Article
Multidisciplinary Sciences
Tyler Salners, Karina E. Avila, Benjamin Nicholson, Christopher R. Myers, John Beggs, Karin A. Dahmen
Summary: A statistical analysis of large neuronal avalanches in mouse and rat brain tissues reveals recurrent activity and cyclic patterns of activation, unlike smaller avalanches. By adapting a model of structural weakening in materials, it is found that dynamical weakening of neuron firing thresholds closely replicates experimental observations. This suggests that dynamical weakening plays a crucial role in the recurrent activity of large neuronal avalanches, providing insights into the causes and dynamics of conditions such as seizures.
SCIENTIFIC REPORTS
(2023)
Article
Multidisciplinary Sciences
Chiaki Itami, Naofumi Uesaka, Jui-Yen Huang, Hui-Chen Lu, Kenji Sakimura, Masanobu Kano, Fumitaka Kimura
Summary: Columnar structure is a fundamental feature of the cerebral cortex, and endocannabinoid signaling through CB1R plays a crucial role in shaping columnar axonal projection.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Geography, Physical
Jiankun Chen, Xiaolan Qiu, Chibiao Ding, Yirong Wu
Summary: This article presents a complete SAR image classifier based on Spiking Neural Network (SNN) with both unsupervised and supervised learning. The SNN achieves good classification accuracy and demonstrates better noise resistance and model parameter advantages compared to Convolutional Neural Networks (CNNs).
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Neurosciences
Jiayi Yang, Peihua Feng, Ying Wu
Summary: In this paper, the collective behavior of neuron firing is studied based on the Leaky Integrate-and-Fire model. The result shows that spike-timing-dependent plasticity (STDP) can facilitate network synchronization and increase the probability of large-scale neuron avalanches. The structure of STDP and network connection density also affect the generation of avalanche critical states. These findings contribute to our understanding of synchronization in neural networks under the effect of STDP learning rules.
COGNITIVE NEURODYNAMICS
(2023)
Article
Optics
Tao Tian, Zhengmao Wu, Xiaodong Lin, Xi Tang, Ziye Gao, Min Ni, Guangqiong Xia, Haitao Chen, Tao Deng
Summary: Based on the Fabry-Perot approach, this study implements optical spike timing dependent plasticity (STDP) with weight-dependent learning window in a vertical-cavity semiconductor optical amplifier (VCSOA) and investigates the weight-dependent STDP characteristics. The simulation results show that the bias current of VCSOA has a significant effect on the optical STDP curve. By introducing an adaptive variation of the bias current according to the present synapse weight, weight-dependent STDP can be realized, which can be used for future energy efficient optical spiking neural networks.
Article
Neurosciences
Stefan Dasbach, Tom Tetzlaff, Markus Diesmann, Johanna Senk
Summary: This study explores the effects of limited synaptic weight resolution on the dynamics of spiking neuronal networks, finding that a naive discretization may distort spike-train statistics, but preserving the mean and variance of total synaptic input currents can maintain firing statistics for certain network types. Even with a discretization of synaptic weights, substantial deviations in firing statistics may occur, emphasizing the importance of careful validation and preservation of specific network characteristics.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Biology
Owen Mackwood, Laura B. Naumann, Henning Sprekeler
Summary: Understanding the connectivity in the brain and how it is influenced by activity-dependent synaptic plasticity is a major challenge in neuroscience. Research in mice's V1 area suggests that connections between excitatory and inhibitory neurons contribute to a stimulus-specific competition among neurons, highlighting the importance of synaptic plasticity in shaping cortical computations.
