Dissecting Artificial Intelligence (AI) to Understand the Human Brain

Author: Cognitive Neuroscience Society
Published: 2018/03/25 - Updated: 2025/03/15
Publication Type: Reports & Proceedings
Peer-Reviewed: Yes
Topic: The Human Brain - Publications List

Page Content: Synopsis - Introduction - Main

Synopsis: Cognitive neuroscientists are using emerging artificial networks to enhance understanding of one of the most elusive intelligence systems, the human brain. Using computer science to understand the human brain is a relatively new field that is expanding thanks to advancements in computing speed and power, along with neuroscience imaging tools. Artificial networks cannot yet replicate human visual abilities, but modeling the human brain is furthering our understanding of cognition and artificial intelligence.

Introduction

In the natural world, intelligence takes many forms. It could be a bat using echolocation to navigate in the dark expertly or an octopus quickly adapting its behavior to survive in the deep ocean. Likewise, multiple forms of artificial intelligence are emerging in the computer science world - different networks, each trained to excel in another task. And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists are increasingly using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain.

Main Item

"The fundamental questions cognitive neuroscientists and computer scientists seek to answer are similar," says Aude Oliva of MIT. "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 determine what those components calculate."

In Oliva's work, which she is presenting at the CNS symposium, neuroscientists are learning much about the role of contextual clues in human image recognition. Using "artificial neurons" - lines of code or software - with neural network models, they can parse out the various elements that go into recognizing a specific place or object.

"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."

In one 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. They expected the network to learn objects such as a bed associated with a bedroom. They didn't expect the network to learn to recognize people and animals, for example, dogs at parks and cats in living rooms.

The machine intelligence programs learn very quickly when given lots of data, enabling them to parse contextual learning at such a good level, Oliva says. While it is impossible to dissect human neurons at such a level, the computer model performing a similar task is transparent. Artificial neural networks serve as mini-brains that can be studied, changed, evaluated, and compared against responses given by human neural networks. Hence, cognitive neuroscientists have sketches of how a real brain may function.

Complete neuron cell diagram showing dendrites, neurotransmitters, and receptacle. Neurons (also known as neurones and nerve cells) are electrically excitable cells in the nervous system that process and transmit information. In vertebrate animals, neurons are the core components of the brain, spinal cord, and peripheral nerves.

Indeed, Kriegeskorte says that 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 task using only computations that biological neurons can perform. Moreover, these neural network models can predict 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, thanks to advancements in computing speed and power and neuroscience imaging tools. The artificial networks cannot yet replicate human visual abilities, Kriegeskorte says, but by modeling the human brain, they are furthering our understanding of cognition and artificial intelligence.

"It's a uniquely exciting time to be working at the intersection of neuroscience, cognitive science, and AI," he says.

Indeed, Oliva says;

"Human cognitive and computational neuroscience is a fast-growing area of research, and knowledge about how the human brain can 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."

• Oliva and Kriegeskorte are presenting in the symposium "Human and machine cognition: The deep learning challenge" at the CNS annual meeting in Boston. More than 1,500 scientists are attending the meeting from March 24-27, 2018.
• CNS is committed to developing mind and brain research investigating cognition's psychological, computational, and neuroscientific bases. Since its founding in 1994, the Society has been dedicated to bringing its 2,000 members worldwide the latest research to facilitate public, professional, and scientific discourse.

Attribution/Source(s): This peer reviewed publication was selected for publishing by the editors of Disabled World (DW) due to its relevance to the disability community. Originally authored by Cognitive Neuroscience Society and published on 2018/03/25, this content may have been edited for style, clarity, or brevity. For further details or clarifications, Cognitive Neuroscience Society can be contacted at cogneurosociety.org NOTE: Disabled World does not provide any warranties or endorsements related to this article.