Special Issue on Combinatorial Optimization in Data Mining

Abstract

The human brain is among the most complex systems known to man. Neuroscientists seek to understand brain function through detailed analysis of neuronal excitability and synaptic transmission. Only in the last few years has it become feasible to capture simultaneous responses from large enough numbers of neurons to empirically test the theories of human brain function. Experimental neuroscience methods have resulted in massive amounts of data, but traditional data-processing and quantitative methods are not sophisticated enough to exploit this new flood of information. There is an increasing number of modern research efforts in data mining, systems analysis and optimization research to advance methods needed to process the large spatial and temporal data arising in quantitative neuroscience. In view of the importance of advances in data mining and optimization, the DIMACS Conference on Data Mining, Systems Analysis and Optimization in Neuroscience was held at the University of Florida, Gainesville, FL, on February 15–17, 2006. The workshop brought together world class researchers working on fundamental and quantitative neuroscience. This special issue of the Journal of Combinatorial Optimization contains six papers originated from the presentations accepted to the conference and expanded in the light of discussions taken place at the conference. Each of the six selected papers in this issue underwent a rigorous extra refereeing and revision, and had been accepted by the stringent refereeing process. The issue demonstrates the range of different types of neuroscience problems being addressed