# Yuriy Mishchenko Papers:

Mishchenko Y., Paninski L. (2012) "A Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data.", Journal of Computational Neuroscience, 33(2), 371

In recent years, the problem of reconstructing the connectivity in
large neural circuits ("connectomics") has re-emerged as one of the
main objectives of neuroscience. Classically, reconstructions of
neural connectivity have been approached anatomically, using electron
or light microscopy and histological tracing methods. This paper
describes a statistical approach for connectivity reconstruction that
relies on relatively easy-to-obtain measurements using fluorescent probes
such as synaptic markers, cytoplasmic dyes, transsynaptic tracers, or
activity-dependent dyes. We describe the possible design of these
experiments and develop a Bayesian framework for extracting synaptic
neural connectivity from such data. We show that the
statistical reconstruction problem can be formulated naturally as a
tractable L_1-regularized quadratic optimization. As a concrete
example, we consider a realistic hypothetical connectivity
reconstruction experiment in C. elegans, a popular neuroscience model
where a complete wiring diagram has been previously obtained based on
long-term electron microscopy work. We show that the new statistical
approach could lead to an orders of magnitude reduction in
experimental effort in reconstructing the connectivity in this
circuit. We further demonstrate that the spatial heterogeneity and
biological variability in the connectivity matrix - not just the
"average" connectivity - can also be estimated using the same method. Full text