Monday, September 9, 2013

[Comp-neuro] PhD studentships: Computation in Brain and Mind at Brown University

Announcing a new multidisciplinary initiative for Computation in Brain and Mind at Brown University, within the Brown Institute for Brain Science.  PhD students are encouraged to apply to any of the departments affiliated with the initiative, including Neuroscience, Cognitive, Linguistic and Psychological Sciences, Applied Mathematics, Computer Science and others.   The initiative includes a seminar series focused on computation with distinguished lecturers, yearly technical workshops and symposia,  and a yearly neural decoding competition. The initiative will also have close links to parallel initiatives at Brown in Human-Robot Interaction, Digital Society (big data), and access to a high performance compute cluster with dedicated cycles for Brain Science.   

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, 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; application to mental illnesses.

* 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.


Core faculty whose research and teaching focus centers around computation in brain and mind include:

* James Anderson
* Joseph Austerweil
* Leon Cooper
* Michael Frank
* Stuart Geman
* Matthew Harrison
* James Hays
* Sorin Istrail
* Stephanie Jones
* Benjamin Kimia
* Michael Littman
* Xi Rossi Luo
* Thomas Serre
* Erik Sudderth
* Wilson Truccolo

In addition there are many affiliated faculty who rely on computation in various aspects of their research. See for a full list.

Michael J Frank, PhD, Associate Professor
Laboratory for Neural Computation and Cognition
Brown University

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