tl;dr: 1) apply to PhD in: stats/neuro, 2) stats; 3) apply to CNBC program
PhD in Statistics and Neural Computation: This degree is aimed at students who want to join the PNC program, while also specializing in statistics.
Ph.D. Program in Neural Computation: a graduate training program in computational neuroscience for students seeking training in the application of quantitative approaches to the study of the brain.
CNBC Grad Training Program: to be admitted to the CNBC Training Program, students must have been accepted into one of the affiliated doctoral programs at either the University of Pittsburgh or Carnegie Mellon. For students applying from outside the two universities, applications to the doctoral program and the CNBC program may be submitted simultaneously. n.b. not a degree-granting program but I still need to apply
People in CNBC (source)
Also check neurotree, and more CNBC.
n.b. to do joint PhD program the advisor must be affiliated with CNBC.
More people here.
Theoretical Neuroscience Group Homepage
Research Topics: Sensation & Perception Characterization of Neural Circuits Learning & Memory Molecular, Cellular & Synaptic Processes
Research topics: Network dynamics and neural coding, cognitive processing, cellular and synaptic dynamics
Research Topics: Methods Development
Research topics: Nonparametric prediction of time series; learning theory and nonlinear dynamics; information theory; stochastic automata, state space and hidden Markov models; causation and prediction; large deviations and ergodic theory; neuroscience; statistical mechanics and self-organization; social and complex networks; heavy-tailed distributions.
n.b. "I have no influence over admissions, and don't want any, so writing me about that is a waste of your time."
Research Topics: Characterization of Neural Circuits Developmental Processes Diseases & Disorders Executive Control & Memory Learning & Memory Molecular, Cellular & Synaptic Processes Motor Control Reasoning & Problem Solving Spatial Cognition & Attention Sensation & Perception
I am broadly interested in Statistical Methods in Neuroscience, but most of my publications have concerned statistical analysis of spike train data, i.e., the output of single-electrode and multiple-electrode neurophysiological experiments.
Great overview and more papers here
And a book
Papers list
Research interests: Research Topics: Characterization of Neural Circuits Motor Control Methods Development Sensation & Perception Sensation & Perception Learning & Memory Learning & Memory Spatial Cognition & Attention
Homepage blurb: My main interest is in spike train data analysis, both for single trains and multiple simultaneously recorded neurons. For single spike trains, I have studied diffusion approximations to various integrate-and-fire models, with particular emphasis on the fitting of models to data, comparing the fits of several models, and refining the diffusion approximations.For multiple spike trains, my main interest is in graphical displays such as snowflake plot and gravitational clustering, along with explanatory methods to find structure in such data.
- Nonconvergence in Logistic and Poisson Models for Neural Spiking
- An L 1-regularized logistic model for detecting short-term neuronal interactions
Research Topics: Characterization of Neural Circuits Methods Development Learning & Memory Sensation & Perception
Research interests: computational neuroscience, computational vision, neurophysiology of the primate visual systems, active and adaptive vision, hierarchical coding and inference, mid-level vision, development of infant vision, learning and adaptation, structure of neural codes.
Notes: most graduate students from CS, none from stats, only one from neuro.
CNBC researchers demonstrate the interactive nature of perceptual processing in early visual areas
Research Topics: Methods Development
My research interests center around making effective inferences in complex scientific problems. On the applications side, I have active collaborations in neuroscience, cosmology, astronomy, and entomology. On the theoretical side, I am interested confidence sets for nonparametric inference, adaptive function estimation, spatial statistics, inverse problems, and multiple testing. Currently, in neuroscience, I am working with different groups to study the remapping of human's visual representation during and after eye movements and the role of the amygdala and pre-frontal cortex in depression
Note: doesn't appear to have grad students or a lab?
Some cool links
- Comparing temporal aspects of visual, tactile, and microstimulation feedback for motor control
- An L 1-regularized logistic model for detecting short-term neuronal interactions
Research Topics: Learning & Memory Spatial Cognition & Attention
Research Topics: Methods Development
Computer science, machine learning, artificial intelligence, and cognitive neuroscience. My research focuses on basic and applied problems in machine learning, understanding how the human brain represents information in terms of neural activity, and statistical learning algorithms for natural language processing.
Notes: no lab? no former or current grad students?
Research Topics: Methods Development
In the last six years I have become keenly interested in the statistical problems associated with fMRI. A typical fMRI experiment run by a cognitive psychologist produces as much as 1 gigabyte of data per hour. The computational challenges are obvious.