Category: Selected articles

• Obscure Matlab features #3: progress reports with publish

  Neurophysiology experiments can generate tons of data. In the initial stages of a project, you can easily become overwhelmed with the results of preliminary analyses. It’s a good idea to collect together the results of these analyses into a single document. This can make it easier to peruse the results with others in your lab,…

  September 4, 2011

• Gibbs sampling made easy – JAGS, RKWard, CODA

  I’ve used OpenBUGS for Gibbs sampling in the past, and while it’s a powerful piece of software, I hate OpenBUGS’ GUI, especially since it runs only on Windows. JAGS is an alternative Gibbs sampling program that uses an OpenBUGS-like syntax and runs on the command line. You can call JAGS in R through the rjags…

  July 13, 2011

• Hexagonal orientation maps in V1

  Interesting paper from Se-Bum Paik and Dario Ringach in this month’s issue of Nature Neuroscience on the origins of the orientation map in V1. Dr. Ringach has been developing a model of V1 orientation selectivity for a number of years now, the statistical connectivity hypothesis, based on the idea that the retinotopic map in V1…

  July 5, 2011

• Coarse-grained parallelism in Matlab with parfor

  Previously, I discussed how you can take advantage of multiple cores in C. In day-to-day research, however, it’s more common to work with high-level languages like Matlab and Python. Although Matlab has been multithreaded for several years now, it’s not very good at maximally using all the cores in a computer. You can verify this…

  June 29, 2011

• Programming for multi-core environments

  CPUs with multiple cores are currently the norm. Getting optimal performance out of these systems is challenging. I recently read Parallel Programming in C with MPI and OpenMP by Michael Quinn, a book that, while released in 2004, remains relevant and actual. Dr. Quinn introduces two technologies which are available in C (and in Fortran…

  June 28, 2011

• The far-reaching influence of sparse coding in V1

  Introduction Olshausen and Field (1996) made a big splash in visual neurophysiology and machine learning by offering an answer to a provocative question: Why are simple cell receptive fields (RFs) organized the way they are? After all, they could just as well be shaped like elongated sine waves, as in Fourier analysis, or they could…

  June 15, 2011