Article
Physics, Applied
K. Segall, C. Purmessur, A. D'Addario, D. Schult
Summary: The recent success of AI systems has led to an increase in computational resources which threatens the future development of AI systems. Unsupervised learning presents a possible solution, and a synaptic circuit made from superconducting electronics capable of STDP has been designed. The circuit demonstrates the hallmark behaviors of STDP through numerical simulation, and when combined with existing superconducting neuromorphic components, it could contribute to the creation of a fast, powerful, and energy-efficient Spiking Neural Network.
APPLIED PHYSICS LETTERS
(2023)
Article
Multidisciplinary Sciences
Charles W. Dickey, Anna Sargsyan, Joseph R. Madsen, Emad N. Eskandar, Sydney S. Cash, Eric Halgren
Summary: Research shows that there is a strong tonic and phase-locked increase in firing and co-firing during spindles, especially those occurring with down-to-upstate transitions, within 25 ms. Co-firing, spindle co-occurrence, and spindle coherence are greatest within approximately 2 mm.
NATURE COMMUNICATIONS
(2021)
Article
Mathematics, Applied
Ewandson L. Lameu, Fernando S. Borges, Kelly C. larosz, Paulo R. Protachevicz, Chris G. Antonopoulos, Elbert E. N. Macau, Antonio M. Batista
Summary: This study investigates the combined effect of short-term and spike-timing-dependent plasticity on synaptic strength, showing that plasticity can facilitate the formation of modular neural networks with complex topologies resembling those with preferential attachment properties. Specifically, the use of an STDP rule altering synaptic coupling intensity based on spike time intervals leads to the emergence of directed connections from high to low frequency spiking neurons. The results suggest that the combined effect of STP and STDP with long recovery times facilitates connections among neurons with similar spike frequencies, resulting in preferential attachment.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2021)
Article
Neurosciences
Mojtaba Madadi Asl, Saeideh Ramezani Akbarabadi
Summary: Synchronization and synaptic plasticity play important roles in learning and memory, but they are affected by transmission delays and spike-timing-dependent plasticity (STDP), leading to changes in the activity and connectivity patterns of neurons.
COGNITIVE NEURODYNAMICS
(2023)
Article
Computer Science, Information Systems
John Moon, Yuting Wu, Xiaojian Zhu, Wei D. Lu
Summary: Understanding connectivity patterns in neural circuitry is essential for grasping the operating mechanism of the brain. The use of spike-timing dependent plasticity (STDP) algorithm for reconstructing neural connectivity patterns in a biological neural network offers higher accuracy and efficiency, leading to highly reliable inference performance.
SCIENCE CHINA-INFORMATION SCIENCES
(2021)
Article
Physiology
Mojtaba Madadi Asl, Atefeh Asadi, Jamil Enayati, Alireza Valizadeh
Summary: Parkinson's disease is a neurodegenerative brain disorder associated with dysfunction of the basal ganglia circuitry, leading to pathological strengthening of pallido-subthalamic synapses and abnormal synchronized neuronal activity. Inhibitory spike-timing-dependent plasticity at these synapses may contribute to the pathological changes observed in PD, shaping bistable activity-connectivity states in the GPe-STN network.
FRONTIERS IN PHYSIOLOGY
(2022)
Article
Mathematical & Computational Biology
Qiu-Sheng Huang, Hui Wei
Summary: The study proposes a working memory model based on spike-timing-dependent plasticity, which uses temporal patterns of action potentials to represent information and can flexibly encode new memory representation. The model can operate in both persistent and silent states, compatible with seemingly conflicting neural mechanisms, challenging the traditional theory of persistent activity. Simulation experiments support the plausibility of the model in biology.
