Special issue from the 2017 International Conference of Mathematical 
Neuroscience

Kilpatrick, Z.P.; Gjorgjieva, Julijana; Rosenbaum, R.

doi:10.1186/s13408-018-0069-5

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Special issue from the 2017 International Conference of Mathematical Neuroscience

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Gjorgjieva, Julijana
Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Max Planck Society;

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Citation

Kilpatrick, Z., Gjorgjieva, J., & Rosenbaum, R. (2019). Special issue from the 2017 International Conference of Mathematical Neuroscience. J. Math.Neurosci., 9(1). doi:10.1186/s13408-018-0069-5.

Cite as: https://hdl.handle.net/21.11116/0000-0006-06D2-6

Abstract

The ongoing acquisition of large and multifaced data sets in neuroscience requires new mathematical tools for quantitatively grounding these experimental findings. Since 2015, the International Conference on Mathematical Neuroscience (ICMNS) has provided a forum for researchers to discuss current mathematical innovations emerging in neuroscience. This special issue assembles currenct research ant tutorials that were presented at the 2017 ICMNS held in Boulder, Colorado from May 30 to June 2. Topics discussed at the meeting inlcude correlation analysis of network activity, information theory for plastic synapses, combinatorics for attractor neural networks, and novel data assimilation methods for neuroscience-all of which are represented in this special issue.