脑科学 · 全部实体（第 89 / 100 页）

本页收录第 8801–8900 条，共 10000 条 · 返回 脑科学 首页

• Application and Computation of Probabilistic Neural Plasticity
  The discovery of neural plasticity has proved that throughout the life of a human being, the brain reorganizes itself through forming new neural connections. The formation of new neural connections are achieved through the brain's effort to adapt to new enviro…
  arxiv Soaad Hossain 2019-05-25T07:03:56Z 置信度 0.78 q-bio.NCcs.CEcs.NE
• A Review on Neural Network Models of Schizophrenia and Autism Spectrum Disorder
  This survey presents the most relevant neural network models of autism spectrum disorder and schizophrenia, from the first connectionist models to recent deep network architectures. We analyzed and compared the most representative symptoms with its neural mode…
  arxiv Pablo Lanillos, Daniel Oliva, Anja Philippsen, Yuichi Yamashita 等 2019-06-24T15:10:44Z 置信度 0.78 q-bio.NCcs.AIcs.NE
• Neural Networks and Schramm-Loewner Evolutions
  In this manuscript, we explore the application of neural networks to predict the natural parameter $κ\geq 0$ of Schramm-Loewner Evolution (SLE$_κ$) theory. SLE$_κ$ is a family of random fractal curves that has significant implications in Statistical Mechanics …
  arxiv Neilesh Shrotri, Vlad Margarint 2026-06-01T17:51:45Z 置信度 0.78 cond-mat.dis-nnmath.PR
• A stochastic discrete slip approach to microplasticity: Application to submicron W pillars
  A stochastic discrete slip approach is proposed to model plastic deformation in submicron domains. The model is applied to the study of submicron pillar ($D~\leq~1μm$) compression experiments on tungsten (W), a prototypical metal for applications under extreme…
  arxiv Carlos J. Ruestes, Javier Segurado 2024-04-16T09:47:53Z 置信度 0.78 cond-mat.mtrl-sci
• How transferable are features in deep neural networks?
  Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Such first-layer features appear not to be specific to a particular dataset or task, bu…
  arxiv Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson 2014-11-06T23:09:37Z 置信度 0.78 cs.LGcs.NE
• Towards Dropout Training for Convolutional Neural Networks
  Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates tha…
  arxiv Haibing Wu, Xiaodong Gu 2015-12-01T12:46:11Z 置信度 0.78 cs.LGcs.CVcs.NE
• Modulation of synaptic plasticity by glutamatergic gliotransmission: A modeling study
  Glutamatergic gliotransmission, that is the release of glutamate from perisynaptic astrocyte processes in an activity-dependent manner, has emerged as a potentially crucial signaling pathway for regulation of synaptic plasticity, yet its modes of expression an…
  arxiv Maurizio De Pittà, Nicolas Brunel 2016-08-12T01:46:26Z 置信度 0.78 q-bio.NC
• A framework for studying synaptic plasticity with neural spike train data
  Learning and memory in the brain are implemented by complex, time-varying changes in neural circuitry. The computational rules according to which synaptic weights change over time are the subject of much research, and are not precisely understood. Until recent…
  arxiv Scott W. Linderman, Christopher H. Stock, Ryan P. Adams 2014-11-14T23:01:38Z 置信度 0.78 stat.MLq-bio.NC
• Acute Lymphoblastic Leukemia Detection Using Hypercomplex-Valued Convolutional Neural Networks
  This paper features convolutional neural networks defined on hypercomplex algebras applied to classify lymphocytes in blood smear digital microscopic images. Such classification is helpful for the diagnosis of acute lymphoblast leukemia (ALL), a type of blood …
  arxiv Guilherme Vieira, Marcos Eduardo Valle 2022-05-26T11:16:34Z 置信度 0.78 cs.CVcs.LGcs.NEeess.IV
• Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics
  In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli which caused them. The Bayesian solution to this problem is known as a Bayes filter, which app…
  arxiv Sacha Sokoloski 2015-12-22T14:52:14Z 置信度 0.78 cs.LGstat.ML
• Do Kernel and Neural Embeddings Help in Training and Generalization?