Article
Computer Science, Artificial Intelligence
Yue Zhou, Nuo Xu, Bin Gao, Fuwei Zhuge, Zijian Tang, Xinchen Deng, Yi Li, Yuhui He, Xiangshui Miao
Summary: A hardware-based architecture of multilayer neural networks utilizing complementary memtransistors as electrical synapses was proposed to implement the extended remote supervised method. The system achieved successful results in solving linearly nonseparable benchmark xor problem and MNIST recognition, while reducing the chip area and power consumption compared to conventional designs.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Environmental
Yu Hu, Zhen Meng, YanZhu Hu, WenJia Tian, YanYing Yang, ShunLi Gao
Summary: This study proposes an improved model of dynamic accident spreading in networks by analyzing complex network theory and STDP mechanism. It explores the interrelationships of elements in work safety accidents and demonstrates the model's effectiveness in simulating the spreading process and edge weight evolution of actual accidents in different types of networks.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2022)
Article
Neurosciences
Ye Yuan, Yongtong Zhu, Jiaqi Wang, Ruoshi Li, Xin Xu, Tao Fang, Hong Huo, Lihong Wan, Qingdu Li, Na Liu, Shiyan Yang
Summary: This study proposes a reward-modulated self-organization recurrent network with structural plasticity (RSRN-SP) to investigate how multiple plasticities interact to shape neural networks and affect neural signal processing. Extensive experiments are conducted to demonstrate the representational ability of RSRN-SP in sequential learning tasks, and the simulations show consistent characteristics with biological observations.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Marcos Eduardo Valle, Rodolfo Anibal Lobo
Summary: The researchers extended bipolar RCNNs to deal with hypercomplex-valued data and investigated the stability of these new networks. Examples were provided to illustrate the theoretical results and computational experiments confirmed the potential application of hypercomplex-valued RCNNs as associative memories for gray-scale images.
Article
Biology
Daniel Miner, Florentin Woergoetter, Christian Tetzlaff, Michael Fauth
Summary: Information processing in the brain occurs in a layered hierarchical network architecture with abundant connections within each layer and sparse long-range connections between layers. After self-organization, stimuli conveyed by sparse inputs can be rapidly read out with only a few long-range connections.
Article
Neurosciences
Dean A. Pospisil, Wyeth Bair
Summary: Signal correlation is commonly used as a metric of tuning similarity between neurons, yet the classic estimate suffers from confounding biases. Through analytical results, corrections, and simulations, the biases in signal correlation estimation are characterized and addressed. A positive relationship between tuning curve similarity and amplitude for nearby neurons in the visual cortical motion area MT is demonstrated, with implications for dimensionality regularization in neural encoding.
JOURNAL OF NEUROSCIENCE
(2021)
Article
Engineering, Mechanical
Hao Si, Xiaojuan Sun
Summary: This paper investigates the propagation of population firing rate and pulse packets in the cortical neural network. The results show that proper feedforward connection probability and strength can promote the propagation of population firing rate, while increasing the feedforward synaptic time constant can enhance the fidelity of the propagating population firing rate. Adjusting the relative strength, recurrent probability, and feedforward synaptic connection can also promote the propagation of pulse packets, with a larger feedforward synaptic time constant having different effects on population firing rate and pulse packets propagation.
NONLINEAR DYNAMICS
(2021)
Article
Biology
Ye Yuan, Jian Liu, Peng Zhao, Hong Huo, Tao Fang
Summary: The research established a neuronal network model and found that spike signal transmission heavily depends on the connectivity between modules, with little impact from connections within modules. Importantly, the spike activity of a module can be predicted by building a resting-state functional connectivity matrix based on the activities of adjacent modules.
JOURNAL OF THEORETICAL BIOLOGY
(2021)
Article
Clinical Neurology
Daniel E. Payne, Katrina L. Dell, Phillipa J. Karoly, Vaclav Kremen, Vaclav Gerla, Levin Kuhlmann, Gregory A. Worrell, Mark J. Cook, David B. Grayden, Dean R. Freestone
Summary: Most seizure forecasting algorithms rely on electroencephalographic features, but this study explored the predictive information of sleep, weather, and temporal factors on upcoming seizures. Results show that environmental and physiological data significantly contribute to seizure forecasts, with combined features outperforming individual features in predicting seizures in some patients. Noninvasive measurements of these predictive features could potentially enhance traditional seizure detection algorithms.
