For many years, measurements of neural activity have either been restricted to recordings from single neurons or a very small number of neurons, and anatomical reconstructions to very sparse and incomplete neural circuits. Major advances in optical imaging (e.g. 2-photon and light-sheet microscopic imaging of calcium signals) and new electrode array technologies are now beginning to provide measurements of neural activity at an unprecedented scale. High-profile initiatives such as BRAIN (Brain Research through Advancing Innovative Neurotechnologies) will fuel the development of ever more powerful techniques for mapping the structure and activity of neural circuits. Computational tools will be important to both the high-throughput acquisition of these large-scale datasets and in the analysis. Acquiring, analyzing and integrating these sources of data raises major challenges and opportunities for computational neuroscience and machine learning: i) What kind of data will be generated by large-scale functional measurements in the next decade? How will it be quantitatively or qualitatively different to the kind of data we have had previously? ii) Algorithmic methods have played an important role in data acquisition, e.g. spike-sorting algorithms or spike-inference algorithms from calcium traces. In the future, what role will computational tools play in the process of high-throughput data acquistion? iii) One of the key-challenges is to link anatomical with functional data -- what computational analysis tools will help in providing a link between these two disparate source of data? What can we learn by measuring ‘functional connectivity’? iv) What have we really learned from high-dimensional recordings that is new? What will we learn? What theories could we test, if only we had access to recordings from more neurons at the same time? We have invited scientists whose research addresses these questions including prominent technologists, experimental neuroscientists, theorists and computational neuroscientists. We foresee active discussions amongst this multi-disciplinary group of scientists to catalyze exciting new research and collaborations. Important dates: Oct 09, 2013: Abstract submission deadline (for poster presentations) Oct 23, 2013: Acceptance for poster presentations Dec 10, 2013: Workshop Partial funding for this workshop will be provided by the Bernstein Center for Computational Neuroscience Tübingen.
http://www.bccn-tuebingen.de/events/nips-workshop-2013.html
