Aggression, anxiety and vocalizations in animals: GABA A and 5-HT anxiolytics

A continuing challenge for preclinical research on anxiolytic drugs is to capture the affective dimension that characterizes anxiety and aggression, either in their adaptive forms or when they become of clinical concern. Experimental protocols for the preclinical study of anxiolytic drugs typically involve the suppression of conditioned or unconditioned social and exploratory behavior (e.g., punished drinking or social interactions) and demonstrate the reversal of this behavioral suppression by drugs acting on the benzodiazepine-GABA A complex. Less frequently, aversive events engender increases in conditioned or unconditioned behavior that are reversed by anxiolytic drugs (e.g., fear-potentiated startle). More recently, putative anxiolytics which target 5-HT receptor subtypes produced effects in these traditional protocols that often are not systematic and robust. We propose ethological studies of vocal expressions in rodents and primates during social confrontations, separation from social companions, or exposure to aversive environmental events as promising sources of information on the affective features of behavior. This approach focusses on vocal and other display behavior with clear functional validity and homology. Drugs with anxiolytic effects that act on the benzodiazepine-GABA A receptor complex and on 5-HT 1A receptors systematically and potently alter specific vocalizations in rodents and primates in a pharmacologically reversible manner; the specificity of these effects on vocalizations is evident due to the effectiveness of low doses that do not compromise other physiological and behavioral processes. Antagonists at the benzodiazepine receptor reverse the effects of full agonists on vocalizations, particularly when these occur in threatening, startling and distressing contexts. With the development of antagonists at 5-HT receptor subtypes, it can be anticipated that similar receptor-specificity can be established for the effects of 5-HT anxiolytics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46351/1/213_2005_Article_BF02245590.pd

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

AbstractLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.</jats:p