The Experts below are selected from a list of 53559 Experts worldwide ranked by ideXlab platform
Linda Partridge - One of the best experts on this subject based on the ideXlab platform.
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a computational model of the escape Response Latency in the giant fiber system of drosophila melanogaster
eNeuro, 2019Co-Authors: Asaph Zylbertal, Hrvoje Augustin, Linda PartridgeAbstract:Abstract The giant fiber system (GFS) is a multi-component neuronal pathway mediating rapid escape Response in the adult fruit-fly Drosophila melanogaster, usually in the face of a threatening visual stimulus. Two branches of the circuit promote the Response by stimulating an escape jump followed by flight initiation. A recent work demonstrated an age-associated decline in the speed of signal propagation through the circuit, measured as the stimulus-to-muscle depolarization Response Latency. The decline is likely due to the diminishing number of inter-neuronal gap junctions in the GFS of ageing flies. In this work, we presented a realistic conductance-based, computational model of the GFS that recapitulates the experimental results and identifies some of the critical anatomical and physiological components governing the circuit’s Response Latency. According to our model, anatomical properties of the GFS neurons have a stronger impact on the transmission than neuronal membrane conductance densities. The model provides testable predictions for the effect of experimental interventions on the circuit’s performance in young and ageing flies.
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a computational model of the escape Response Latency in the giant fiber system of drosophila melanogaster
bioRxiv, 2018Co-Authors: Asaph Zylbertal, Hrvoje Augustin, Linda PartridgeAbstract:ABSTRACT The Giant Fiber System (GFS) is a multi-component neuronal pathway mediating rapid escape Response in the adult fruit-fly Drosophila melanogaster, usually in the face of a threatening visual stimulus. Two branches of the circuit promote the Response by stimulating an escape jump followed by flight initiation. Our recent work demonstrated an age-associated decline in the speed of signal propagation through the circuit, measured as the stimulus-to-muscle depolarization Response Latency. The decline is likely due to the diminishing number of interneuronal gap junctions in the GFS of ageing flies. In this work, we presented a realistic conductance-based, computational model of the GFS that recapitulates our experimental results and identifies some of the critical anatomical and physiological components governing the circuit’s Response Latency. According to our model, anatomical properties of the GFS neurons have a stronger impact on the transmission than neuronal membrane conductance densities. The model provides testable predictions for the effect of experimental interventions on the circuit’s performance in young and ageing flies.
Haiying Shen - One of the best experts on this subject based on the ideXlab platform.
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iask a distributed q a system incorporating social community and global collective intelligence
International Conference on Peer-to-Peer Computing, 2015Co-Authors: Guoxin Liu, Haiying ShenAbstract:Traditional web-based Question and Answer (Q&A) websites cannot easily solve non-factual questions to match askers’ preference. Recent research efforts begin to study social-based Q&A systems that rely on an asker’s social friends to provide answers. However, this method cannot find answerers for a question not belonging to the asker’s interests. To solve this problem, we propose a distributed Q&A system incorporating both social community intelligence and global collective intelligence, named as iASK. iASK improves the Response Latency and answer quality in both the social domain and global domain. It uses a neural network based friend ranking method to identify answerer candidates by considering social closeness and Q&A activities. To efficiently identify answerers in the global user base, iASK builds a virtual server tree that embeds the hierarchical structure of interests, and also maps users to the tree based on user interests. To accurately locate the cooperative experts, iASK has a fine-grained reputation system to evaluate user reputation based on their cooperativeness and expertise. Experimental results from large-scale trace-driven simulation and realworld daily usages of the iASK prototype show the superior performance of iASK. It achieves high answer quality with 24% higher accuracy, short Response Latency with 53% less delay and effective cooperative incentives with 16% more answers compared to other social-based Q&A systems.
