Can machines be programmed to operate with the efficiency and proficiency of the human brain? This neuroscience-centric question guides the research of many in the Department of Cognitive Sciences who study the biological foundation of perceptual, motor, and higher cognitive capacities.

Newest to the machine learning arm of the group is Emre Neftci, an assistant professor who runs the Neuromorphic Machine Intelligence Lab where he is working on two newly funded studies aimed at building “neuromorphic” computing systems - networks that work more like the brain - that scale up the speed, processing power and energy efficiency of computing technology in human-centric tasks.

The first study is a joint venture with a team of engineers from the University of Notre Dame’s EXCEL Center. The group is working on new nanotechnology that relies on a transistor element designed at Notre Dame that Neftci’s neuromorphic algorithms can harness.  If the group can successfully program Neftci’s neural models to run on the transistor, the resulting architecture will be novel in its ability to house memory and processing in one place, similarly to the brain. The step would be distinct from mainstream computers that rely on architecture where memory and processing are housed separately, and could pave the way for future developments in nanotechnology, and even artificial intelligence. The second study, funded by the National Science Foundation, supports the scientist’s on-going efforts to further define the algorithms and simulations behind his cognitive neural models so that they can autonomously learn to perform more precise and complex tasks.

Project funding for the work totals $960,000 with $335,242 coming from Notre Dame through 2019 and $624,697 expected from NSF in renewals through 2022.