Brain science seeks to understand the myriad functions of the brain in terms of principles that lead from molecular interactions to behavior. Although the complexity of the brain is daunting and the field seems brazenly ambitious, painstaking experimental efforts have made impressive progress. While investigations, being dependent on methods of measurement, have frequently been driven by clever use of the newest technologies, many diverse phenomena have been rendered comprehensible through interpretive analysis, which has often leaned heavily on mathematical and statistical ideas. These ideas are varied, but a central framing of the problem has been to “elucidate the representation and transmission of information in the nervous system” (Perkel and Bullock 1968). In addition, new and improved measurement and storage devices have enabled increasingly detailed recordings, as well as methods of perturbing neural circuits, with many scientists feeling at once excited and overwhelmed by opportunities of learning from the ever-larger and more complex data sets they are collecting. Thus, computational neuroscience has come to encompass not only a program of modeling neural activity and brain function at all levels of detail and abstraction, from sub-cellular biophysics to human behavior, but also advanced methods for analysis of neural data.
The paragraph above comes from this paper. I am broadly interested in analysis of neural data (the basics of which are covered in my book on this subject), but most of my publications have concerned statistical analysis of spike train data. For an overview of my research in this area see
For other subjects, see my CV. Some, but not all, of the papers on neuroscientific topics are also included on my web page selected papers, together with some general statistical work.
Related Software (OLD):