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cloud fog towards high quality of experience in cloud gaming
International Conference on Parallel Processing, 2015Co-Authors: Haiying ShenAbstract:With the increasing popularity of Massively Multiplayer Online Game (MMOG) and fast growth of mobile gaming, cloud gaming exhibits great promises over the conventional MMOG gaming model as it frees players from the requirement of hardware and game installation on their local computers. However, as the graphics rendering is offloaded to the cloud, the data transmission between the end-users and the cloud significantly increases the Response Latency and limits the user coverage, thus preventing cloud gaming to achieve high user Quality of Experience (QoE). To solve this problem, previous research suggested deploying more data centers, but it comes at a prohibitive cost. We propose a lightweight system called Cloud Fog, which incorporates "fog" consisting of super nodes that are responsible for rendering game videos and streaming them to their nearby players. Fog enables the cloud to be only responsible for the intensive game state computation and sending update information to super nodes, which significantly reduce the traffic hence the Latency and bandwidth consumption. To further enhance QoE, we propose the receiver-driven encoding rate adaptation strategy to increase the playback continuity and the deadline-driven sender buffer scheduling strategy to ensure that the segments arrive at the players within their Response Latency. Experimental results from Peer Sim and Planet Lab show the effectiveness and efficiency of Cloud Fog and our individual strategies in increasing user coverage, reducing Response Latency and bandwidth consumption.
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leveraging fog to extend cloud gaming for thin client mmog with high quality of experience
International Conference on Distributed Computing Systems, 2015Co-Authors: Haiying ShenAbstract:With the increasing popularity of Massively Multiplayer Online Game (MMOG) and fast growth of mobile gaming, cloud gaming exhibits great promises over the conventional MMOG gaming model as it frees players from the requirement of hardware and game installation on their local computers. However, as the graphics rendering is offloaded to the cloud, the data transmission between the end-users and the cloud significantly increases the Response Latency and limits the user coverage, thus preventing cloud gaming to achieve high user Quality of Experience (QoE). To solve this problem, previous research suggested deploying more data centers, but it comes at a prohibitive cost. We propose a lightweight system called Cloud Fog, which incorporates "fog" consisting of super nodes that are responsible for rendering game videos and streaming them to their nearby players. Fog enables the cloud to be only responsible for the intensive game state computation and sending update information to super nodes, which significantly reduce the traffic hence the Latency and bandwidth consumption. Experimental results from PeerSim and Planet Lab show the effectiveness and efficiency of Cloud Fog in increasing user coverage, reducing Response Latency and bandwidth consumption.
Hrvoje Augustin - One of the best experts on this subject based on the ideXlab platform.
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a computational model of the escape Response Latency in the giant fiber system of drosophila melanogaster
eNeuro, 2019Co-Authors: Asaph Zylbertal, Hrvoje Augustin, Linda PartridgeAbstract:Abstract The giant fiber system (GFS) is a multi-component neuronal pathway mediating rapid escape Response in the adult fruit-fly Drosophila melanogaster, usually in the face of a threatening visual stimulus. Two branches of the circuit promote the Response by stimulating an escape jump followed by flight initiation. A recent work demonstrated an age-associated decline in the speed of signal propagation through the circuit, measured as the stimulus-to-muscle depolarization Response Latency. The decline is likely due to the diminishing number of inter-neuronal gap junctions in the GFS of ageing flies. In this work, we presented a realistic conductance-based, computational model of the GFS that recapitulates the experimental results and identifies some of the critical anatomical and physiological components governing the circuit’s Response Latency. According to our model, anatomical properties of the GFS neurons have a stronger impact on the transmission than neuronal membrane conductance densities. The model provides testable predictions for the effect of experimental interventions on the circuit’s performance in young and ageing flies.
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a computational model of the escape Response Latency in the giant fiber system of drosophila melanogaster
bioRxiv, 2018Co-Authors: Asaph Zylbertal, Hrvoje Augustin, Linda PartridgeAbstract:ABSTRACT The Giant Fiber System (GFS) is a multi-component neuronal pathway mediating rapid escape Response in the adult fruit-fly Drosophila melanogaster, usually in the face of a threatening visual stimulus. Two branches of the circuit promote the Response by stimulating an escape jump followed by flight initiation. Our recent work demonstrated an age-associated decline in the speed of signal propagation through the circuit, measured as the stimulus-to-muscle depolarization Response Latency. The decline is likely due to the diminishing number of interneuronal gap junctions in the GFS of ageing flies. In this work, we presented a realistic conductance-based, computational model of the GFS that recapitulates our experimental results and identifies some of the critical anatomical and physiological components governing the circuit’s Response Latency. According to our model, anatomical properties of the GFS neurons have a stronger impact on the transmission than neuronal membrane conductance densities. The model provides testable predictions for the effect of experimental interventions on the circuit’s performance in young and ageing flies.
