Search results for "Neural"
showing 10 items of 2783 documents
A neural network clustering algorithm for the ATLAS silicon pixel detector
2014
A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. …
A novel arousal-based individual screening reveals susceptibility and resilience to PTSD-like phenotypes in mice
2021
Translational animal models for studying post-traumatic stress disorder (PTSD) are valuable for elucidating the poorly understood neurobiology of this neuropsychiatric disorder. These models should encompass crucial features, including persistence of PTSD-like phenotypes triggered after exposure to a single traumatic event, trauma susceptibility/resilience and predictive validity. Here we propose a novel arousal-based individual screening (AIS) model that recapitulates all these features. The AIS model was designed by coupling the traumatization (24 h restraint) of C57BL/6 J mice with a novel individual screening. This screening consists of z-normalization of post-trauma changes in startle …
Machine Learning Identification of Pro-arrhythmic Structures in Cardiac Fibrosis
2021
Cardiac fibrosis and other scarring of the heart, arising from conditions ranging from myocardial infarction to ageing, promotes dangerous arrhythmias by blocking the healthy propagation of cardiac excitation. Owing to the complexity of the dynamics of electrical signalling in the heart, however, the connection between different arrangements of blockage and various arrhythmic consequences remains poorly understood. Where a mechanism defies traditional understanding, machine learning can be invaluable for enabling accurate prediction of quantities of interest (measures of arrhythmic risk) in terms of predictor variables (such as the arrangement or pattern of obstructive scarring). In this st…
Relations between basal ganglia and hippocampus: Action of substantia nigra and pallidum
1986
Several interrelationships exist between basal ganglia and hippocampus. The ventral striatum appears to be involved in the control of the dopaminergic nigro-striatal pathway. The caudate, in turn, seems to influence the hippocampal theta rhythm and to inhibit hippocampal spikes. In the present work the role played by globus pallidus pars interna and substantia nigra pars compacta on hippocampal bioelectrical activity is studied. Injection of sodium penicillin i.v. produces steady interictal spikes in the hippocampus. Substantia nigra stimulation induces regular theta rhythm and inhibits the spikes. Pallidal stimulation, on the contrary, appears to strongly enhance epileptiform activity, pro…
Moderate-Vigorous Physical Activity across Body Mass Index in Females : Moderating Effect of Endocannabinoids and Temperament
2014
Altres ajuts: This manuscript was supported by grants from Instituto Salud Carlos III (FIS PI11/210 and CIBERobn). Sarah Sauchelli is recipient of a pre-doctoral Grant (2013-17) by IDIBELL. Jose C. Fernández-García is recipient of a 'Rio Hortega' contract from 'Instituto de Salud Carlos III', Madrid, Spain (CM12/00059). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Endocannabinoids and temperament traits have been linked to both physical activity and body mass index (BMI) however no study has explored how these factors interact in females. The aims of this cross-sectional study were to 1) examine differences…
Presynaptic CB1 Receptors Regulate Synaptic Plasticity at Cerebellar Parallel Fiber Synapses
2011
Endocannabinoids are potent regulators of synaptic strength. They are generally thought to modify neurotransmitter release through retrograde activation of presynaptic type 1 cannabinoid receptors (CB1Rs). In the cerebellar cortex, CB1Rs regulate several forms of synaptic plasticity at synapses onto Purkinje cells, including presynaptically expressed short-term plasticity and, somewhat paradoxically, a postsynaptic form of long-term depression (LTD). Here we have generated mice in which CB1Rs were selectively eliminated from cerebellar granule cells, whose axons form parallel fibers. We find that in these mice, endocannabinoid-dependent short-term plasticity is eliminated at parallel fiber…
Honeybee (Apis mellifera) vision can discriminate between and recognise images of human faces.
2005
SUMMARY Recognising individuals using facial cues is an important ability. There is evidence that the mammalian brain may have specialised neural circuitry for face recognition tasks, although some recent work questions these findings. Thus, to understand if recognising human faces does require species-specific neural processing, it is important to know if non-human animals might be able to solve this difficult spatial task. Honeybees (Apis mellifera) were tested to evaluate whether an animal with no evolutionary history for discriminating between humanoid faces may be able to learn this task. Using differential conditioning, individual bees were trained to visit target face stimuli and to …
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
2016
This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). Our paper addresses this efficiency issue. Instead of a numerical deconvolution in previous work, we precompute a feed-forward, strided convolutional network that captures the feature statistics of Markovian patches and is able to directly generate outputs of arbitrary dimensions. Such network can directly decode brown noise to realistic textu…
Leveraging Uncertainty Estimates to Improve Segmentation Performance in Cardiac MR
2021
In medical image segmentation, several studies have used Bayesian neural networks to segment and quantify the uncertainty of the images. These studies show that there might be an increased epistemic uncertainty in areas where there are semantically and visually challenging pixels. The uncertain areas of the image can be of a great interest as they can possibly indicate the regions of incorrect segmentation. To leverage the uncertainty information, we propose a segmentation model that incorporates the uncertainty into its learning process. Firstly, we generate the uncertainty estimate (sample variance) using Monte-Carlo dropout during training. Then we incorporate it into the loss function t…
Applying Artificial Intelligence Methods to Detect and Classify Fish Calls from the Northern Gulf of Mexico
2021
Passive acoustic monitoring is a method that is commonly used to collect long-term data on soniferous animal presence and abundance. However, these large datasets require substantial effort for manual analysis