Neuroinformatics grand challenges.

The conference on Neural Circuit Reconstruction recently held at the Janelia Farm research campus of the Howard Hughes Medical Institute (www.hhmi.org/janelia/conf-007. html) dramatically exposed the need and urgency to advance towards the automated digital tracing of neural arbors. The scientific problem was agreed to be of great importance and a bottleneck in fundamental research endeavors. At the same time, a mismatch became apparent between the confidence of computational scientists that the problem is very solvable (if not already solved) and the frustration of experimental neuroscientists with the practical limits and inadequacy of the existing solutions. In practice, despite several commercially and academically available programs for automated reconstruction, the vast majority of axons and dendrites are still traced manually. The idea was thus launched to “challenge” algorithm developers with data provided by potential users. The resulting collegial competition would bring together computational scientists and neuroscientists to join forces and solve the problem. Such an unconventional interaction would likely lead to significant scientific advancements and would certainly result in an explicit assessment of the remaining obstacles. In fact, neuroscience is a field rich in examples of problems that are supposed to be solved in principle, but that in practice encounter many “exceptions.” An old joke describes the difference between theory and practice by stating that, in theory, there is no difference. A “grand challenge” format may be a proper empirical approach to make those differences explicit. Two recent implementations of this idea gave rise to the open-ended Neural Prediction Challenge (http://neuralprediction.berkeley.edu) and to the Competitions for Predicting Single-Neuron Behavior (http://icwww.epfl.ch/~gerstner/QuantNeuron Mod2007/challenge.html). While both of these initiatives deal with electrophysiological records, the former has a functional twist as it consists of responses to sensory stimulation. In contrast, the latter is based on direct neuronal stimulation and aims to predict with high precision spike timing and subthreshold membrane potential for arbitrary inputs. The identification of other grand challenges on the road to understanding neural computation was also the topic of a recent workshop (http://cnls.lanl.gov/neuralcomp). The National Institutes of Health have recently issued a new program on Predictive Multiscale Models of the Physiome in Health and Disease (http://grants.nih.gov/grants/guide/pafiles/PAR-08-023.html). Neuroinformatics and computational neuroscience clearly provide fertile ground for these applications, with the emphasis on data sharing, mining, and analysis (e.g. http://cosyne.org/wiki/Cosyne08_Data_ sharing_and_data_analysis_challenges_in_neuroscience) as also discussed by Teeters et al. (2008) in this issue. To flesh out this idea in more detail, let us consider again the digital representation of neuronal structure. The morphology of dendrites and axons constitutes a cardinal element of neuronal phenotype, and plays a determinant role in cellular excitability, synaptic integration, and network connectivity. Whole dendritic and axonal trees can be visualized with various combinations of histological and microscopic techniques, such as e.g. intracellular injection of biocytin and brightfield imaging, or expression of a fluorescent marker (e.g. green fluorescent protein (GFP)) and confocal microscopy. Starting from Cajal, Neuroinform (2008) 6:1–3 DOI 10.1007/s12021-008-9010-5