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Frontiers | Eeg-Derived Estimators of Present and Future Cognitive Performance
Previous EEG-based fatigue-related research primarily focused on the association between concurrent cognitive performance and time-locked physiology. The goal of this study was to investigate the capa... [Read More]
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Understanding the role of individual units in a deep neural network
Deep neural networks excel at finding hierarchical representations that solve complex tasks over large datasets. How can we humans understand these learned representations? In this work, we presen... [Read More]
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Parsing events from raw data — MNE 1.1.dev0 documentation
Parsing events from raw dataThis tutorial describes how to read experimental events from raw recordings, and how to convert between the two different representations of events within MNE-Python (Eve... [Read More]
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Meta-control: From psychology to computational neuroscience
Research in the past decades shed light on the different mechanisms that underlie our capacity for cognitive control. However, the meta-level processes that regulate cognitive control itself rem... [Read More]
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eeglib: A Python module for EEG feature extraction
Electroencephalography (EEG) signals analysis is non-trivial, thus tools for helping in this task are crucial. One typical step in many studies is feature extraction, however, there are not many tools... [Read More]
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Analyzing EEG maturation in preterm infants: The value of a quantitative approach - IOS Press
Despite increase in survival of very low birth weight infants, the number of infants who experience neuromotor or neurocognitive problems later in life is still high. Therefore, accurate documentation... [Read More]
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Intelligent Scanning Using Deep Learning for MRI — The TensorFlow Blog
Here we describe our experience using TensorFlow to train a neural network to identify specific anatomy during a brain magnetic resonance imaging (MRI) exam to help improve speed and consistency... [Read More]
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Nipype Beginner's Guide — All you need to know to become an expert in Nipype
How To Normalize Your Data Before you can start with a second level analysis you are facing the problem that all your output from the first level analysis are still in their subject specific subj... [Read More]
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Analysis of Functional Magnetic Resonance Imaging in Python | IEEE Journals & Magazine | IEEE Xplore
The authors describe a package for analyzing magnetic resonance imaging (MRI) and functional MRI (fMRI) data, which is part of the Neuroimaging in Python (NIPY) [Read More]
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What the success of brain imaging implies about the neural code
The success of fMRI places constraints on the nature of the neural code. The fact that researchers can infer similarities between neural representations, despite fMRI’s limitations, implies that c... [Read More]
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Large-scale cognitive model design using the Nengo neural simulator
The Neural Engineering Framework (NEF) and Semantic Pointer Architecture (SPA) provide the theoretical underpinnings of the neural simulation environment Nengo. Nengo has recently been used to build S... [Read More]
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Frontiers | Python scripting in the Nengo simulator | Frontiers in Neuroinformatics
Nengo is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological s... [Read More]
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Frontiers | Nengo: a Python tool for building large-scale functional brain models | Frontiers in Neuroinformatics
Neuroscience currently lacks a comprehensive theory of how cognitive processes can be implemented in a biological substrate. The Neural Engineering Framework (NEF) proposes one such theory, but has no... [Read More]
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Nengo brain maker
Nengo is a graphical and scripting based Python package for simulating large-scale neural networks. [Read More]