Article
Neurosciences
Anna Schroeder, M. Belen Pardi, Joram Keijser, Tamas Dalmay, Ayelen I. Groisman, Erin M. Schuman, Henning Sprekeler, Johannes J. Letzkus
Summary: This study combines various techniques such as synaptic two-photon calcium imaging, circuit mapping, cortex-dependent learning, and chemogenetics in mice to investigate the role of inhibitory top-down projections. The results show that GABAergic afferents from the subthalamic zona incerta play a major role in transmitting top-down input to the neocortex. This transmission undergoes plasticity during learning and contributes to information transfer and behavioral memory.
Article
Engineering, Mechanical
Marco Pizzoli, Francesco Saltari, Franco Mastroddi, Jon Martinez-Carrascal, Leo M. Gonzalez-Gutierrez
Summary: The aim of this work is to provide a reduced-order model using a feed forward neural network to describe the dissipative behavior of nonlinear vertical sloshing involving Rayleigh-Taylor instability. A 1-degree-of-freedom system was used as a representative of fluid-structure interaction problem, with sloshing replaced by a boxed-in bouncing ball model with parameters tuned based on experiments. The neural network model, trained on a large dataset of bouncing ball simulations, has shown promising performances for integration in complex structural systems.
NONLINEAR DYNAMICS
(2022)
Article
Biology
Yue Kris Wu, Friedemann Zenke
Summary: Neural circuits can achieve rapid information processing through nonlinear transient amplification, which involves two phases - selective amplification of inputs exceeding a critical threshold by positive feedback excitation, and stabilization of runaway dynamics into an inhibitory state by short-term plasticity. NTA offers a parsimonious explanation for how excitatory-inhibitory co-tuning and short-term plasticity collaborate in recurrent networks to achieve transient amplification.
Article
Computer Science, Interdisciplinary Applications
Mengjie Zhao, Yitao Yan, Jie Bao, Wei Wang
Summary: This paper presents a new big data-based approach to control the feature dynamics of continuous nonlinear chemical/industrial processes, using the behavioural systems theory and deep learning tools. The feature dynamics are extracted using an Autoencoder, and the optimization of feature variables is achieved by controlling the latent variable dynamics.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Mathematics, Interdisciplinary Applications
Amir Sabouri, Mahdieh Ghasemi, Mahtab Mehrabbeik
Summary: This paper investigates the synchronization of up-down state oscillations in the brain neocortex using a non-uniform neuronal network model. Dynamical analysis reveals that the studied model exhibits chaotic behavior within a wide range of coupling strengths. Additionally, rare phenomenon of neural dynamics like instantaneous periodic windows were observed within the chaotic regions when excitatory-excitatory neuronal coupling strength was used as the control parameter.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Physics, Multidisciplinary
Ryan van Mastrigt, Marjolein Dijkstra, Martin van Hecke, Corentin Coulais
Summary: This paper demonstrates that convolutional neural networks can learn to recognize the boundaries of combinatorial mechanical metamaterials, even with sparse training sets, and successfully generalize, opening up new possibilities for complex material design.
PHYSICAL REVIEW LETTERS
(2022)
Article
Multidisciplinary Sciences
Ahmadreza Azizi, Michel Pleimling
Summary: The study investigates the performance of machine learning algorithms trained with configurations from Monte Carlo simulations of the two-dimensional Ising model. Convolutional neural networks are effective in locating the phase transition point accurately and displaying finite-size scaling with Ising critical exponent. However, restricted Boltzmann machines generate configurations with magnetizations and energies not allowed in the original system, leading to incorrect weight distributions and spatial correlations. Shortcomings are also observed when training RBM with configurations from the non-conserved Ising model.
SCIENTIFIC REPORTS
(2021)
Article
Physics, Multidisciplinary
Junyu Liu, Khadijeh Najafi, Kunal Sharma, Francesco Tacchino, Liang Jiang, Antonio Mezzacapo
Summary: Parametrized quantum circuits, or quantum neural networks, have the potential to outperform classical counterparts in addressing learning problems. However, the convergence rate of training quantum neural networks is not fully understood. In this study, we analyze the dynamics of gradient descent for a class of variational quantum machine learning models and derive a simple analytic formula to describe their loss function behavior.
