Spatiotemporal Rules Research Articles

Designing driver assistance systems with crossmodal signals: multisensory integration rules for saccadic reaction times apply.

Modern driver assistance systems make increasing use of auditory and tactile signals in order to reduce the driver's visual information load. This entails potential crossmodal interaction effects that need to be taken into account in designing an optimal system. Here we show that saccadic reaction times to visual targets (cockpit or outside mirror), presented in a driving simulator environment and accompanied by auditory or tactile accessories, follow some well-known spatiotemporal rules of multisensory integration, usually found under confined laboratory conditions. Auditory nontargets speed up reaction time by about 80 ms. The effect tends to be maximal when the nontarget is presented 50 ms before the target and when target and nontarget are spatially coincident. The effect of a tactile nontarget (vibrating steering wheel) was less pronounced and not spatially specific. It is shown that the average reaction times are well-described by the stochastic “time window of integration” model for multisensory integration developed by the authors. This two-stage model postulates that crossmodal interaction occurs only if the peripheral processes from the different sensory modalities terminate within a fixed temporal interval, and that the amount of crossmodal interaction manifests itself in an increase or decrease of second stage processing time. A qualitative test is consistent with the model prediction that the probability of interaction, but not the amount of crossmodal interaction, depends on target–nontarget onset asynchrony. A quantitative model fit yields estimates of individual participants' parameters, including the size of the time window. Some consequences for the design of driver assistance systems are discussed.

Spatio-temporal Binding: An Adaptation of the Peak Interval Procedure to Assess Spatio-temporal Learning in Rats

Time and space are commonly approached as two distinct dimensions, and rarely combined together in a single task. In this project, we designed a new version of the peak interval procedure in which we included a spatial component. The aim of this experiment was to investigate the learning of a spatio-temporal rule in animals. Adult male Sprague-Dawley rats were placed in front of a 5 nose-poke wall where a given hole could be visited after a precise time after a sound onset in a Fixed Interval (FI) schedule. The animals were presented two different contexts, differentiable by the house-light (A-red or B-green), tone frequency (1 or 11kHz), and spatio-temporal combination rule given by lighting a specific hole and associating it to a specific FI value (i.e., Hole 2-FI 15s vs. Hole 4-FI 60s). Non-reinforced probe trials of longer duration than the FI values (45s for FI15 and 120s for FI60) were pseudo-randomly presented to ascertain temporal peak interval functions. After 8 weeks of training, all the animals were able to differentiate the two contexts as shown by the number of nose-pokes in the correct hole peaking at the appropriate time, while virtually no activity was recorded in the other holes. In a single Pavlovian session of 8 trials, we changed the spatio- temporal rule for half of the animals: in the second context, the hole 1 was reinforced at 30seconds. Here we showed that this one session was sufficient for the animals to learn the new spatio-temporal association. Contrary to the control group, the experimental group exhibited a behavioral pattern associated to each hole indicative of a possible spatio-temporal binding. Interestingly, the behavior in the other control Context, in which the spatio-temporal rule had not been changed, was preserved in both groups. The experiment was followed for an extra 10 days after the Pavlovian Shift. At the end of the experiment, all the animals were perfectly and fully adapted to new spatio-temporal rule. In conclusion, the study suggests that rats are able to learn a spatio-temporal association in a single session, and that the two (space, time) dimensions may be learned together and not separately.

Geographic divisions and modeling of virological data on seasonal influenza in the Chinese mainland during the 2006-2009 monitoring years.

BackgroundSeasonal influenza epidemics occur annually with bimodality in southern China and unimodality in northern China. Regional differences exist in surveillance data collected by the National Influenza Surveillance Network of the Chinese mainland. Qualitative and quantitative analyses on the spatiotemporal rules of the influenza virus's activities are needed to lay the foundation for the surveillance, prevention and control of seasonal influenza.MethodsThe peak performance analysis and Fourier harmonic extraction methods were used to explore the spatiotemporal characteristics of the seasonal influenza virus activity and to obtain geographic divisions. In the first method, the concept of quality control was introduced and robust estimators were chosen to make the results more convincing. The dominant Fourier harmonics of the provincial time series were extracted in the second method, and the VARiable CLUSter (VARCLUS) procedure was used to variably cluster the extracted results. On the basis of the above geographic division results, three typical districts were selected and corresponding sinusoidal models were applied to fit the time series of the virological data.ResultsThe predominant virus during every peak is visible from the bar charts of the virological data. The results of the two methods that were used to obtain the geographic divisions have some consistencies with each other and with the virus activity mechanism. Quantitative models were established for three typical districts: the south1 district, including Guangdong, Guangxi, Jiangxi and Fujian; the south2 district, including Hunan, Hubei, Shanghai, Jiangsu and Zhejiang; and the north district, including the 14 northern provinces except Qinghai. The sinusoidal fitting models showed that the south1 district had strong annual periodicity with strong winter peaks and weak summer peaks. The south2 district had strong semi-annual periodicity with similarly strong summer and winter peaks, and the north district had strong annual periodicity with only winter peaks.