Peter Vlerick - One of the best experts on this subject based on the ideXlab platform.
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you ve got mail does workplace telepressure relate to email communication
Cognition Technology & Work, 2020Co-Authors: Ruben Cambier, Peter VlerickAbstract:Email is ubiquitous at the workplace. However, only few studies have empirically investigated the cognitive underpinning of computer-mediated business communication in general or work-related email behaviour in particular. In this paper, we further explored the relationship between workplace telepressure and two specific behavioural outcomes (i.e., email reply quantity and email Response Latency) by examining email traffic in a convenience sample of 55 employees who used their business email address daily during a typical workweek. Results of hierarchical regression analyses revealed that employees’ workplace telepressure was positively related to their email reply quantity and, surprisingly, unrelated to their email Response Latency. Altogether, our findings highlight the key role of individual cognitive characteristics, such as one’s workplace telepressure, in message-based computer-mediated communication.
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You’ve got mail: does workplace telepressure relate to email communication?
Cognition Technology & Work, 2019Co-Authors: Ruben Cambier, Peter VlerickAbstract:Email is ubiquitous at the workplace. However, only few studies have empirically investigated the cognitive underpinning of computer-mediated business communication in general or work-related email behaviour in particular. In this paper, we further explored the relationship between workplace telepressure and two specific behavioural outcomes (i.e., email reply quantity and email Response Latency) by examining email traffic in a convenience sample of 55 employees who used their business email address daily during a typical workweek. Results of hierarchical regression analyses revealed that employees’ workplace telepressure was positively related to their email reply quantity and, surprisingly, unrelated to their email Response Latency. Altogether, our findings highlight the key role of individual cognitive characteristics, such as one’s workplace telepressure, in message-based computer-mediated communication.
Stephen G Lisberger - One of the best experts on this subject based on the ideXlab platform.
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the neural basis for Response Latency in a sensory motor behavior
Cerebral Cortex, 2020Co-Authors: Joonyeol Lee, Timothy R Darlington, Stephen G LisbergerAbstract:We seek a neural circuit explanation for sensory-motor reaction times. In the smooth eye movement region of the frontal eye fields (FEFSEM), the latencies of pairs of neurons show trial-by-trial correlations that cause trial-by-trial correlations in neural and behavioral Latency. These correlations can account for two-third of the observed variation in behavioral Latency. The amplitude of preparatory activity also could contribute, but the Responses of many FEFSEM neurons fail to support predictions of the traditional "ramp-to-threshold" model. As a correlate of neural processing that determines reaction time, the local field potential in FEFSEM includes a brief wave in the 5-15-Hz frequency range that precedes pursuit initiation and whose phase is correlated with the Latency of pursuit in individual trials. We suggest that the Latency of the incoming visual motion signals combines with the state of preparatory activity to determine the Latency of the transient Response that controls eye movement. IMPACT STATEMENT The motor cortex for smooth pursuit eye movements contributes to sensory-motor reaction time through the amplitude of preparatory activity and the Latency of transient, visually driven Responses.
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the neural basis for Response Latency in a sensory motor behavior
bioRxiv, 2019Co-Authors: Joonyeol Lee, Timothy R Darlington, Stephen G LisbergerAbstract:Abstract We seek a neural circuit explanation for sensory-motor reaction times. We have found evidence that two of three possible mechanisms could contribute to reaction times in smooth pursuit eye movements. In the smooth eye movement region of the frontal eye fields (FEFSEM), an area that causally affects the initiation of smooth pursuit eye movement, neural and behavioral latencies have significant trial-by-trial correlations that can account for 40% to 100% of the variation in behavioral Latency. The amplitude of preparatory activity, which represents the motor system’s expectations for target motion, shows negative trial-by-trial correlations with behavioral Latency and could contribute to the neural computation of reaction time. In contrast, the traditional “ramp-to-threshold” model is contradicted by the Responses of many, but not all FEFSEM neurons. As evidence of neural processing that determines reaction time, the local field potential in FEFSEM includes a brief wave in the 5-15 Hz frequency range that precedes pursuit initiation and whose phase is correlated with the Latency of pursuit in individual trials. We suggest that the Latency of the incoming visual motion signals combines with the state of preparatory activity to determine the Latency of the transient Response that drives eye movement.
