Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network

In this paper, a search for supersymmetry (SUSY) is presented in events with two opposite-sign isolated leptons in the final state, accompanied by hadronic jets and missing transverse energy. An artificial neural network is employed to discriminate possible SUSY signals from a standard model background. The analysis uses a data sample collected with the CMS detector during the 2011 LHC run, corresponding to an integrated luminosity of 4.98 fb-1 of proton-proton collisions at the center-of-mass energy of 7 TeV. Compared to other CMS analyses, this one uses relaxed criteria on missing transverse energy (EÌ̧T>40 GeV) and total hadronic transverse energy (HT>120 GeV), thus probing different regions of parameter space. Agreement is found between standard model expectation and observations, yielding limits in the context of the constrained minimal supersymmetric standard model and on a set of simplified models. © 2013 CERN.

Neural network activity in the neonatal rat barrel cortex in vivo

Coordinated patterns of electrical activity are important for the early development of sensory systems. The spatiotemporal dynamics of these early activity patterns and the role of the peripheral sensory input for their generation are essentially unknown. There are two projects in this thesis. In project1, we performed extracellular multielectrode recordings in the somatosensory cortex of postnatal day 0 to 7 rats in vivo and observed three distinct patterns of synchronized oscillatory activity. (1) Spontaneous and periphery-driven spindle bursts of 1–2 s in duration and ~10 Hz in frequency occurred approximately every 10 s. (2) Spontaneous and sensory-driven gamma oscillations of 150–300 ms duration and 30–40 Hz in frequency occurred every 10–30 s. (3) Long oscillations appeared only every ~20 min and revealed the largest amplitude (250–750 µV) and longest duration (>40 s). These three distinct patterns of early oscillatory activity differently synchronized the neonatal cortical network. Whereas spindle bursts and gamma oscillations did not propagate and synchronized a local neuronal network of 200–400 µm in diameter, long oscillations propagated with 25–30 µm/s and synchronized 600-800 µm large ensembles. All three activity patterns were triggered by sensory activation. Single electrical stimulation of the whisker pad or tactile whisker activation elicited neocortical spindle bursts and gamma activity. Long oscillations could be only evoked by repetitive sensory stimulation. The neonatal oscillatory patterns in vivo depended on NMDAreceptor-mediated synaptic transmission and gap junctional coupling. Whereas spindle bursts and gamma oscillations may represent an early functional columnar-like pattern, long oscillations may serve as a propagating activation signal consolidating these immature neuronal networks. In project2, Using voltage-sensitive dye imaging and simultaneous multi-channel extracellular recordings in the barrel cortex and somatosensory thalamus of newborn rats in vivo, we found that spontaneous and whisker stimulation induced activity patterns were restricted to functional cortical columns already at the day of birth. Spontaneous and stimulus evoked cortical activity consisted of gamma oscillations followed by spindle bursts. Spontaneous events were mainly generated in the thalamus or by spontaneous whisker movements. Our findings indicate that during early developmental stages cortical networks self-organize in ontogenetic columns via spontaneous gamma oscillations triggered by the thalamus or sensory periphery.

Long-term potentiation and neural network activity in the neonatal rat cerebral cortex in vivo

Long-term potentiation in the neonatal rat rnbarrel cortex in vivo rnLong-term potentiation (LTP) is important for the activity-dependent formation of early cortical circuits. In the neonatal rodent barrel cortex LTP has been so far only studied in vitro. I combined voltage-sensitive dye imaging with extracellular multi-electrode recordings to study whisker stimulation-induced LTP for both the slope of field potential and the number of multi-unit activity in the whisker-to-barrel cortex pathway of the neonatal rat barrel cortex in vivo. Single whisker stimulation at 2 Hz for 10 min induced an age-dependent expression of LTP in postnatal day (P) 0 to P14 rats with the strongest expression of LTP at P3-P5. The magnitude of LTP was largest in the stimulated barrel-related column, smaller in the surrounding septal region and no LTP could be observed in the neighboring barrel. Current source density analyses revealed an LTP-associated increase of synaptic current sinks in layer IV / lower layer II/III at P3-P5 and in the cortical plate / upper layer V at P0-P1. This study demonstrates for the first time an age-dependent and spatially confined LTP in the barrel cortex of the newborn rat in vivo. These activity-dependent modifications during the critical period may play an important role in the development and refinement of the topographic map in the barrel cortex. (An et al., 2012)rnEarly motor activity triggered by gamma and spindle bursts in neonatal rat motor cortexrnSelf-generated neuronal activity generated in subcortical regions drives early spontaneous motor activity, which is a hallmark of the developing sensorimotor system. However, the neuronal activity patterns and functions of neonatal primary motor cortex (M1) in the early movements are still unknown. I combined voltage-sensitive dye imaging with simultaneous extracellular multi-electrode recordings in the neonatal rat S1 and M1 in vivo. At P3-P5, gamma and spindle bursts observed in M1 could trigger early paw movements. Furthermore, the paw movements could be also elicited by the focal electrical stimulation of M1 at layer V. Local inactivation of M1 could significantly attenuate paw movements, suggesting that the neonatal M1 operates in motor mode. In contrast, the neonatal M1 can also operate in sensory mode. Early spontaneous movements and sensory stimulations of paw trigger gamma and spindle bursts in M1. Blockade of peripheral sensory input from the paw completely abolished sensory evoked gamma and spindle bursts. Moreover, both sensory evoked and spontaneously occurring gamma and spindle bursts mediated interactions between S1 and M1. Accordingly, local inactivation of the S1 profoundly reduced paw stimulation-induced and spontaneously occurring gamma and spindle bursts in M1, indicating that S1 plays a critical role in generation of the activity patterns in M1. This study proposes that both self-generated and sensory evoked gamma and spindle bursts in M1 may contribute to the refinement and maturation of corticospinal and sensorimotor networks required for sensorimotor coordination.rn

A neural network based technique for automatic classification of road cracks

This paper presents a neural network based technique for the classification of segments of road images into cracks and normal images. The density and histogram features are extracted. The features are passed to a neural network for the classification of images into images with and without cracks. Once images are classified into cracks and non-cracks, they are passed to another neural network for the classification of a crack type after segmentation. Some experiments were conducted and promising results were obtained. The selected results and a comparative analysis are included in this paper.

Spiking neural network-based target tracking control for autonomous mobile robots

In this paper, a target tracking controller based on spiking neural network is proposed for autonomous robots. This controller encodes the preprocessed environmental and target information provided by CCD cameras, encoders and ultrasonic sensors into spike trains, which are integrated by a three-layer spiking neural network (SNN). The outputs of SNN are generated based on the competition between the forward/backward neuron pair corresponding to each motor, with the weights evolved by the Hebbian learning. The application to target tracking of a mobile robot in unknown environment verifies the validity of the proposed controller.

Evolving an adaptive artificial neural network with a gravitational search algorithm

In this paper, a supervised fuzzy adaptive resonance theory neural network, i.e., Fuzzy ARTMAP (FAM), is integrated with a heuristic Gravitational Search Algorithm (GSA) that is inspired from the laws of Newtonian gravity. The proposed FAM-GSA model combines the unique features of both constituents to perform data classification. The classification performance of FAM-GSA is benchmarked against other state-of-art machine learning classifiers using an artificially generated data set and two real data sets from different domains. Comparatively, the empirical results indicate that FAM-GSA generally is able to achieve a better classification performance with a parsimonious network size, but with the expense of a higher computational load.