Generation and Disruption of Circadian Rhythms in the Suprachiasmatic Nucleus: A Core-Shell Model.

We focus our research on how the core-shell organization controls behavior of the suprachiasmatic nucleus (SCN), how the core and shell are synchronized to the environment, what impact they have on the behavior of the SCN under different lighting conditions, and what mechanisms disrupt synchronization. To this end, we use a reduced Kuramoto model, with parameters inferred from experimental observations and calibrated for mice, and perform a detailed comparison between the model and experimental data under light-dark (LD), dark-dark (DD), and light-light (LL) conditions. The operating limits of free-running and entrained SCN activity under symmetric LD cycles are analyzed, with particular focus on the phenomena of anticipation and dissociation.

Statistical analysis of unidirectional and reciprocal chemical connections in the C. elegans connectome.

We analyze unidirectional and reciprocally connected pairs of neurons in the chemical connectomes of the male and hermaphrodite Caenorhabditis elegans, using recently published data. Our analysis reveals that reciprocal connections provide communication between most neurons with chemical synapses, and comprise on average more synapses than both unidirectional connections and the entire connectome. We further reveal that the C.

The central role of peripheral nodes in directed network dynamics.

Many social, technological, and biological systems with asymmetric interactions display a variety of collective phenomena, such as opinion formation and synchronization. This has motivated much research on the dynamical impact of local and mesoscopic structure in directed networks. However, the unique constraints imposed by the global organization of directed networks remain largely undiscussed.

Construction and analysis of a human testis/sperm-enriched interaction network: Unraveling the PPP1CC2 interactome.

Background: Phosphoprotein phosphatase 1 catalytic subunit gamma 2 (PPP1CC2), a PPP1CC tissue-specific alternative splice restricted to testicular germ cells and spermatozoa, is essential for spermatogenesis and spermatozoa motility. The key to understand PPP1CC2 regulation lies on the characterization of its interacting partners.

Methods: We construct a testis/sperm-enriched protein interaction network and analyzed the topological properties and biological context of the network.

Amyloid precursor protein interaction network in human testis: sentinel proteins for male reproduction.

Background: Amyloid precursor protein (APP) is widely recognized for playing a central role in Alzheimer's disease pathogenesis. Although APP is expressed in several tissues outside the human central nervous system, the functions of APP and its family members in other tissues are still poorly understood. APP is involved in several biological functions which might be potentially important for male fertility, such as cell adhesion, cell motility, signaling, and apoptosis.

Investigation of efficient features for image recognition by neural networks.

In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier.

Generalization of features in the assembly neural networks.

The purpose of the paper is an experimental study of the formation of class descriptions, taking place during learning, in assembly neural networks. The assembly neural network is artificially partitioned into several sub-networks according to the number of classes that the network has to recognize. The features extracted from input data are represented in neural column structures of the sub-networks.

Inhibitory connections in the assembly neural network for texture segmentation.

A neural network with assembly organization is described. This assembly network is applied to the problem of texture segmentation in natural scenes. The network is partitioned into several subnetworks: one for each texture class.