Article
Neurosciences
Francoise Lecaignard, Olivier Bertrand, Anne Caclin, Jeremie Mattout
Summary: Predictive coding theory has a profound influence on brain functions and poses a fundamental paradox. Brain responses reflect precision-weighted prediction error, and it is necessary to differentiate the contributions of precision and prediction error in electrophysiology. By combining EEG and MEG, our study reveals adaptive learning of surprise in the brain and precision encoding through specific connections, which has important implications for applications in psychiatry.
JOURNAL OF NEUROSCIENCE
(2022)
Article
Neurosciences
J. Metsomaa, P. Belardinelli, M. Ermolova, U. Ziemann, C. Zrenner
Summary: The study proposes a method for personalized EEG classification to accurately identify brain excitability states. Results show that excitability fluctuates predominantly in the mu-oscillation range, but there is variability in relevant power spectra, phases, and cortical regions among different subjects.
Article
Chemistry, Analytical
Jing Zhang, Dong Liu, Weihai Chen, Zhongcai Pei, Jianhua Wang
Summary: This study introduces a deep convolutional neural network for EEG-based motor decoding, achieving high classification accuracy and outperforming other models. Feature visualization was used to evaluate discriminative channels for decoding and demonstrated the feasibility of the proposed architecture. Deep learning can improve decoding performance and expand understanding of brain mapping.
Article
Biochemical Research Methods
Sam Gijsen, Miro Grundei, Robert T. Lange, Dirk Ostwald, Felix Blankenburg
Summary: Tracking statistical regularities of the environment using Bayesian principles is crucial for shaping human behavior and perception. This study investigates the cortical dynamics of somatosensory learning and reveals neural signatures of surprise in the brain during perception. Early surprise signals indicate the need for model updates, providing insights into how somatosensory processing contributes to the learning of environmental statistics.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Biology
Davide Borra, Valeria Mondini, Elisa Magosso, Gernot R. Mueller-Putz
Summary: This study aims to decode hand kinematics (position and velocity) in Brain-Computer Interfaces (BCIs) using an interpretable convolutional neural network (ICNN). The ICNN outperformed other decoders, balancing performance, size, and training time. It also allowed interpretation of the most relevant spectral and spatial features.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Neurosciences
Nora Hollenstein, Cedric Renggli, Benjamin Glaus, Maria Barrett, Marius Troendle, Nicolas Langer, Ce Zhang
Summary: This study reveals the potential of using EEG brain activity data to enhance natural language processing tasks, with filtering signal into frequency bands found to be beneficial for tasks like sentiment classification. However, further research is needed for more complex tasks such as relation detection.
FRONTIERS IN HUMAN NEUROSCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Jingyuan Sun, Shaonan Wang, Jiajun Zhang, Chengqing Zong
Summary: The article discusses the use of distributed semantic models (DSMs) to explain the cortical representation of language in the brain and explores the relationship between DSMs and neural activation patterns. The authors found that differences in the performance of different DSMs in modeling brain activities can be partially explained by the granularity of their semantic representations.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Zhongyu Huang, Changde Du, Yingheng Wang, Kaicheng Fu, Huiguang He
Summary: Brain signal-based emotion recognition has gained attention for its potential in human-computer interaction. Researchers have attempted to decode human emotions from brain imaging data using emotion and brain representations. However, the relationship between emotions and brain regions is not explicitly incorporated into the representation learning process, leading to insufficient informative representations for specific tasks such as emotion decoding. This work proposes a graph-enhanced emotion neural decoding approach that integrates the relationships between emotions and brain regions into the process, demonstrating its effectiveness and superiority through comprehensive experiments on visually evoked emotion datasets.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Engineering, Biomedical
Pablo Ortega, A. Aldo Faisal
Summary: The study shows that by using EEG and fNIRS signals along with deep learning methods, hand-specific forces can be effectively decoded, with the cnnatt model performing better in signal fusion. Detection of force generation is crucial for performance improvement, and at the cortical level, forces from each hand are encoded differently.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Neurosciences
Antonino Visalli, Mariagrazia Capizzi, Ettore Ambrosini, Bruno Kopp, Antonino Vallesi
Summary: The brain predicts the timing of events in a Bayesian manner, with P3 modulations reflecting the updating of temporal beliefs. Neural activity and event-related potentials (ERPs) can differentiate responses to surprising events from belief updating.
