Structured learning of assignment models for neuron reconstruction to minimize topological errors

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Structured learning provides a powerful framework for empirical risk minimization on the predictions of structured models. It allows end-to-end learning of model parameters to minimize an application specific loss function. This framework is particularly well suited for discrete optimization models that are used for neuron reconstruction from anisotropic electron microscopy (EM) volumes. However, current methods are still learning unary potentials by training a classifier that is agnostic about the model it is used in. We believe the reason for that lies in the difficulties of (1) finding a representative training sample, and (2) designing an application specific loss function that captures the quality of a proposed solution. In this paper, we show how to find a representative training sample from human generated ground truth, and propose a loss function that is suitable to minimize topological errors in the reconstruction. We compare different training methods on two challenging EM-datasets. Our structured learning approach shows consistently higher reconstruction accuracy than other current learning methods.Peer ReviewedPostprint (author's final draft

Differences in Organization of Neural Oxytocin Receptor Reflects Differences in Sex Behavior and Parental Care

Oxytocin (OXT) is a neuropeptide synthesized in the hypothalamus and stored in the posterior pituitary gland. Its most known functions include being involved in the stimulation of the uterus during childbirth and allowing for milk letdown in the process of lactation after birth. OXT has also been implicated to play a role in parental care and sex behavior. Specific binding to brain oxytocin receptors (OXTR) was observed by in vitro receptor autoradiography with an iodinated OXT analogue in the monogamous prairie vole (Microtus ochrogaster) and the polygamous montane vole (Microtus montanus). What was found was that OXTR density in the prairie vole was highest in the prelimbic cortex, bed nucleus of the stria terminalis, nucleus accumbens, midline nucleus of the thalamus, and the lateral amygdala. In contrast, OXTR density in the montane vole was highest in the lateral septum, ventromedial nucleus of the hypothalamus, and cortical nucleus of the amygdala. Prairie voles show a high level of parental care in both naïve and postpartum individuals, whereas montane voles show a much higher level of parental care postpartum. Differences in OXTR distribution were also seen in two additional species of voles, the monogamous pine vole (Microtus pinetorum) and the polygamous meadow vole (Microtus pennsylvanicus). The receptor distribution of two other neurotransmitter systems that play a role in the mediation of social behavior (benzodiazepines and µ opioids) were also studied but showed little comparable differences between monogamous and polygamous species, which further highlights the importance of OXTR distribution as a mediator of social behavior

Simple Lattice-Models of Ion Conduction: Counter Ion Model vs. Random Energy Model

The role of Coulomb interaction between the mobile particles in ionic conductors is still under debate. To clarify this aspect we perform Monte Carlo simulations on two simple lattice models (Counter Ion Model and Random Energy Model) which contain Coulomb interaction between the positively charged mobile particles, moving on a static disordered energy landscape. We find that the nature of static disorder plays an important role if one wishes to explore the impact of Coulomb interaction on the microscopic dynamics. This Coulomb type interaction impedes the dynamics in the Random Energy Model, but enhances dynamics in the Counter Ion Model in the relevant parameter range.Comment: To be published in Phys. Rev.

Contributions to the mixed-alkali effect in molecular dynamics simulations of alkali silicate glasses

The mixed-alkali effect on the cation dynamics in silicate glasses is analyzed via molecular dynamics simulations. Observations suggest a description of the dynamics in terms of stable sites mostly specific to one ionic species. As main contributions to the mixed--alkali slowdown longer residence times and an increased probability of correlated backjumps are identified. The slowdown is related to the limited accessibility of foreign sites. The mismatch experienced in a foreign site is stronger and more retarding for the larger ions, the smaller ions can be temporarily accommodated. Also correlations between unlike as well as like cations are demonstrated that support cooperative behavior.Comment: 10 pages, 12 figures, 1 table, revtex4, submitted to Phys. Rev.