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| http://dx.doi.org/10.1016/j.hrcr.2022.03.006 | DOI Listing |
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Current neural network models of primate vision focus on replicating overall levels of behavioral accuracy, often neglecting perceptual decisions' rich, dynamic nature. Here, we introduce a novel computational framework to model the dynamics of human behavioral choices by learning to align the temporal dynamics of a recurrent neural network (RNN) to human reaction times (RTs). We describe an approximation that allows us to constrain the number of time steps an RNN takes to solve a task with human RTs.
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Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
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College of Computer and Information Engineering, Nanjing Tech University, Nanjing, Jiangsu, China.
Intelligent transportation systems heavily rely on forecasting urban traffic flow, and a variety of approaches have been developed for this purpose. However, most current methods focus on exploring spatial and temporal dependencies in historical traffic data, while often overlooking the inherent spectral characteristics hidden in traffic time series. In this paper, we introduce an approach to analyzing traffic flow in the frequency domain.
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