scholarly journals Author response: How biological attention mechanisms improve task performance in a large-scale visual system model

2018 ◽  
Author(s):  
Grace W Lindsay ◽  
Kenneth D Miller
Keyword(s):  
Visual System ◽  
Task Performance ◽  
Large Scale ◽  
System Model ◽  
Author Response

scholarly journals How biological attention mechanisms improve task performance in a large-scale visual system model

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Grace W Lindsay ◽  
Kenneth D Miller
Keyword(s):  
Visual System ◽  
Task Performance ◽  
Large Scale ◽  
System Model ◽  
Scale Model ◽  
Simple Experiment ◽  
Attentional Modulation ◽  
Large Scale Model ◽  
Feature Similarity ◽  
Visual Tasks

How does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question. We model the feature similarity gain model of attention, in which attentional modulation is applied according to neural stimulus tuning. Using a variety of visual tasks, we show that neural modulations of the kind and magnitude observed experimentally lead to performance changes of the kind and magnitude observed experimentally. We find that, at earlier layers, attention applied according to tuning does not successfully propagate through the network, and has a weaker impact on performance than attention applied according to values computed for optimally modulating higher areas. This raises the question of whether biological attention might be applied at least in part to optimize function rather than strictly according to tuning. We suggest a simple experiment to distinguish these alternatives.

scholarly journals How biological attention mechanisms improve task performance in a large-scale visual system model

2017 ◽  
Author(s):  
Grace W. Lindsay ◽  
Kenneth D. Miller
Keyword(s):  
Visual System ◽  
Task Performance ◽  
Large Scale ◽  
System Model ◽  
Scale Model ◽  
Simple Experiment ◽  
Attentional Modulation ◽  
Large Scale Model ◽  
Feature Similarity ◽  
Visual Tasks

AbstractHow does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question. We model the feature similarity gain model of attention, in which attentional modulation is applied according to neural stimulus tuning. Using a variety of visual tasks, we show that neural modulations of the kind and magnitude observed experimentally lead to performance changes of the kind and magnitude observed experimentally. We find that, at earlier layers, attention applied according to tuning does not successfully propagate through the network, and has a weaker impact on performance than attention applied according to values computed for optimally modulating higher areas. This raises the question of whether biological attention might be applied at least in part to optimize function rather than strictly according to tuning. We suggest a simple experiment to distinguish these alternatives.

Author response for "The brain of the African wild dog. IV . The visual system"

2020 ◽  
Author(s):  
Samson Chengetanai ◽  
Adhil Bhagwandin ◽  
Mads F. Bertelsen ◽  
Therese Hård ◽  
Patrick R. Hof ◽  
...  
Keyword(s):  
Visual System ◽  
Author Response ◽  
African Wild Dog ◽  
Wild Dog ◽  
The Brain

scholarly journals Earth System Model Evaluation Tool (ESMValTool) v2.0 – an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP

2020 ◽  
Vol 13 (7) ◽  
pp. 3383-3438 ◽  
Author(s):  
Veronika Eyring ◽  
Lisa Bock ◽  
Axel Lauer ◽  
Mattia Righi ◽  
Manuel Schlund ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Large Scale ◽  
Performance Metrics ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Evaluation Tool ◽  
The Earth ◽  
And Performance ◽  
Earth System Models

Abstract. The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility. It consists of (1) an easy-to-install, well-documented Python package providing the core functionalities (ESMValCore) that performs common preprocessing operations and (2) a diagnostic part that includes tailored diagnostics and performance metrics for specific scientific applications. Here we describe large-scale diagnostics of the second major release of the tool that supports the evaluation of ESMs participating in CMIP Phase 6 (CMIP6). ESMValTool v2.0 includes a large collection of diagnostics and performance metrics for atmospheric, oceanic, and terrestrial variables for the mean state, trends, and variability. ESMValTool v2.0 also successfully reproduces figures from the evaluation and projections chapters of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and incorporates updates from targeted analysis packages, such as the NCAR Climate Variability Diagnostics Package for the evaluation of modes of variability, the Thermodynamic Diagnostic Tool (TheDiaTo) to evaluate the energetics of the climate system, as well as parts of AutoAssess that contains a mix of top–down performance metrics. The tool has been fully integrated into the Earth System Grid Federation (ESGF) infrastructure at the Deutsches Klimarechenzentrum (DKRZ) to provide evaluation results from CMIP6 model simulations shortly after the output is published to the CMIP archive. A result browser has been implemented that enables advanced monitoring of the evaluation results by a broad user community at much faster timescales than what was possible in CMIP5.

