How is information about the external world and about animals's internal states represented within their nervous systems? Although a great deal is known about the relationships between the stimulus/response properties of nerve cells in a variety of systems, we are in many cases far from having a detailed understanding of the correspondence between neural activity patterns and the information represented by those patterns. We will not be able to understand the operation of any nervous system rigorously until we decipher the neural code, i.e., the system of symbols used to represent and convey information within that system. A sound, rigorous understanding of neural coding will also be essential from the standpoint of developing sophisticated models of nerve cells and systems. What aspects of neural ensemble activity patterns should be measured experimentally and incorporated into models?
There is probably no such thing as THE neural code, universal across all animals or even between different subsystems in a single animal, in the same sense as there exists a universal genetic code. However, general principles of neural encoding are starting to emerge. Much recent work in this area involves the application of sophisticated statistical approaches to the analysis of neural spike train data, and applied mathematicians have made substantial contributions to this area of research. Numerous approaches to the estimation of information-theoretic quantities from spike trains have been proposed and applied in a variety of systems. However, many of the approaches are based on very different sets of assumptions. Some significant differences have emerged in the interpretations of these information theoretic analyses, and it is unclear how much of these differences can be explained by differences in what is actually being measured, to the biases or hidden assumptions in the methodologies, or to real differences in the biological coding schemes. The whole field is ripe for a rigorous examination, comparison and normalization of the different approaches. Neuroscience would benefit greatly from an increased involvement of mathematicians and statisticians in extending the analytical framework, and from their direct involvement in designing and interpreting the experiments.
Three general aims of the workshop include the following:
Examples of organizing questions to be considered in this workshop are as follows:
|Monday, February 10|
|8:15-8:45am||Coffee and registration|
|8:45-9:15am||Welcome and introduction: Avner Friedman, Jim Miller, and Emery Brown|
|3:00-4:15pm||Chalk Talks: Statistical analysis of neural data:, L.Paninski, M.DeWeese S.Panzeri|
|Tuesday, February 11|
|3:00-5:00pm||Chalk Talks: More analysis of neural data: B.Ghosh, S.Gruen, H.Bokil, G.Stanley, T.Gedeon|
|Wednesday, February 12|
|3:00-5:00pm||Chalk Talks: Neural Engineering: C.Eliasmith, C.Anderson, D.Arathorn, M. Stetter|
|Thursday, February 13|
|3:00-5:00pm||Chalk Talks: Neural modeling: J.Hertz, T. Sharpee, S.Crook, J.Miller, E.Bienenstock|
|Friday, February 14|
|3:00-5:00pm||Chalk Talks: Fancy statistical data analysis and modeling: N. Singh, C.Machens, P. Roy|