Striving to understand how the brain computes and stores information
Key leaders: Michael Frank, Ph.D. and Thomas Serre, Ph.D.
The Initiative in Computation in Brain and Mind brings together experts in theory and computation at Brown, links them to experimental brain scientists, and enables outstanding, multilevel and multidisciplinary training for undergraduates, graduates, and postdocs. The problem of understanding the relationship between brain and mind is so immensely complex that a close interaction among theorists and experimentalists is required to gain a deeper understanding of fundamental brain and cognitive processes. Because of the many levels spanning from molecules to cognition, multiple levels of computer simulations – from those focused on details of neuronal function to those focused on the abstract computations that emerge from these networks – can be fruitfully applied to bridge this gap. At Brown, these types of interactions regularly occur among faculty members and students at all levels.
Brown neuroscientists and cognitive scientists rely on computational tools for two core purposes: (i) to develop and refine theories about the fundamental computations of mind and brain, used to guide and interpret experiments; (ii) to develop sophisticated statistical analysis tools for decoding neural data and predicting, for example, spike trains in a given neuronal population based on their spike history and to leverage this predictability for applications such as brain-machine interfaces. Other applications include the use of computational tools to automate the monitoring and analysis of behavioral neuroscience data.
Brown has particular expertise in computational approaches to higher order brain function, from perception to cognition, spanning departments of Neuroscience, Cognitive, Linguistic & Psychological Sciences, Applied Mathematics, Computer Science, Neurosurgery, Biostatistics, and Engineering.