  Recent results on optimization and generalization properties of neural networks showed that in a simple two-layer network, the alignment of the labels to the eigenvectors of the corresponding Gram matrix determines the convergence of the optimization during tr…
  arxiv Arman Rahbar, Emilio Jorge, Devdatt Dubhashi, Morteza Haghir Chehreghani 2019-05-13T15:38:38Z 置信度 0.78 cs.LGcs.AIstat.ML
• A multiplicative finite strain crystal plasticity formulation based on additive elastic corrector rates: Theory and numerical implementation
  The purpose of continuum plasticity models is to efficiently predict the behavior of structures beyond their elastic limits. The purpose of multiscale materials science models, among them crystal plasticity models, is to understand the material behavior and de…
  arxiv Meijuan Zhang, K. Nguyen, Javier Segurado, Francisco J. Montans 2020-07-13T03:44:46Z 置信度 0.78 cond-mat.mtrl-sci
• Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks
  The evolution of the human brain has led to the development of complex synaptic plasticity, enabling dynamic adaptation to a constantly evolving world. This progress inspires our exploration into a new paradigm for Spiking Neural Networks (SNNs): a Plasticity-…
  arxiv Guobin Shen, Dongcheng Zhao, Yiting Dong, Yang Li 等 2023-08-23T11:11:31Z 置信度 0.78 cs.NE
• Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
  The impressive lifelong learning in animal brains is primarily enabled by plastic changes in synaptic connectivity. Importantly, these changes are not passive, but are actively controlled by neuromodulation, which is itself under the control of the brain. The …
  arxiv Thomas Miconi, Aditya Rawal, Jeff Clune, Kenneth O. Stanley 2020-02-24T23:19:17Z 置信度 0.78 cs.NE
• Plasticity of Zr-Nb-Ti-Ta-Hf high-entropy alloys
  We have investigated the plastic deformation properties of non-equiatomic single phase Zr-Nb-Ti-Ta-Hf high-entropy alloys from room temperature up to 300 °C. Uniaxial deformation tests at a constant strain rate of 10$^{-4}$ s$^{-1}$ were performed including in…
  arxiv M. Feuerbacher, M. Heidelmann, C. Thomas 2014-01-16T11:48:08Z 置信度 0.78 cond-mat.mtrl-sci
• Control of synaptic plasticity in neural networks
  The brain is a nonlinear and highly Recurrent Neural Network (RNN). This RNN is surprisingly plastic and supports our astonishing ability to learn and execute complex tasks. However, learning is incredibly complicated due to the brain's nonlinear nature and th…
  arxiv Mohammad Modiri 2023-03-10T13:36:31Z 置信度 0.78 cs.NEcs.LGcs.RO
• Optimizing Neural Networks via Koopman Operator Theory
  Koopman operator theory, a powerful framework for discovering the underlying dynamics of nonlinear dynamical systems, was recently shown to be intimately connected with neural network training. In this work, we take the first steps in making use of this connec…
  arxiv Akshunna S. Dogra, William T Redman 2020-06-03T16:23:07Z 置信度 0.78 cs.NEeess.SPmath.DSphysics.comp-ph
• SpikePropamine: Differentiable Plasticity in Spiking Neural Networks
  The adaptive changes in synaptic efficacy that occur between spiking neurons have been demonstrated to play a critical role in learning for biological neural networks. Despite this source of inspiration, many learning focused applications using Spiking Neural …
  arxiv Samuel Schmidgall, Julia Ashkanazy, Wallace Lawson, Joe Hays 2021-06-04T19:29:07Z 置信度 0.78 cs.NEcs.LGcs.RO
• A Prototype-Based Neural Network for Image Anomaly Detection and Localization
  Image anomaly detection and localization perform not only image-level anomaly classification but also locate pixel-level anomaly regions. Recently, it has received much research attention due to its wide application in various fields. This paper proposes Proto…
  arxiv Chao Huang, Zhao Kang, Hong Wu 2023-10-04T04:27:16Z 置信度 0.78 cs.CV
• Existence and Global Logarithmic Stability of Impulsive Neural Networks with Time Delay
  The stability and convergence of the neural networks are the fundamental characteristics in the Hopfield type networks. Since time delay is ubiquitous in most physical and biological systems, more attention is being made for the delayed neural networks. The in…
  arxiv A. K. Ojha, Dushmanta Mallick, C. Mallick 2010-02-05T09:20:51Z 置信度 0.78 cs.NE
• Adversarial Frontier Stitching for Remote Neural Network Watermarking
  The state of the art performance of deep learning models comes at a high cost for companies and institutions, due to the tedious data collection and the heavy processing requirements. Recently, [35, 22] proposed to watermark convolutional neural networks for i…
  arxiv Erwan Le Merrer, Patrick Perez, Gilles Trédan 2017-11-06T13:57:08Z 置信度 0.78 cs.CR
• Continual Learning for Recurrent Neural Networks: an Empirical Evaluation
  Learning continuously during all model lifetime is fundamental to deploy machine learning solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with recurrent neural networks could pave the way to a large number of applicatio…