Article
Clinical Neurology
Zhuying Chen, David B. Grayden, Anthony N. Burkitt, Udaya Seneviratne, Wendyl J. D'Souza, Chris French, Philippa J. Karoly, Katrina Dell, Kent Leyde, Mark J. Cook, Matias I. Maturana
Summary: The study found that high-frequency activity (HFA) and epileptiform spikes, used as bio-markers for epilepsy, exhibit high variability, especially after electrode implantation. Both HFA and spike rates show strong circadian rhythms and correlations with seizures, but these correlations are patient-specific.
Article
Anatomy & Morphology
Eleonora De Filippi, Anira Escrichs, Estela Camara, Cesar Garrido, Theo Marins, Marti Sanchez-Fibla, Matthieu Gilson, Gustavo Deco
Summary: This study investigated the neural correlates of meditation through scanning experienced meditators and control subjects using MRI. The findings revealed strengthened brain connectivity in meditators, especially in large-scale networks within the left hemisphere. Differences in functional domains were reflected to some extent in changes at the anatomical level as well.
BRAIN STRUCTURE & FUNCTION
(2022)
Article
Clinical Neurology
Rachel E. Stirling, Cindy M. Hidajat, David B. Grayden, Wendyl J. D'Souza, Jodie Naim-Feil, Katrina L. Dell, Logan D. Schneider, Ewan Nurse, Dean Freestone, Mark J. Cook, Philippa J. Karoly
Summary: Bed and wake times are more crucial than sleep duration in identifying seizure risk for people with epilepsy. Undersleeping is associated with a slight decrease in seizure risk, possibly due to nocturnal seizures. Wearables can be used to analyze sleep-seizure relationships and provide clinical recommendations.
Article
Automation & Control Systems
Jing Mu, Ying Tan, David B. Grayden, Denny Oetomo
Summary: A novel decoding algorithm based on linear Diophantine equation solvers is proposed to reduce the time complexity of decoding multifrequency SSVEPs. Simulation results show that the runtime of the linear Diophantine equation (LDE) decoder is only one fifth of the MFCCA decoder under optimized settings, making it easier to implement multifrequency SSVEP in real-time systems. The effectiveness of this new decoding algorithm is validated with nine healthy participants using dry electrode scalp electroencephalography (EEG).
ASIAN JOURNAL OF CONTROL
(2023)
Review
Clinical Neurology
Ashley Reynolds, Michaela Vranic-Peters, Alan Lai, David B. Grayden, Mark J. Cook, Andre Peterson
Summary: This scoping review explores prognostic electroencephalographic (EEG) biomarkers and models for assessing antiseizure medication (ASM) efficacy in epilepsy treatment. Qualitative and quantitative biomarkers, as well as prognostic models using EEG features, are identified, but further research is needed to determine their clinical application.
Article
Clinical Neurology
Michael Wenzel, Gilles Huberfeld, David B. Grayden, Marco de Curtis, Andrew J. Trevelyan
Summary: A critical question in understanding the onset of focal seizures is whether specific cell classes can be identified as drivers of the pathological process. This topic was debated at the recent International Conference for Technology and Analysis of Seizures (ICTALS) meeting in Bern, Switzerland in July 2022, and we provide a summary here. Advances in manipulating subpopulations of cells in relative isolation, particularly through optogenetics, have fueled this debate in recent years. The motivation behind resolving this debate is to identify new targets for therapeutic interventions based on a deeper understanding of the etiology of seizures.
Article
Neurosciences
Rajanikant Panda, Ane Lopez-Gonzalez, Matthieu Gilson, Olivia Gosseries, Aurore Thibaut, Gianluca Frasso, Benedetta Cecconi, Anira Escrichs, Gustavo Deco, Steven Laureys, Gorka Zamora-Lopez, Jitka Annen
Summary: The study of brain's dynamic activity is helping in the clinical assessment of patients with consciousness disorders. The reduced neural propagation and responsiveness to events in patients with disorders of consciousness is related to severe reduction in glucose metabolism. These findings provide insights into the mechanisms behind consciousness disorders, combining network function with measures of brain integrity and behavior.
