Research Summary:

The research in my group is aimed at understanding the principles of synaptic connectivity in the cerebral cortex. Most of my research projects are linked by a common theme which can be summarized as: inferring synaptic connectivity through the quantitative analysis of neuron morphology. The topics of interest range from the theoretical and computational analyses of real and artificial neural networks and their memory storage capacity, to building cortical connectivity diagrams based on the experimental datasets of reconstructed neurons, to developing algorithms for automated reconstruction of neural networks from confocal microscopy stacks of images.

Recent Publications:

Wen, Q., Stepanyants, A., Elston, G.N., Grosberg, A.Y., and Chklovskii, D.B., Maximization of the connectivity repertoire as a statistical principle governing the shapes of dendritic arbors, PNAS, (2009).

Stepanyants, A., Martinez, L.M., Ferecskó, A.S., and Kisvárday, Z.F., The fractions of short- and long-range connections in the visual cortex, PNAS, 106(9): 3555-3560 (2009).

Vasilkoski, Z. and Stepanyants, A., "Detection of the optimal neuron traces in confocal microscopy images", J. Neuroscience Methods, 178(1): 197-204 (2009).

Escobar, G., Fares, T., and Stepanyants, A., Structural plasticity of circuits in cortical neuropil, J. Neuroscience, 28(34): 8477-8488 (2008).

Stepanyants, A., Hirsch, J.A., Martinez, L.M., Kisvárday, Z.F., Ferecskó, A.S., and Chklovskii, D.B., Local potential connectivity in cat primary visual cortex, Cerebral Cortex, 18(1): 13-28 (2008).

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