  arxiv Andrea Cossu, Antonio Carta, Vincenzo Lomonaco, Davide Bacciu 2021-03-12T19:25:28Z 置信度 0.78 cs.LGcs.AI
• Masked Conditional Neural Networks for Audio Classification
  We present the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural Network (MCLNN) designed for temporal signal recognition. The CLNN takes into consideration the temporal nature of the sound signal and the MCLNN extends upon the CLNN through a…
  arxiv Fady Medhat, David Chesmore, John Robinson 2018-03-06T20:54:00Z 置信度 0.78 stat.MLcs.LGcs.SDeess.AS
• Neural Plasticity Networks
  Neural plasticity is an important functionality of human brain, in which number of neurons and synapses can shrink or expand in response to stimuli throughout the span of life. We model this dynamic learning process as an $L_0$-norm regularized binary optimiza…
  arxiv Yang Li, Shihao Ji 2019-08-13T18:57:30Z 置信度 0.78 cs.NEcs.LGstat.ML
• Beam Search Strategies for Neural Machine Translation
  The basic concept in Neural Machine Translation (NMT) is to train a large Neural Network that maximizes the translation performance on a given parallel corpus. NMT is then using a simple left-to-right beam-search decoder to generate new translations that appro…
  arxiv Markus Freitag, Yaser Al-Onaizan 2017-02-06T22:08:46Z 置信度 0.78 cs.CL
• Predicting concentration levels of air pollutants by transfer learning and recurrent neural network
  Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short t…
  arxiv Iat Hang Fong, Tengyue Li, Simon Fong, Raymond K. Wong 等 2025-01-30T23:39:19Z 置信度 0.78 cs.LGcs.NEphysics.ao-ph
• Neural Unsigned Distance Fields for Implicit Function Learning
  In this work we target a learnable output representation that allows continuous, high resolution outputs of arbitrary shape. Recent works represent 3D surfaces implicitly with a Neural Network, thereby breaking previous barriers in resolution, and ability to r…
  arxiv Julian Chibane, Aymen Mir, Gerard Pons-Moll 2020-10-26T22:49:45Z 置信度 0.78 cs.CVcs.LG
• Crystal elasto-plasticity on the Poincaré half-plane
  We explore the nonlinear variational modelling of two-dimensional (2D) crystal plasticity based on strain energies which are invariant under the full symmetry group of 2D lattices. We use a natural parameterization of strain space via the upper complex Poincar…
  arxiv Edoardo Arbib, Paolo Biscari, Luca Bortoloni, Clara Patriarca 等 2019-07-21T21:12:15Z 置信度 0.78 cond-mat.mtrl-scicond-mat.soft
• Stable Lifelong Learning: Spiking neurons as a solution to instability in plastic neural networks
  Synaptic plasticity poses itself as a powerful method of self-regulated unsupervised learning in neural networks. A recent resurgence of interest has developed in utilizing Artificial Neural Networks (ANNs) together with synaptic plasticity for intra-lifetime …
  arxiv Samuel Schmidgall, Joe Hays 2021-11-07T15:55:48Z 置信度 0.78 cs.NE
• Scale-bridging dislocation plasticity in MgO at room temperature
  Dislocations in ceramics have recently gained renewed research interest, in contrast to the traditional belief that ceramics are inherently brittle. Understanding dislocation mechanics in representative oxides is beneficial for effective dislocation engineerin…
  arxiv Jiawen Zhang, Zhangtao Li, Yuwei Zhang, Hendrik Holz 等 2025-10-21T17:25:47Z 置信度 0.78 cond-mat.mtrl-sci
• Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity
  Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological …
  arxiv Thomas Limbacher, Ozan Özdenizci, Robert Legenstein 2022-05-23T12:48:37Z 置信度 0.78 cs.NEcs.LGq-bio.NC
• IP$^{2}$-RSNN: Bi-level Intrinsic Plasticity Enables Learning-to-learn in Recurrent Spiking Neural Networks
  Learning-to-learn (L2L), defined as progressively faster learning across similar tasks, is fundamental to both neuroscience and artificial intelligence. However, its neural basis remains elusive, as most studies emphasize neural population dynamics induced by …
  arxiv Yingchao Yu, Yaochu Jin, Kuangrong Hao, Yuchen Xiao 等 2025-01-24T14:45:03Z 置信度 0.78 cs.NEcs.LG
• Developmental Plasticity-inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks
  Developmental plasticity plays a prominent role in shaping the brain's structure during ongoing learning in response to dynamically changing environments. However, the existing network compression methods for deep artificial neural networks (ANNs) and spiking …
  arxiv Bing Han, Feifei Zhao, Yi Zeng, Guobin Shen 2022-11-23T05:26:51Z 置信度 0.78 cs.NEcs.AI
• Reinitializing weights vs units for maintaining plasticity in neural networks
  Loss of plasticity is a phenomenon in which a neural network loses its ability to learn when trained for an extended time on non-stationary data. It is a crucial problem to overcome when designing systems that learn continually. An effective technique for prev…
  arxiv J. Fernando Hernandez-Garcia, Shibhansh Dohare, Jun Luo, Rich S. Sutton 2025-07-31T23:25:19Z 置信度 0.78 cs.NEcs.AI
