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Explanations for Neural Networks by Neural Networks [PDF]
Applied Sciences, 2022 Understanding the function learned by a neural network is crucial in many domains, e.g., to detect a model’s adaption to concept drift in online learning. Existing global surrogate model approaches generate explanations by maximizing the fidelity between the neural network and a surrogate model on a sample-basis, which can be very time-consuming ...
Sascha Marton +2 more
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Neural Networks With Motivation [PDF]
Frontiers in Systems Neuroscience, 2021 Animals rely on internal motivational states to make decisions. The role of motivational salience in decision making is in early stages of mathematical understanding. Here, we propose a reinforcement learning framework that relies on neural networks to learn optimal ongoing behavior for dynamically changing motivation values. First, we show that neural
Sergey A. Shuvaev +5 more
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Persistence of shocks in CDS returns on Croatian bonds: Quantile autoregression approach [PDF]
Zbornik radova Ekonomskog fakulteta u Rijeci : časopis za ekonomsku teoriju i praksu, 2019 The paper aims to examine persistence of shocks in returns on CDS for 5Y Croatian bonds. Based on sample of daily data from January 6, 2004 up until December 13, 2019 the paper evaluated research hypothesis that assumed persistence ...
Mile Bošnjak, Ivan Novak, Maja Bašić
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Bioelectronic Medicine, 2021 Background Extracellular recording represents a crucial electrophysiological technique in neuroscience for studying the activity of single neurons and neuronal populations.
Joshua J. Strohl +4 more
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Neural network approximation [PDF]
Acta Numerica, 2021 Neural networks (NNs) are the method of choice for building learning algorithms. They are now being investigated for other numerical tasks such as solving high-dimensional partial differential equations. Their popularity stems from their empirical success on several challenging learning problems (computer chess/Go, autonomous navigation, face ...
Ronald A. DeVore +2 more
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Scientific Reports, 2021 Semantic categorization is a fundamental ability in language as well as in interaction with the environment. However, it is unclear what cognitive and neural basis generates this flexible and context dependent categorization of semantic information.
Atsushi Matsumoto +3 more
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Communications Biology, 2022 Modelling based on fMRI data obtained during more than 100 different cognitive tasks reveals that representation and decoding are preserved across the cortex, cerebellum, and ...
Tomoya Nakai, Shinji Nishimoto
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Scientific Reports, 2022 While information enriches daily life, it can also sometimes have a negative impact, depending on an individual’s mental state. We recorded electroencephalogram (EEG) signals from depressed and non-depressed individuals classified based on the Beck ...
Kohei Fuseda +5 more
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Operational neural networks [PDF]
Neural Computing and Applications, 2020 AbstractFeed-forward, fully connected artificial neural networks or the so-called multi-layer perceptrons are well-known universal approximators. However, their learning performance varies significantly depending on the function or the solution space that they attempt to approximate. This is mainly because of their homogenous configuration based solely
Serkan Kiranyaz +3 more
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Frontiers in Neuroanatomy, 2016 As an important component of ascending activating systems, brainstem cholinergic neurons in the pedunculopontine tegmental nucleus (PPTg) are involved in the regulation of motor control (locomotion, posture and gaze) and cognitive processes (attention ...
Fumika Mori +7 more
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