The Initiative in Computation in Brain and Mind aims to bring together experts in theory and computation at Brown, link them to experimental brain scientists, and enable outstanding, multilevel and multidisciplinary training for undergraduates, graduates, and postdocs.
Computational methods have been enormously productive for understanding complexities inherent in natural systems — from the weather to aerodynamics, physics, and biology. The brain is perhaps the most complex (and interesting!) of these systems. 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, multliple 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. Brown is a place where these types of interactions regularly occur across faculty members and across 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.
Sophisticated mathematical and computational tools have been incredibly useful for understanding links across levels of analysis between mind and brain, from genes to molecules to cells to networks to behavior. Because of the complexity of the systems involved, understanding fundamental computations at each of these levels and their inter-relations is absolutely critical for progress. Advances in this field are not only instrumental in furthering basic science, but are also desperately needed to better explain the neural basis of psychiatric and neurological mental illnesses (core level i) and for applying statistical methods (core ii) in combination with engineering methods to proactively develop treatments via brain machine interfaces.
Brown has particular expertise in computational approaches to higher order brain function, from perception to cognition, spaning departments of Neuroscience, Cognitive, Linguistic & Psychological Sciences, Applied Mathematics, Computer Science, Neurosurgery, Biostatistics, and Engineering. Most of these faculties cross theory and experiment, but primary foci are listed here:
Core level i
- Computational perception: Theories about how the brain integrates sensory information to give rise to percepts, constrained by biophysics and computational objectives.
- Control over action: reinforcement learning, decision making, and cognitive control.
- Fundamental questions in neural computation: synaptic plasticity, circuits, networks.
Core level ii
- Neurotechnology: brain-machine interface, advanced neural data analysis.
- Automated collection of neuroscience data, e.g. via computer vision and annotation.
- These core areas are supported by boundary-pushing development of technical and analytic methods in Computer Science an Applied Mathematics.
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