Carney Institute for Brain Science
Center for Computational Brain Science
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Faculty at Brown earn prominent awards, distinctions
In recent months, prestigious national and international organizations recognized Brown faculty for their research, scholarship, humanitarian efforts and leadership.
Carney professor wins $100,000 in MIT’s biotech startup award program
In 1999, an MIT study on the status of women faculty in science showed that less than ten percent of the 250 biotech startups created by MIT professors were founded by women, even though 22 percent of MIT faculty are women. To help turn this tide and increase the number of female-founded biotech startups, MIT launched the Faculty Founder Initiative in 2020. Assistant professor of brain science Frederike Petzschner was named as one of two runners-up in this year’s competition, garnering a $100,000 award. She and professor of neuroscience Stephanie Jones were among the competitors lauded at this year’s award ceremony held at MIT’s Broad Institute on May 2.
Carney researchers show that large language models can reproduce mechanisms found in the human brain
A collaboration between professors Michael Frank and Ellie Pavlick is yielding important results about similarities between how ChatGPT-like AI and the human brain accomplish certain complex tasks, opening the door for transformative research at the intersection of computational neuroscience and computer science.
Acting on Impulse: Parkinson’s disease treatments alter decision-making through two paths
Combining experimentation and computational modeling, Carney researchers show how two therapies for Parkinson's disease (PD) - deep brain stimulation and dopaminergic therapy - differently alter impulsive decision-making and suggest a computational biomarker that could aid in treatment selection.
Frederike Petzschner named as a MIT Faculty Founder Initiative finalist
The competition supports female faculty entrepreneurs in biotechnology and provides them with resources to help take their ideas to commercialization.
Practical challenges for precision medicine
The prediction of individual treatment responses with machine learning faces hurdles
June 28, 2023
News from Brown
New tool explains how AI ‘sees’ images and why it might mistake an astronaut for a shovel
A team of Brown brain and computer scientists developed a new approach to understanding computer vision, which can be used to help create better, safer and more robust artificial intelligence systems.
Carney researchers develop a technique to more accurately determine treatment for patients suffering from a neurodegenerative disease
A multidisciplinary team of Carney-affiliated researchers have published research that has meaningful implications for patients suffering from Normal Pressure Hydrocephalus (NPH) and identifies new biomarkers in the neurodegenerative disease landscape.
Recent discoveries in computational neuroscience hold important implications for how humans learn and make decisions
How does dopamine help us make important decisions? What kinds of learning scenarios best enable us to become proficient at something? And why does overthinking sometimes hinder learning? Professor Michael Frank’s lab has published scholarship that responds to these questions. The Director of Carney’s Center for Computational Brain Science, Frank’s research combines computational modeling and experimental work to understand the neural mechanisms underlying reinforcement learning, decision making and cognitive control.
A new mobile app developed by researchers at Brown University’s brain science institute is looking to find what happens in the brain during the transition from acute to chronic pain.
Ryan Thorpe, Darcy Diesburg and Chad Williams named winner/runners-up of the 2022 Brainstorm Challenge
This year, participants were provided with electrical brain activity and clinical scores of 172 clinically depressed patients both pre and post treatment with TMS from Linda Carpenter’s Lab at the Butler Hospital. The investigators’ main challenge was to predict, based on electroencephalogram data from the first treatment session, if a patient would respond to TMS treatment. In practice, these predictions would be able to determine which patients would be the best candidates for and most responsive to TMS treatment for depression.
Brown scholars put their heads together to decode the neuroscience behind ChatGPT
The Carney Institute for Brain Science brought together faculty who study different aspects of artificial intelligence to discuss what it has in common with human intelligence, and its implications for society.