BrainHub Announces Recipients of ProSEED Funding
Grants will provide seed funding for innovative ideas in neuroscience
Carnegie Mellon University has funded four new interdisciplinary neuroscience projects through its ProSEED grant program. The projects aim to create new tools and techniques to vastly improve how scientists study the brain and leverage the university’s strengths in biology, computer science, machine learning, psychology, and engineering. They are part of CMU’s global BrainHub initiative, which focuses on how the structure and activity of the brain give rise to complex behaviors.
ProSEED, a program initiated by Carnegie Mellon President Subra Suresh, provides startup funding for innovative projects that span a number of disciplines. The ProSEED/BrainHub seed grants were created to help researchers develop novel approaches to the study of brain and behavior, and foster new collaborations within CMU and with BrainHub partner institutions.
“The future of brain research lies at the interface of neuroscience, computer science, and engineering,” said Gerry Balbier, executive director of BrainHub. “At Carnegie Mellon, we have unique expertise in all these disciplines. The ProSEED grants allow us accelerate research at these interfaces in its early, most experimental stages.”
The ProSEED/BrainHub grants, which total $246,193, will allow scientists to complete the fundamental research needed to apply for further funding from governmental and other sources. BrainHub seed grants are funded through generous gifts from Henry L. Hillman and Kris Gopalakrishnan. Matching funds are provided by the Mellon College of Science, Dietrich College of Humanities and Social Sciences, College of Engineering, and School of Computer Science.
The newly funded projects include two that combine engineering with neurobiology to create probes to stimulate and record neural activity, a project aimed at developing statistical methods to study higher-order areas of the brain, and one that will develop active machine learning algorithms that will help researchers analyze brain images.
The projects are:
Optoflex: An implantable micro-laser neural probe
Optical methods, using light of particular colors, to stimulate and record neural activity have revolutionized how scientists study the brain. Maysam Chamanzar, assistant professor of electrical and computer engineering, Elias Towe, professor of electrical and computer engineering, and Alison Barth, professor of biological sciences and interim director of BrainHub, will combine their complementary expertise in neural probe development, laser fabrication technology, and neurophysiology to develop a prototype, next-generation optical neural implant. Such optical neural probes will enable stimulation and recording of neuronal activity across larger and deeper areas of the brain with high spatiotemporal resolution.
Development and in vivo evaluation of ultra-compliant ultra-miniature intracortical probes for brain-computer interfaces
Intracortical neural probes have provided scientists with a precise tool for recording and stimulating neurons. These probes are key to revolutionary technologies including deep-brain stimulation and neural prostheses. However, existing probes can cause damage to surrounding brain tissue, making them ineffective for extended use. Burak Ozdoganlar, the Ver Plank Professor of Mechanical Engineering, Aryn Gittis, assistant professor of biological sciences, and Gary Fedder, the Howard M. Wilkoff Professor of Electrical and Computer Engineering and professor in the Robotics Institute, will create “CMU probes.” The tiny probes are inserted into the brain with dissolvable, biocompatible needles. The polymeric needles also serve as a platform for delivering drugs that can prevent inflammation and promote neural regeneration, making the probes more likely to function for long periods of time.
Scaling up the number of stimuli for studying the inferotemporal cortex
When studying higher-order brain areas, such as the inferotemporal cortex, researchers need to measure how neurons respond to hundreds of stimuli, more than can be accurately recorded. Associate professor of Electrical and Computer Engineering and Biomedical Engineering Byron Yu and Carl Olson, a professor in the joint CMU/University of Pittsburgh Center for the Neural Basis of Cognition, will develop a statistical method that will allow them to fill in the gaps of incomplete data sets, providing a valuable tool for studying higher-order areas of the brain.
Identifying how the brain visually processes natural scenes
Over the past several decades, neuroscientists have been unable to identify the complex features that the human brain uses to visually process the world around us. The main obstacle has been how current imaging technologies (like fMRI) limit how many images can be shown during an experiment and record corresponding brain activation patterns. To overcome this, CMU’s Aarti Singh, the A. Nico Habermann Associate Professor of Machine Learning, and Michael J. Tarr, professor and head of the Department of Psychology, will team up with Daniel Leeds, assistant professor of computer and information sciences at Fordham University. With funding from both universities, they will develop active machine learning algorithms to efficiently search through the huge space of potential visual stimulus images in real-time to find the ones that are maximally informative of the visual perception system’s underlying brain mechanisms.