The tremendous technological advance of experimental methods in neurobiology over recent years has generated extensive data sets about the activities of many neurons in parallel. These data describe collective neuronal processing to a level of detail that only several years ago would have been considered impossible by many researchers. We now have the ability to do multielectrode recordings of extracellular spiking from over a hundred neurons in parallel; further, two-photon microscopy allows parallel intracellular calcium imaging of even hundreds of neurons. The leading labs presently strive to bring these techniques into behaving animal research and to further increase the number of recorded single cells - and even cellular compartments.
These developments pose an enormous challenge to theoretical neuroscientists. On the one hand, more efficient data analysis methods are required to harvest the full potential of these recordings. On the other hand, these data allow a new way of envisioning and validating computational models of neural systems. The networks in the brain are now understood to evolve from an abstract computational entity to experimentally tractable dynamical systems. The possible implications of this are significant, potentially slowly closing the gap between microcircuits and large scale signals as EEG or MRI.
Network: Computation in Neural Systems publishes theoretical neuroscience research that is driven by experimental data, with a particular focus on such new technologies. Papers are welcome from both the data analysis and the biologically motivated modeling perspective.
The articles should rest on a solid mathematical background and be accessible to neurobiologists, psychologists, and cognitive scientists.
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- Editor-in-Chief: Christian Leibold, Ludwig-Maximilians-Universität, München, Germany (firstname.lastname@example.org)