By working to understand how new AI systems integrate flexible and incremental learning, researchers gained insights about the human brain.

Researchers at the Carney Institute for Brain Science have identified electrical activity in the brain that could predict progression to Alzheimer’s disease.

Graduate students from Brown University and Ben-Gurion University put their brains together in a unique summer practicum to invent and pitch ideas for neurotechnology startups.

A new institute, based at Brown and supported by a $20 million National Science Foundation grant, will convene researchers to guide development of a new generation of AI assistants for use in mental and behavioral health.

With the launch, Brown becomes one of a handful of U.S. universities with an undergraduate curriculum that marries neuroscience with computer science and math.

The lab of Michael Frank has reconciled theories of how humans store and retrieve information in the short-term, a process called working memory, in a new biologically-inspired computational model. The findings have implications for dopamine-related disorders like Parkinson’s disease, attention-deficit/hyperactivity disorder (ADHD) and schizophrenia.

Jay Gopal is enrolled in Brown’s Program in Liberal Medical Education, which combines undergraduate and medical school education. A researcher in the Serre Lab, Gopal is creating human-aligned deep neural networks and leading the design and development of ClickMe, an object recognition game with thousands of online users who are creating a massive AI training set. He is also co-founder of a medical software startup.

Ellie Pavlick is the associate chair of the Department of Computer Science, and a Carney collaborator on artificial intelligence projects that involve natural language processing - a technology that makes chatbots possible. How language works, in humans and machines, is Pavlick's central scientific fascination.

Carney Institute affiliate Carina Curto served as a presenter and panelist at the recent BRAIN NeuroAI Workshop, making a case for using the fundamental principles of neuroscience to build better deep neural networks that run artificial intelligence systems.

How neuroscience is forging the future of mental health care

Deep neural networks are computer models that drive today’s artificial intelligence technologies. Unpacking what’s inside these models is the focus of a growing field of research known as explainability or XAI. Explainability demystifies AI by revealing how a deep neural network model has learned to solve a given task.