As research strives to understand how the brain computes and stores information, computational neuroscience is increasingly at the core of the inquiry.
The growing field employs sophisticated mathematics, theoretical analysis and modeling to understand the function of neural circuits and how circuits generate behavior, whether beneficial or not. A prime area of research at the Carney Institute for Brain Science involves innovative advances in computational neuroscience to address behavior and mood disorders, using understanding of neural circuitry to predict who is most at risk and how best to apply therapies, whether through drugs or devices.
“Computational neuroscience has become integrated into almost every part of brain science now,” said John Davenport, managing director of the Carney Institute. “There is a real interest in leveraging computational expertise to get tools into the world that can help people — just think of the possibilities if we could predict depression or any number of brain disorders.”
Michael Frank, professor of cognitive, linguistic and psychological sciences, is active in the field of computational psychiatry, which is spreading as a major influence in the diagnosis and treatment of mental illness. The field combines many types of computation with different types of data, and can be used as predictive in situations such as psychiatric illness or the risk of suicide.
Frank also leads a lab whose research combines multiple levels of computational modeling and experimental work to better understand the neural mechanisms that underlie reinforcement learning, decision making and cognitive control. A recent study of Frank’s showed how two distinct brain systems work cooperatively as people learn new tasks.
Computer vision — using computers and algorithms to analyze and organize large amounts of data and images — is a specialty of Thomas Serre, associate professor of cognitive, linguistic and psychological sciences. Serre’s recent work includes using artificial intelligence to create an Automated Continuous Behavioral Monitoring system to collect and annotate laboratory behavioral data. In collaboration with Kevin Bath, assistant professor of cognitive, linguistic and psychological sciences, the approach is now a core research tool and is being applied to mice carrying human mutations related to amyotrophic lateral sclerosis (ALS) in order to uncover the earliest events in the neurodegenerative disease.
Serre’s research also seeks to understand the neural computations supporting visual perception. His group uses a combination of experimental methods to develop quantitative computational models that try not only to mimic the processing of visual information in the cortex but also to match human performance in complex visual tasks.
Another Carney Institute researcher in computational neuroscience, Stephanie Jones, associate professor of neuroscience, uses experimental and theoretical techniques to study human brain dynamics. In recent research, she showed that people and mice alike use bursts of beta brainwaves, rather than sustained rhythms, to control attention and perception.
Confirming that mice model the human experience means researchers can rely on mice in experiments that delve more deeply into how beta bursts arise and what their consequences are in neurons and circuits. According to Jones, an improved understanding of how beta works could translate into improving therapies such as transcranial magnetic stimulation or transcranial alternating current to treat neurological disorders, such as chronic pain, or depression.
Wilson Truccolo, assistant professor of computational neuroscience, studies how brain function emerges from the coordinated activity of many neurons and how neurological disorders, such as epilepsy, result from particular dynamics of neural activity.
“Brown’s edge,” says Davenport, “is not just the strength of our individual faculty, but our ability through collaboration to integrate knowledge across different scales of computational approaches. Only through such combined efforts will we truly be able to understand how neural circuits generate behavior.”