“Cognitive decline is not simply a function of age; it is helpful to understand what parts of the brain are connected to changes in thinking and seeing,” he says.
A Collaboration with Columbia
Leeds, who specializes in computational neuroscience, is one of six Fordham faculty working this year as part of the Columbia and NYU Research Fellow Forum. His recent research has been greatly aided by access to data from the lab of Yaakov Stern, Ph.D., professor and chief of the Neurocognitve Science Division at Columbia University Medical Center.
Leeds’ research on computer data and brain activity is regularly mixed in with artificial intelligence research. He’s particularly interested in the connection of the brain to eyesight. In the lab, Leeds develops computer models of cognition and how the brain thinks, particularly how it represents and stores visual information.
“Here at Fordham we have the resources to do the computational work, and we have great faculty in computer and information sciences, psychology and biology. Columbia has similarly excellent scholars. In terms of big sets of data, Columbia has very great resources and state-of-the-art brain scanners,” says Leeds. “I’m very fortunate that the fellowship has supported our access to Dr. Stern’s lab; it has been a great symbiotic relationship.”
Not only has Leeds benefited, but so have junior Sarah Cavanagh, an integrative neuroscience major, and Caleb Hulbert, a master’s candidate in computer science. Through the Fordham-Columbia fellowship, the two students were able to assist Leeds over the summer, and they continue to work with him in analyzing the data.
Processing Visual Information
Different parts of the brain represent visual information differently, Leeds says. Representation of straight lines or edges is most clear at the back part of the brain. Another area of the brain shows more active involvement in perception of written language, and yet another area may best perceive curvy objects, such as faces.
Stern’s lab at Columbia is working on how people perform cognitive tasks from the ages 20 to 80, and Leeds has been contributing to their data analysis. Using the data obtained in the Columbia lab, Leeds has also homed in on his specialties, vision and cognition.
While his research is still in the very early stage, he has observed that cognitive ability is more a factor of how neural pathways represent visual information, rather than how the body ages. For example, an avid reader may continue to read well into old age and, at the same time, may have problems seeing the edge of a table—depending on which neural pathway is more preserved.
Give-and-Take with Artificial Intelligence
The growing collaboration among scientists creating computer models and those researching neuroscience has obvious implications for artificial intelligence, says Leeds, but there’s an ongoing debate as to what are the proper relations between the two branches of science.
“I think that artificial intelligence and neuroscience provide great information back and forth,” he said, because by understanding the brain, computer scientists can develop better programs for artificial intelligence, and vice versa.
“I often take A.I. computer models that are already used in computer vison tests and ask how well a model reflects how the brain acts,” he said.
“If artificial intelligence can help us better understand what’s going on in the brain, and how it can go wrong when different elements are affected, then potentially we can do an intervention on the brain system level, or even a lower-level intervention with behavioral therapy. That’s a good thing.”