Titlebook: Neural Information Processing; 28th International C Teddy Mantoro,Minho Lee,Achmad Nizar Hidayanto Conference proceedings 2021 Springer Nat

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Robot Arm Control Using Reward-Modulated Hebbian Learningerform delicate tasks that only humans can do. On the other hand, it is challenging to control. Therefore, in this research, we focused on reservoir computing with a biologically inspired learning algorithm. Reward-modulated Hebbian learning, one of the reservoir computing frameworks, is based on He

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A Region Descriptive Pre-training Approach with Self-attention Towards Visual Question Answering-answer) and image inputs in a forced manner. In this paper, we introduce a region descriptive pre-training approach with self-attention towards VQA. The model is a new learning method that uses the image region descriptions combined with object labels to create a proper alignment between the text(q

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Prediction of Inefficient BCI Users Based on Cognitive Skills and Personality Traitslls and personality traits correlate with MI-BCI real-time performance. Other studies have examined sensorimotor rhythm changes (known as . suppression) as a valuable indicator of successful execution of the motor imagery task. This research aims to combine these insights by investigating whether co

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Analysis of Topological Variability in Mouse Neuronal Populations Based on Fluorescence Microscopy Ias occupied by ensembles of cell groups in mouse brain tissue. Recognition of mouse neuronal populations was performed on the basis of visual properties of fluorescence-activated cells. In our study 60 fluorescence microscopy datasets obtained from 23 mice ex vivo were analyzed. Based on data from l

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Explaining Neural Network Results by Sensitivity Analysis for Deception Detectionervers, we train a three-layer neural network, a long short-term memory (LSTM) and a multi-tasking learning neural network (MTL-NN). We demonstrate that examined models are able to identify deception with an accuracy up to 62%, surpassing the average accuracy of human deception detection. The superi

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Stress Recognition with EEG Signals Using Explainable Neural Networks and a Genetic Algorithm for Fedeterioration, it is important for researchers to understand and improve its detection. This paper uses neural network techniques to classify whether an individual is stressed, based on signals from an electroencephalogram (EEG), a popular physiological sensor. We also overcome two prominent limitat