scholarly journals Group Buying-based Data Transmission in Flying Ad-hoc Networks: A Coalition Game Approach

2018 ◽  
Author(s):  
Lang Ruan ◽  
Jin Chen ◽  
Qiuju Guo ◽  
Xiaobo Zhang ◽  
Yuli Zhang ◽  
...  
Keyword(s):  
Formation Mechanism ◽  
Coalition Formation ◽  
Data Transmission ◽  
Large Scale ◽  
Ad Hoc ◽  
Model Simulation ◽  
System Model ◽  
Throughput Maximization ◽  
Coalition Game ◽  
Group Buying

In scenarios such as natural disasters and military strike, it is common for unmanned aerial vehicles (UAVs) to form groups to execute reconnaissance and surveillance. To ensure the effectiveness of UAV communications, repeated resource acquisition issues and transmission mechanism design need to be addressed urgently. In this paper, we build an information interaction scenario in a Flying Ad-hoc network (FANET). The data transmission problem with the goal of throughput maximization is modeled as a coalition game framework. Then, a novel mechanism of coalition selection and data transmission based on group-buying is investigated. Since large-scale UAVs will generate high transmission overhead due to the overlapping resource requirements, we propose a resource allocation optimization method based on distributed data content. Comparing existing works, a data transmission and coalition formation mechanism is designed. Then the system model is classified into graph game and coalition formation game. Through the design of the utility function, we prove that both games have stable solutions. We also prove the convergence of the proposed approach with coalition order and Pareto order. Binary log-linear learning based coalition selection algorithm (BLL-CSA) is proposed to explore the stable coalition partition of system model. Simulation results show that the proposed data transmission and coalition formation mechanism can achieve higher data throughput than the other contrast algorithms.

scholarly journals Description and evaluation of NorESM1-F: A fast version of the Norwegian Earth System Model (NorESM)

2018 ◽  
Author(s):  
Chuncheng Guo ◽  
Mats Bentsen ◽  
Ingo Bethke ◽  
Mehmet Ilicak ◽  
Jerry Tjiputra ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Sea Ice ◽  
Large Scale ◽  
Coupled Model ◽  
Coupled System ◽  
Internal Variability ◽  
Earth System Model ◽  
Meridional Overturning Circulation ◽  
System Model ◽  
Earth System

Abstract. A new computationally efficient version of the Norwegian Earth System Model (NorESM) is presented. This new version (here termed NorESM1-F) runs about 2.5 times faster (e.g. 90 model years per day on current hardware) than the version that contributed to the fifth phase of the Coupled Model Intercomparison project (CMIP5), i.e., NorESM1-M, and is therefore particularly suitable for multi-millennial paleoclimate and carbon cycle simulations or large ensemble simulations. The speedup is primarily a result of using a prescribed atmosphere aerosol chemistry and a tripolar ocean-sea ice horizontal grid configuration that allows an increase of the ocean-sea ice component time steps. Ocean biogeochemistry can be activated for fully coupled and semi-coupled carbon cycle applications. This paper describes the model and evaluates its performance using observations and NorESM1-M as benchmarks. The evaluation emphasises model stability, important large-scale features in the ocean and sea ice components, internal variability in the coupled system, and climate sensitivity. Simulation results from NorESM1-F in general agree well with observational estimates, and show evident improvements over NorESM1-M, for example, in the strength of the meridional overturning circulation and sea ice simulation, both important metrics in simulating past and future climates. Whereas NorESM1-M showed a slight global cool bias in the upper oceans, NorESM1-F exhibits a global warm bias. In general, however, NorESM1-F has more similarities than dissimilarities compared to NorESM1-M, and some biases and deficiencies known in NorESM1-M remain.

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