Article
Engineering, Biomedical
Christina Yi Jin, Jelmer P. Borst, Marieke K. van Vugt
Summary: In this study, EEG classifiers were trained using convolutional neural networks to track mind-wandering. The results showed limited generalizability across participants and tasks. However, the meta-learner trained with stERPs performed the best among the state-of-the-art neural networks, indicating the importance of each EEG channel.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Hanwen Wang, Yu Qi, Lin Yao, Yueming Wang, Dario Farina, Gang Pan
Summary: Brain-computer interfaces (BCIs) provide a direct connection between the brain and external devices, showing great potential for assistive and rehabilitation technologies. In this study, a human-machine joint learning framework is proposed to accelerate the learning process in BCIs by guiding users to generate brain signals towards an optimal distribution. Experimental results demonstrated that the proposed joint learning process outperformed traditional approaches in terms of learning efficiency and effectiveness.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Biochemical Research Methods
Benjamin Skerritt-Davis, Mounya Elhilali
Summary: This study presents a computational model for predictive coding of complex auditory scenes, tracking sound features using Bayesian inference. The model is flexible and able to capture a variety of statistical structures.
JOURNAL OF NEUROSCIENCE METHODS
(2021)
Article
Mathematics, Interdisciplinary Applications
Alireza Modirshanechi, Johanni Brea, Wulfram Gerstner
Summary: Surprising events have a measurable impact on brain activity and human behavior, affecting learning, memory, and decision-making. The definition of surprise lacks consensus. In this study, the authors identify and classify 18 mathematical definitions of surprise, proposing a taxonomy based on the measured quantity. This research lays the foundation for studying the functional roles and physiological signatures of surprise in the brain.
JOURNAL OF MATHEMATICAL PSYCHOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Changde Du, Kaicheng Fu, Jinpeng Li, Huiguang He
Summary: This paper presents a neural decoding method called BraVL that uses multimodal learning of brain-visual-linguistic features to decode human visual neural representations. The method overcomes the limitations of existing methods in generalizing to novel categories and improves data efficiency by leveraging multimodal deep generative models. The experiments show that decoding novel visual categories is practically possible with good accuracy using the combination of visual and linguistic features.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Vasiliki Liakoni, Alireza Modirshanechi, Wulfram Gerstner, Johanni Brea
Summary: Surprise-based learning allows agents to adapt rapidly in nonstationary environments, with the Bayes Factor Surprise modulating the balance between forgetting old observations and integrating new ones. Novel surprise-based algorithms have constant scaling in observation sequence length and simple update dynamics for distributions in the exponential family, showing better parameter estimation compared to alternative approaches. Theoretical insight and proposed online algorithms in the study may be useful for analyzing behavior in animals and humans, as well as reinforcement learning in changing environments.
NEURAL COMPUTATION
(2021)
Article
Neurosciences
Vahid Esmaeili, Keita Tamura, Samuel P. Muscinelli, Alireza Modirshanechi, Marta Boscaglia, Ashley B. Lee, Anastasiia Oryshchuk, Georgios Foustoukos, Yanqi Liu, Sylvain Crochet, Wulfram Gerstner, Carl C. H. Petersen
Summary: This study used a combination of calcium imaging, electrophysiology, and optogenetics to track the cortical activity sequence in mice from whisker stimulation to delayed licking. It found that enhanced activity in the secondary whisker motor cortex and transient reduction in orofacial sensorimotor cortex played important roles in completing the task, while sustained activity in the frontal cortex was crucial for licking during the response period.
Article
Biochemical Research Methods
He A. Xu, Alireza Modirshanechi, Marco P. Lehmann, Wulfram Gerstner, Michael H. Herzog
Summary: This research demonstrates the importance of incorporating surprise and novelty into reinforcement learning theories to explain human behavior. It is found that human decisions are mainly influenced by model-free action choices, with the world-model playing a role in detecting surprising events.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Mathematics, Interdisciplinary Applications
Alireza Modirshanechi, Johanni Brea, Wulfram Gerstner
Summary: Surprising events have a measurable impact on brain activity and human behavior, affecting learning, memory, and decision-making. The definition of surprise lacks consensus. In this study, the authors identify and classify 18 mathematical definitions of surprise, proposing a taxonomy based on the measured quantity. This research lays the foundation for studying the functional roles and physiological signatures of surprise in the brain.
JOURNAL OF MATHEMATICAL PSYCHOLOGY
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Parnian Kassraie, Alireza Modirshanechi, Hamid K. Aghajan
PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON DATA SCIENCE, TECHNOLOGY AND APPLICATIONS (DATA)
(2017)
Article
Neurosciences
Jose Sanchez-Bornot, Roberto C. Sotero, J. A. Scott Kelso, Ozguer Simsek, Damien Coyle
Summary: This study proposes a multi-penalized state-space model for analyzing unobserved dynamics, using a data-driven regularization method. Novel algorithms are developed to solve the model, and a cross-validation method is introduced to evaluate regularization parameters. The effectiveness of this method is validated through simulations and real data analysis, enabling a more accurate exploration of cognitive brain functions.