A bistatic SAR(synthetic aperture radar) system based on GPS(global positioning system) time synchronization module is proposed in this study. To configure a bistatic SAR system, two GPS time-synchronized SAR transceivers are operated as a moving transmitter and a stationary receiver, respectively. The GPS time synchronization module generates a reference clock of 10MHz and a trigger signal to control the time synchronization of both SAR transceivers for data acquisition. To implement the proposed method, the experimental device consisting of a ready-made GPS-DO(GPS disciplined oscillator) and a self-manufactured GTSM(GPS time synchronization module) was added to the Echo-SAR system, which is a chirp pulse-based radar transceiver. These synchronization modules make it possible to temporally synchronize two same SAR transceivers separated physically. To reconstruct a bistatic SAR image, optimum synchronization parameters were extracted from raw data collected through a field campaign, and an optimized procedure of bistatic SAR signal processing based on the back-projection algorithm was established.

In humans and animals, reward prediction error encoded by dopamine systems is thought to be important in the temporal difference learning class of reinforcement learning (RL). With RL algorithms, many brain models have described the function of dopamine and related areas, including the basal ganglia and frontal cortex. In spite of this importance, how the reward prediction error itself is computed is not understood well, including the problem of how the current states are assigned to a memorized states and how the values of the states are memorized. In this paper, we describe a neocortical model for memorizing state space and computing reward prediction error, known as `reward hierarchical temporal memory' (rHTM). In this model, the temporal relationships among events are hierarchically stored. Using this memory, rHTM computes reward prediction errors by associating the memorized sequences to rewards and inhibits the predicted reward. In a simulation, our model behaved similarly to dopaminergic neurons. We suggest that our model can provide a hypothetical framework of interaction between cortex and dopamine neurons.

Learning invariant representations of environments through experience has been important area of research both in the field of machine learning as well as in computational neuroscience. In the present study, we propose a novel unsupervised method for the discovery of invariants from a single video input based on the learning of the spatio-temporal relationship of inputs. In an experiment, we tested the learning of spatio-temporal invariant features from a single video that involves rotational movements of faces of several subjects. From the results of this experiment, we demonstrate that the proposed system for the learning of invariants based on spatio-temporal continuity can be used as a compelling unsupervised method for learning invariants from an input that includes temporal information.

UNLABELLED: Protein Structural Interactome map (PSIMAP) is a global interaction map that describes domain-domain and protein-protein interaction information for known Protein Data Bank structures. It calculates the Euclidean distance to determine interactions between possible pairs of structural domains in proteins. PSIbase is a database and file server for protein structural interaction information calculated by the PSIMAP algorithm. PSIbase also provides an easy-to-use protein domain assignment module, interaction navigation and visual tools. Users can retrieve possible interaction partners of their proteins of interests if a significant homology assignment is made with their query sequences. AVAILABILITY: http://psimap.org and http://psibase.kaist.ac.kr/

BACKGROUND: Most proteins function by interacting with other molecules. Their interaction interfaces are highly conserved throughout evolution to avoid undesirable interactions that lead to fatal disorders in cells. Rational drug discovery includes computational methods to identify the interaction sites of lead compounds to the target molecules. Identifying and classifying protein interaction interfaces on a large scale can help researchers discover drug targets more efficiently. DESCRIPTION: We introduce a large-scale protein domain interaction interface database called InterPare http://interpare.net. It contains both inter-chain (between chains) interfaces and intra-chain (within chain) interfaces. InterPare uses three methods to detect interfaces: 1) the geometric distance method for checking the distance between atoms that belong to different domains, 2) Accessible Surface Area (ASA), a method for detecting the buried region of a protein that is detached from a solvent when forming multimers or complexes, and 3) the Voronoi diagram, a computational geometry method that uses a mathematical definition of interface regions. InterPare includes visualization tools to display protein interior, surface, and interaction interfaces. It also provides statistics such as the amino acid propensities of queried protein according to its interior, surface, and interface region. The atom coordinates that belong to interface, surface, and interior regions can be downloaded from the website. CONCLUSION: InterPare is an open and public database server for protein interaction interface information. It contains the large-scale interface data for proteins whose 3D-structures are known. As of November 2004, there were 10,583 (Geometric distance), 10,431 (ASA), and 11,010 (Voronoi diagram) entries in the Protein Data Bank (PDB) containing interfaces, according to the above three methods. In the case of the geometric distance method, there are 31,620 inter-chain domain-domain interaction interfaces and 12,758 intra-chain domain-domain interfaces.