PHYSICAL REVIEW LETTERS
(2023)
Article
Engineering, Multidisciplinary
Saurabh Balkrishna Tandale, Marcus Stoffel
Summary: The present study aims to introduce an AI algorithm suitable for neuromorphic computing to solve Boundary Value Problems in Engineering Mechanics. By using Spiking Neural Networks (SNNs), the study proposes a surrogate model for mechanical tasks that is more energy-efficient than traditional neural networks. The researchers also propose a hybrid model that combines spiking recurrent cells, the spiking variant of the Legendre Memory Unit (LMU), and classical dense transformations to compute the nonlinear response of shock wave-loaded plate elements.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Review
Neurosciences
Benjamin Schuman, Shlomo Dellal, Alvar Pronneke, Robert Machold, Bernardo Rudy
Summary: The brain relies on the ability of the neocortex to integrate sensory information with internal signals in order to make quick adjustments to behavior, with pyramidal cells in layer 1 playing a crucial role. Layer 1 serves as the primary input layer for top-down information, contributing to functions such as sensory perception, cross-modal integration, controlling states of consciousness, attention, and learning.
ANNUAL REVIEW OF NEUROSCIENCE, VOL 44, 2021
(2021)
Article
Computer Science, Interdisciplinary Applications
Difeng Cai
Summary: Generating quasirandom points with high uniformity is a fundamental task in many fields. This paper presents a novel physics-informed framework that can transform a given set of points into a distribution with better uniformity. Two schemes based on molecular dynamics and deep neural networks are introduced. The new framework can be easily extended to other geometries and various experiments demonstrate its effectiveness.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Polymer Science
Luis A. Miccio, Claudia Borredon, Ulises Casado, Anh D. Phan, Gustavo A. Schwartz
Summary: The analysis of structural relaxation dynamics of polymers provides insights into their mechanical properties, which are important for determining a material's suitability for practical applications. However, obtaining the relaxation time through experimental processes after polymer synthesis is time-consuming. In this study, we propose a method that combines artificial neural networks and the elastically collective nonlinear Langevin equation (ECNLE) to estimate the temperature dependence of the main structural relaxation time of polymers based solely on the chemical structure of the monomer.
Review
Neurosciences
M. Belen Pardi, Anna Schroeder, Johannes J. Letzkus
Summary: Accurate perception of the environment involves integrating sensory signals with internally generated information. Recent advances in brain imaging techniques allow for the study of how top-down information is encoded and integrated with bottom-up signals.
TRENDS IN NEUROSCIENCES
(2023)
Article
Optics
Mario Krenn, Jonas Landgraf, Thomas Foesel, Florian Marquardt
Summary: In recent years, the rapid development in machine learning has had a significant impact on various fields of science and technology. This perspective article explores how quantum technologies are benefiting from this revolution. It showcases how scientists have utilized machine learning and artificial intelligence to analyze quantum measurements, estimate parameters of quantum devices, discover new quantum experimental setups and protocols, and improve aspects of quantum computing, communication, and simulation. The article also highlights the challenges and future possibilities in the field and provides speculative visions for the next decade.