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2021)
Article
Neurosciences
Bettina C. Schwab, Peter Koenig, Andreas K. Engel
Summary: This computational study demonstrated that spike-timing-dependent plasticity can explain the connectivity aftereffects of dual-site tACS, but not all combinations of tACS frequency and application sites are expected to effectively modulate connectivity via STDP. It is suggested to use appropriate computational models and/or EEG analysis for planning and interpreting dual-site tACS studies relying on aftereffects.
Article
Computer Science, Artificial Intelligence
Long Chen, Xuhang Li, Yaqin Zhu, Haitao Wang, Jiayong Li, Yu Liu, Zijian Wang
Summary: This research proposes a new neuron model with dense intralayer connections and efficient forward and backward processes in backpropagation (BP) training. It also introduces a probabilistic spike-timing dependent plasticity (STDP) method and a hybrid training method combining BP and STDP. The proposed model, with these improvements, outperforms other SNN-BP models in various tasks.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Neurosciences
Irene Martinez-Gallego, Mikel Perez-Rodriguez, Heriberto Coatl-Cuaya, Gonzalo Flores, Antonio Rodriguez-Moreno
Summary: In the somatosensory cortex of mice, the t-LTD at L4 to L2/3 synapses disappears after the fourth week of development, while t-LTP is induced in the more mature stage of P38 to 60 days. This presynaptic t-LTP is primarily regulated by astrocytes and requires the activation of NMDARs, mGlu1Rs, and the entry of Ca2+.
JOURNAL OF NEUROSCIENCE
(2022)
Article
Physics, Multidisciplinary
Tianyu Li, Yong Wu, Lijian Yang, Xuan Zhan, Ya Jia
Summary: This study investigates the effects of Spike-timing-dependent plasticity (STDP) on chaotic resonance (CR) in a small-world network. The research shows that moderately strong chaotic current inputs enhance CR due to an increase in average coupling strength after STDP learning. The study also reveals that networks with weaker coupling strengths exhibit better plasticity in response to weak signals. Furthermore, adjusting STDP windows can promote or suppress CR, and the difference in area between long-time depression and long-time potentiation windows is closely related to average coupling strength after STDP learning.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Chunming Jiang, Le Yang, Yilei Zhang
Summary: This paper demonstrates the feasibility of applying SNN to classify tactile signals and shows that a trained SNN can accurately categorize tactile signals into different levels of roughness on metal surfaces.
IEEE SENSORS JOURNAL
(2022)
Article
Chemistry, Multidisciplinary
Xueyan Zhong, Hongbing Pan
Summary: This paper proposes an adaptive threshold Spike Neural Network model to address the practical constraints of high resource occupancy and complex calculations in existing models. Experimental results on the MNIST dataset show that the proposed model reduces the complexity of the weight update algorithm and maintains an accuracy rate of around 96%.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Daniel Gerlinghoff, Tao Luo, Rick Siow Mong Goh, Weng-Fai Wong
Summary: Spiking neural networks (SNNs) are an alternative to conventional artificial neural networks that prioritize resource efficiency and computational complexity. However, training SNNs has been a challenge due to the non-differentiable nature of neuron models and the limitations of traditional gradient-based backpropagation algorithms. This study presents desire backpropagation, a method that incorporates desired spike activity into local weight updates, leading to efficient capture of neuron dynamics and high classification accuracy.
Article
Biology
Ohad Stoler, Alexandra Stavsky, Yana Khrapunsky, Israel Melamed, Grace Stutzmann, Daniel Gitler, Israel Sekler, Ilya Fleidervish
Summary: Mitochondrial activity plays a crucial role in synaptic plasticity, and the firing pattern of pre- and postsynaptic neurons affects mitochondrial function. Our study revealed that postsynaptic spike firing induces rapid mitochondrial Ca2+ responses, with larger responses observed in cell bodies and apical dendrites. Additionally, the coincidence of an EPSP with a backpropagating spike generated prominent mitochondrial Ca2+ hotspots. These findings suggest that mitochondria decode spike firing patterns into Ca2+ signals, which in turn influence metabolic output and may lead to long-term changes in synaptic efficacy.