Interesting spatiotemporal rules discovery: application to remotely sensed image databases

Purpose – Knowledge discovery in databases aims to discover useful and significant information from multiple databases. However, in the remote sensing field, the large size of discovered information makes it hard to manually look for interesting information quickly and easily. The purpose of this paper is to automate the process of identifying interesting spatiotemporal knowledge (expressed as rules).Design/methodology/approach – The proposed approach is based on case‐based reasoning (CBR) process. CBR allows the recognition of useful and interesting rules by simulating a human reasoning process, and combining objective and subjective interestingness measures. It takes advantage of statistics' power from objective criteria and the reliability of subjective criteria. This helps improve the discovery of interesting rules by taking into consideration the different properties of interestingness measures.Findings – The proposed approach combines several interestingness measures with complementary properties to...

Mining Long, Sharable Patterns in Trajectories of Moving Objects

The efficient analysis of spatio-temporal data, generated by moving objects, is an essential requirement for intelligent location-based services. Spatio-temporal rules can be found by constructing spatio-temporal baskets, from which traditional association rule mining methods can discover spatio-temporal rules. When the items in the baskets are spatio-temporal identifiers and are derived from trajectories of moving objects, the discovered rules represent frequently travelled routes. For some applications, e.g., an intelligent ridesharing application, these frequent routes are only interesting if they are long and sharable, i.e., can potentially be shared by several users. This paper presents a database projection based method for efficiently extracting such long, sharable frequent routes. The method prunes the search space by making use of the minimum length and sharable requirements and avoids the generation of the exponential number of sub-routes of long routes. Considering alternative modelling options for trajectories, leads to the development of two effective variants of the method. SQL-based implementations are described, and extensive experiments on both real life- and large-scale synthetic data show the effectiveness of the method and its variants.

Identifying the rules of engagement enabling in silico leukocyte rolling, activation, and adhesion

Objectives: Leukocyte rolling, activation, and adhesion on endothelial cell surfaces are key events during leukocyte recruitment to tissues during the inflammatory process. The interactions between leukocytes and endothelial cell surfaces during rolling, activation, and adhesion are complex and involve ligand-binding events between several combinations of receptorligand pairs. Spatially restricted signaling events between chemokine receptors and local integrins as well as lateral diffusion and clustering of integrins are thought to play important roles in mediating the transition from rolling to adhesion. The objective of this project was to develop a novel computational model that aimed at gaining a fundamental understanding of the spatiotemporal events and key rules of engagement that determine when and how leukocytes are able to adhere to endothelial cell surfaces during inflammatory conditions. Methods: We used the synthetic modeling approach to construct a multilevel, multiagent, in silico model of leukocyte rolling, activation, and adhesion. Object-oriented software components were designed, verified, plugged together, and then operated in ways that represent the mechanisms and processes believed responsible for leukocyte rolling and adhesion. The in silico system constructed was a discrete event, discrete space, and discrete time analogue of the entire parallel plate flow chamber system commonly used to study leukocyte rolling and adhesion in vitro. This modeling approach allowed us to represent the spatial and discrete event phenomena that are thought to occur in vitro and in vivo. Results: We previously reported details of the model and initial results of in silico rolling and adhesion on P-selectin and VCAM-1 substrate in the presence of GRO-alpha chemokine (PMID 17408504): in silico leukocyte movement is a function of (1) number and type of bonds, and (2) location of the bond-containing patches at the interface. Our current effort has extended the model and increased resolution to enable exploration of the role of lateral diffusion and clustering of LFA-1 integrin in mediating rolling and adhesion to ICAM-1 substrate. Conclusions: These new methods represent important progress in gaining a fundamental understanding of the spatiotemporal events and key rules of engagement that determine when and how leukocytes adhere to endothelial cell surfaces during inflammation. The current model and results represent an important step in developing scientifically useful validated simulations of leukocyte recruitment under physiologic and pathophysiologic conditions.

A multilayer feedforward neural network model for visual motion perception

The local visual motion detection mechanism used in the visual systems of primates can only sense the motion component oriented perpendicularly to the contrast gradient of the brightness pattern. But the visual system of higher animals can adaptively determine the actual direction of motion through a learning process. In this paper a multilayered feedforward neural network model for perception of visual motion is presented. This model employs W. Reichardt’s elementary motion detectors array and T. Kohonen’s self-organizing feature map. We explored the self-organizing principles for perception of visual motion. The computer simulations show that this neural network is able to recognize the true direction of motion through an unsupervised learning process. In addition, the neurons with the same or similar motion direction selectivity tend to appear in “functional columns” which seem to be qualitatively similar to the cortical motion columns observed by electrophysiological and cytohistochemical studies in certain higher areas such as MT. It proves that motion-detection by spatio-temporal coherences, mapping, cooperation, competition, and Hebb rule may be the basic principles for the self-organization of visual motion perception networks.