Article
Engineering, Electrical & Electronic
Paul M. Baggenstoss, Steven Kay
Summary: A new information criterion for nonlinear dimension reduction based on PDF estimation is proposed, which maximizes information transfer and can generate desired output distribution. The method is general, efficient, and superior to traditional dimension reduction methods in experiments with high-dimensional non-Gaussian input data.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Neurosciences
Mayank R. Mehta
Article
Biochemistry & Molecular Biology
Lavanya Acharya, Zahra M. Aghajan, Cliff Vuong, Jason J. Moore, Mayank R. Mehta
Article
Multidisciplinary Sciences
Jason J. Moore, Pascal M. Ravassard, David Ho, Lavanya Acharya, Ashley L. Kees, Cliff Vuong, Mayank R. Mehta
Article
Neurosciences
Zahra M. Aghajan, Lavanya Acharya, Jason J. Moore, Jesse D. Cushman, Cliff Vuong, Mayank R. Mehta
NATURE NEUROSCIENCE
(2015)
Article
Multidisciplinary Sciences
Jesse D. Cushman, Daniel B. Aharoni, Bernard Willers, Pascal Ravassard, Ashley Kees, Cliff Vuong, Briana Popeney, Katsushi Arisaka, Mayank R. Mehta
Article
Neurosciences
Nasrin Sadat Hashemi, Fereshteh Dehnavi, Sahar Moghimi, Maryam Ghorbani
Article
Clinical Neurology
Fereshteh Dehnavi, Sahar Moghimi, Shima Sadrabadi Haghighi, Mostafa Safaie, Maryam Ghorbani
Article
Neurosciences
Shima Sadrabadi Haghighi, Maryam Ghorbani, Fereshteh Dehnavi, Mostafa Safaie, Sahar Moghimi
Article
Neurosciences
Sahar Moghimi, Azadeh Shadkam, Mahdi Mahmoudzadeh, Olivia Calipe, Marine Panzani, Mohammadreza Edalati, Maryam Ghorbani, Laura Routier, Fabrice Wallois
HUMAN BRAIN MAPPING
(2020)
Article
Clinical Neurology
Fereshteh Dehnavi, Ping Chai Koo-Poeggel, Maryam Ghorbani, Lisa Marshall
Summary: The study focuses on the synchronization of neural activity and dynamic changes in EEG during sleep, aiming to investigate the association between these factors and the effectiveness of stimulation on memory retention. Results indicate that specific neural activity patterns during deep nonrapid eye movement baseline sleep, such as nesting of slow spindles to SO trough and characteristics of SO slope, are indicative of stimulation efficacy on memory consolidation.
Article
Neurosciences
Karen Safaryan, Mayank R. Mehta
Summary: Hippocampal theta rhythm plays a vital role in neuroplasticity, learning, and memory, with variations across species. Running in virtual reality greatly amplifies theta rhythmicity and can even lead to the emergence of a novel rhythm, indicating that multisensory experience influences hippocampal rhythms. Virtual reality can be utilized to enhance or regulate brain rhythms and influence neural dynamics, connections, and plasticity.
NATURE NEUROSCIENCE
(2021)
Article
Multidisciplinary Sciences
Jason J. Moore, Jesse D. Cushman, Lavanya Acharya, Briana Popeney, Mayank R. Mehta
Summary: The hippocampus plays a crucial role in spatial navigation, Hebbian synaptic plasticity, spatial selectivity, and episodic memory, yet the exact relationship between these functions remains unclear. Hippocampal neurons exhibit multiplexed selectivity while rats perform virtual navigation tasks, encoding path distance and head angle with weak allocentric spatial components. Neural activity in the hippocampus shows increased clustering and becomes better predictors of behaviorally relevant variables, supporting navigation and encoding journey-specific episodes through Hebbian plasticity.
Article
Multidisciplinary Sciences
Chinmay S. Purandare, Shonali Dhingra, Rodrigo Rios, Cliff Vuong, Thuc To, Ayaka Hachisuka, Krishna Choudhary, Mayank R. Mehta
Summary: Research shows that rodent hippocampal neurons can retrospectively encode the position and distance of a moving light bar, demonstrating visually evoked vectorial selectivity (VEVS) that differs from place cells but shows correlation during spatial exploration.
Article
Neurosciences
Amin Azimi, Zahra Alizadeh, Maryam Ghorbani
Summary: The relationship between cortical slow oscillations, thalamic spindles, and hippocampal sharp wave ripples during slow wave sleep is crucial for memory consolidation, yet poorly understood. A minimal hippocampocortico-thalamic network model was developed to explain this mechanism, which was experimentally verified in sleeping rodents. The model predicted the nesting of ripples in spindle troughs and the longer duration but lower amplitude of ripples during spindle or slow oscillation co-occurrence.
Article
Neurosciences
Alain Destexhe, Mayank Mehta
Summary: The dendritic membrane potential in drug-free, naturally behaving rats was recently measured for the first time, revealing that neuronal dendrites generate a significantly higher number of sodium spikes compared to somatic spikes. This review discusses the experimental findings, computational models, and consequences of intense spike traffic in dendrites. It highlights the role of biophysical properties of dendritic ion channels in driving dendritic spiking activity, and the implications of fast dendritic spikes for synaptic strength and computational capacity in neuronal networks.