Article
Physics, Multidisciplinary
David Dahmen, Matthieu Gilson, Moritz Helias
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2020)
Article
Multidisciplinary Sciences
Toshitake Asabuki, Tomoki Fukai
NATURE COMMUNICATIONS
(2020)
Article
Neurosciences
Nicolas Gravel, Remco J. Renken, Ben M. Harvey, Gustavo Deco, Frans W. Cornelissen, Matthieu Gilson
Article
Biochemical Research Methods
Matthieu Gilson, David Dahmen, Ruben Moreno-Bote, Andrea Insabato, Moritz Helias
PLOS COMPUTATIONAL BIOLOGY
(2020)
Article
Neurosciences
Tomoki Kurikawa, Kenji Mizuseki, Tomoki Fukai
Summary: Researchers constructed a neural network model to explore how the brain processes information during spatial working memory tasks, finding that cholinergic modulation regulates information flow in different memory task stages, and theta oscillation coordinates interactions between various brain regions. The model predicts that the MEC plays a significant role in decoding and encoding spatial memory.
Article
Biochemical Research Methods
Tatsuya Haga, Tomoki Fukai
Summary: The paper introduces an extended attractor network model for graph-based hierarchical computation, called Laplacian associative memory, which autonomously performs hierarchical segmentation by identifying groups of tightly linked memories. The model generates multiscale representations for communities of associative links between memory items.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Multidisciplinary Sciences
Makio Torigoe, Tanvir Islam, Hisaya Kakinuma, Chi Chung Alan Fung, Takuya Isomura, Hideaki Shimazaki, Tazu Aoki, Tomoki Fukai, Hitoshi Okamoto
Summary: The study shows that adult zebrafish can assign rules to colors of the surrounding walls and generate a neural ensemble that becomes activated when there is a discrepancy between real and predicted scenery. The fish with this ensemble are able to escape more efficiently, suggesting that zebrafish can use goal-directed behavior guided by minimizing prediction errors.
NATURE COMMUNICATIONS
(2021)
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
Mathematics, Applied
Hongjie Bi, Tomoki Fukai
Summary: Amplitude-mediated multicluster chimera states are found in nonlocally coupled Stuart-Landau oscillators, and the prerequisites for different types of chimera states are clarified through analytical and numerical studies of phase transitions.
Article
Biochemical Research Methods
Toshitake Asabuki, Prajakta J. Kokate, Tomoki Fukai
Summary: The study demonstrates the crucial role of recurrent gating of dendro-somatic signal transfers in cortical learning of context-dependent segmentation tasks, using a recurrent gated network of neurons with dendrites. The model efficiently solves difficult segmentation tasks and provides a powerful tool for analyzing neural activity patterns.
PLOS COMPUTATIONAL BIOLOGY
(2022)
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
Multidisciplinary Sciences
Chi Chung Alan Fung, Tomoki Fukai
Summary: This study investigates the mechanism of synaptic competition in separating similar memories and finds that synaptic competition and neuronal maturation play distinct roles in this process. Furthermore, it shows that a competition-based learning rule can outperform the backpropagation algorithm in neural network training for tasks with limited data.
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)
Article
Clinical Neurology
Mohit H. Adhikari, Joseph Griffis, Joshua S. Siegel, Michel Thiebaut de Schotten, Gustavo Deco, Andrea Instabato, Matthieu Gilson, Maurizio Corbetta
Summary: Recent studies have identified key biomarkers of acute brain dysfunction in stroke patients using resting-state functional MRI. These biomarkers include changes in inter-hemispheric functional connectivity and abnormal increases in ipsi-lesional functional connectivity. Utilizing whole-brain computational modeling, researchers found that effective connectivity was a better predictor than functional connectivity for distinguishing healthy individuals from stroke patients, as well as for predicting long-term outcomes post-stroke.
BRAIN COMMUNICATIONS
(2021)