HUMAN BRAIN MAPPING
(2023)
Article
Engineering, Biomedical
Yueyang Liu, Artemio Soto-Breceda, Philippa Karoly, David B. Grayden, Yun Zhao, Mark J. Cook, Daniel Schmidt, Levin Kuhlmann
Summary: This study presents a data-driven method to track the states and parameters of neural mass models (NMMs) from EEG recordings using deep learning techniques, specifically a long short-term memory (LSTM) neural network. Test results using simulated and real EEG data demonstrated that the method is robust to noise, more accurate than a nonlinear Kalman filter when the initial conditions of the Kalman filter are not accurate, and can reveal changes in connectivity strength parameters.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Engineering, Biomedical
Elise A. Ajay, Ella P. Trang, Alexander C. Thompson, Andrew K. Wise, David B. Grayden, James B. Fallon, Rachael T. Richardson
Summary: This study compared the temporal precision of auditory nerve responses to optogenetic, electrical, and combined stimulation in acutely and chronically deafened animals. The results showed that electrical stimulation had significantly greater temporal precision than optogenetic stimulation. Chronically deafened animals exhibited poorer temporal fidelity but improved temporal precision with optogenetic and hybrid stimulation compared to acutely deafened animals.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Engineering, Biomedical
Kevin Meng, Farhad Goodarzy, EuiYoung Kim, Ye Jin Park, June Sic Kim, Mark J. Cook, Chun Kee Chung, David B. Grayden
Summary: This study aimed to demonstrate the feasibility of synthesizing artificial speech sounds from human cortical surface recordings during silent speech production. Ten participants with intractable epilepsy were temporarily implanted with intracranial electrode arrays. A decoding model predicted audible outputs directly from patient-specific neural feature inputs, and the synthesized sounds were objectively and subjectively assessed.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Parvin Zarei Eskikand, Artemio Soto-Breceda, Mark J. Cook, Anthony N. Burkitt, David B. Grayden
Summary: This study investigates a model of neural populations across different layers of the cortex, revealing that layer 2/3 does not conform to an Inhibition Stabilized Network (ISN) model, while layers 4 and 5 do. Additionally, a gradient of inhibitory stabilization is found across different layers, dependent on the level of excitation-inhibition balance, with the strength of the gradient increasing as the model approaches bifurcation points.
Article
Computer Science, Information Systems
Syeda R. Zehra, Jing Mu, Brandon V. Syiem, Anthony N. Burkitt, David B. Grayden
Summary: This study compared the classification accuracy of Augmented Reality (AR)-based Steady-State Visually Evoked Potentials (SSVEP) with 3D and 2D stimuli, and collected participants' feedback. The results showed no significant difference in classification accuracy between 2D and 3D stimuli. However, for most participants, the classification accuracy with flickering stimuli was above average, while stimuli that changed only in size were below average. Participants had different preferences for stimulus types in terms of comfort.
Article
Physics, Fluids & Plasmas
Matthieu Gilson, Enzo Tagliazucchi, Rodrigo Cofre
Summary: The study shows that the level of consciousness can be inferred by calculating entropy production in brain activity, thus providing an advanced understanding of the link between consciousness and complexity from the perspective of statistical physics.
Article
Neuroimaging
Xenia Kobeleva, Gael Varoquaux, Alain Dagher, Mohit Adhikari, Christian Grefkes, Matthieu Gilson
Summary: This review explores the limitations of fMRI in clinical applications and discusses different models proposed as potential solutions. The authors evaluate the predictability and interpretability of these models in relation to clinical variables and highlight the need for a new generation of fMRI models that combine biophysical and decoding approaches. They argue that this synergy is fundamental for discovering new targets and using models as biomarkers in neurology and psychiatry.
NEUROIMAGE-CLINICAL
(2022)
