of Columbia University, who is chairing the symposium. “Neural network models are brain-inspired models that are now state-of-the-art in many artificial intelligence applications, such as computer vision.”

According to Kriegeskorte, these models have helped neuroscientists understand how people can recognize the objects around them in the blink of an eye. “This involves millions of signals emanating from the retina, that sweep through a sequence of layers of neurons, extracting semantic information, for example that we’re looking at a street scene with several people and a dog,” he says. “Current neural network models can perform this kind of task using only computations that biological neurons can perform. Moreover, these neural network models can predict to some extent how a neuron deep in the brain will respond to any image.”

Using computer science to understand the human brain is a relatively new field that is expanding rapidly thanks to advancements in computing speed and power, along with neuroscience imaging tools. The artificial networks cannot yet replicate human visual abilities, according to Kriegeskorte, but by modeling the human brain, they are furthering understanding of both cognition and artificial intelligence. “It’s a uniquely exciting time to be working at the intersection of neuroscience, cognitive science, and AI,” added Kriegeskorte.

Related Links:
Cognitive Neuroscience Society