Kendrick Kay
(University of Minnesota)
Recent data-driven efforts for studying vision using 7T fMRI
Date: October 30, 2018
Description:
In this talk, I will describe some recent and ongoing projects that exploit functional magnetic resonance imaging (fMRI) at ultra-high magnetic field strength (7 Tesla). The first project is a publicly available dataset consisting of whole-brain fMRI retinotopic mapping in 181 healthy adults (1.6-mm resolution). We are currently developing tools based on manual annotations of this dataset in order to quantify topography in individual subjects. The second project investigates the nature of ultra-high-resolution fMRI measurements (0.8-mm resolution) and whether the data from this technique deliver accurate and reliable measurements of local neural activity. The third project is an upcoming effort to collect a massive dataset of whole-brain fMRI responses to natural scenes (1.8-mm resolution, 8 subjects, 10,000 images each). Through careful design of the experiment, data acquisition, and data analysis, we aim to achieve a high-quality dataset that will serve as a benchmark for computational models of visual processing in the human brain.”
Aviv Mezer
(Stanford University)
Lifespan maturation and degeneration of human brain white matter
Date: October 2, 2014
Description:
Understanding human brain structure and function organization in health, disease and development is one of the great challenges for neuroscience. Magnetic resonance imaging (MRI) is the most valuable technique for noninvasive in vivo imaging of human brain. However, the use of MRI is currently limited, due to the lack of theory that links the specific biological structures to the measured signal. In my presentation I will describe a new quantitative MRI (qMRI) method that directly measures the biophysical properties of the human brain tissue such as the macromolecular tissue volume and the macromolecular physico-chemical environment. I will discuss how such quantities can be used for 1) individualized diagnostic applications and 2) testing hypotheses of the principles underlying lifespan changes in white matter structure. The quantitative measurements and models of the living human brain offer a unique opportunity to bridge the gap between cognitive, systems and cellular neuroscience. Such understanding of how different tissue types develop and degenerate could be crucial to the early diagnosis and treatment of developmental and degenerative disorders.
Eero Simoncelli
(NYU)
Recovery of sparse translation-invariant signals with continuous basis pursuit
Date: 8/20/2013
Description:
This talk was given as part of the Distiguished Lecture series in the school of Electrical Engineering at Stanford. Please visit their site to view the talk (SUNetID required): https://ee.stanford.edu/lectures/distinguished
We consider the problem of decomposing a signal into a linear combination of features, each a continuously translated version of one of a small set of elementary features. Although these constituents are drawn from a continuous family, most current signal decomposition methods rely on a finite dictionary of discrete examples selected from this family (e.g.,a set of shifted copies of a set of basic waveforms), and apply sparse optimization methods to select and solve for the relevant coefficients. Here, we generate a dictionary that includes auxilliary interpolation functions that approximate translates of features via adjustment of their coefficients. We formulate a constrained convex optimization problem, in which the full set of dictionary coefficients represent a linear approximation of the signal, the auxiliary coefficients are constrained so as to onlyrepresent translated features, and sparsity is imposed on the non-auxiliary coefficients using an L1 penalty. The well-known basis pursuit denoising (BP) method may be seen as a special case, in which the auxiliary interpolation functions are omitted, and we thus refer to our methodology ascontinuous basis pursuit (CBP). We develop two implementations of CBP for a one- dimensional translationinvariant source, one using a first-order Taylor approximation, and another using a form of trigonometric spline. We examine the tradeoff between sparsity and signal reconstruction accuracy in these methods, demonstrating empirically that trigonometric CBP substantially outperforms Taylor CBP, which in turn offers substantial gains over ordinary BP. In addition, the CBP bases can generally achieve equally good or better approximations with much coarser sampling than BP, leading to a reduction in dictionary dimensionality.
Phil Zimbardo
(Stanford University)
Making Psychology Come Alive: How to Make Your Lectures Challenging, Fun, and Memorable
Date: 07/13/2012
Description:
In his talk, Zimbardo will both describe and vividly illustrate some of his most effective techniques for building engaging lectures, based on decades of experience teaching introductory psychology.