• Powerset Convolutional Neural Networks
  We present a novel class of convolutional neural networks (CNNs) for set functions, i.e., data indexed with the powerset of a finite set. The convolutions are derived as linear, shift-equivariant functions for various notions of shifts on set functions. The fr…
  arxiv Chris Wendler, Dan Alistarh, Markus Püschel 2019-09-05T08:08:40Z 置信度 0.78 cs.LGstat.ML
• Exoplanet formation inference using conditional invertible neural networks
  The interpretation of the origin of observed exoplanets is usually done only qualitatively due to uncertainties of key parameters in planet formation models. To allow a quantitative methodology which traces back in time to the planet birth locations, we train …
  arxiv Remo Burn, Victor F. Ksoll, Hubert Klahr, Thomas Henning 2025-12-05T14:38:34Z 置信度 0.78 astro-ph.EPcs.NEphysics.data-an
• Encoding binary neural codes in networks of threshold-linear neurons
  Networks of neurons in the brain encode preferred patterns of neural activity via their synaptic connections. Despite receiving considerable attention, the precise relationship between network connectivity and encoded patterns is still poorly understood. Here …
  arxiv Carina Curto, Anda Degeratu, Vladimir Itskov 2012-11-30T22:43:11Z 置信度 0.78 q-bio.NCmath.COmath.MG
• On fracture in finite strain gradient plasticity
  In this work a general framework for damage and fracture assessment including the effect of strain gradients is provided. Both mechanism-based and phenomenological strain gradient plasticity (SGP) theories are implemented numerically using finite deformation t…
  arxiv Emilio Martínez-Pañeda, Christian F. Niordson 2017-11-03T09:50:26Z 置信度 0.78 cond-mat.mtrl-sci
• Optimal hierarchical modular topologies for producing limited sustained activation of neural networks
  An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable regimes of network activation, typically arising from a limited parameter range. In this range …
  arxiv Marcus Kaiser, Claus C. Hilgetag 2010-03-16T05:13:41Z 置信度 0.78 q-bio.NCcond-mat.dis-nnphysics.soc-ph
• Neural Memory Plasticity for Anomaly Detection
  In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, trajectory prediction, object tracking, and language modelling. However, we observ…
  arxiv Tharindu Fernando, Simon Denman, David Ahmedt-Aristizabal, Sridha Sridharan 等 2019-10-12T00:32:56Z 置信度 0.78 cs.NEcs.CVcs.LGstat.ML
• NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning
  Spiking Neural Networks (SNNs) are computational models inspired by the structure and dynamics of biological neuronal networks. Their event-driven nature enables them to achieve high energy efficiency, particularly when deployed on neuromorphic hardware platfo…
  arxiv Ashish Gautam, Prasanna Date, Shruti Kulkarni, Robert Patton 等 2025-06-17T03:02:04Z 置信度 0.78 cs.NEcs.AI
• Understanding plasticity in neural networks
  Plasticity, the ability of a neural network to quickly change its predictions in response to new information, is essential for the adaptability and robustness of deep reinforcement learning systems. Deep neural networks are known to lose plasticity over the co…
  arxiv Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Avila Pires 等 2023-03-02T18:47:51Z 置信度 0.78 cs.LG
• Synthesis of recurrent neural networks for dynamical system simulation
  We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of …
  arxiv Adam Trischler, Gabriele MT D'Eleuterio 2015-12-17T18:08:33Z 置信度 0.78 cs.NE
• Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
  Works on lottery ticket hypothesis (LTH) and single-shot network pruning (SNIP) have raised a lot of attention currently on post-training pruning (iterative magnitude pruning), and before-training pruning (pruning at initialization). The former method suffers …
  arxiv Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi 等 2021-06-19T02:09:25Z 置信度 0.78 cs.LGcs.CV
• Improving Generalization of Deep Neural Networks by Leveraging Margin Distribution
  Recent research has used margin theory to analyze the generalization performance for deep neural networks (DNNs). The existed results are almost based on the spectrally-normalized minimum margin. However, optimizing the minimum margin ignores a mass of informa…
  arxiv Shen-Huan Lyu, Lu Wang, Zhi-Hua Zhou 2018-12-27T16:34:54Z 置信度 0.78 cs.LGstat.ML
• A flexible framework for structural plasticity in GPU-accelerated sparse spiking neural networks
  The majority of research in both training Artificial Neural Networks (ANNs) and modeling learning in biological brains focuses on synaptic plasticity, where learning equates to changing the strength of existing connections. However, in biological brains, struc…
  arxiv James C. Knight, Johanna Senk, Thomas Nowotny 2025-10-22T16:50:00Z 置信度 0.78 cs.NEq-bio.NC
• Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation
  Accurate uncertainty quantification is necessary to enhance the reliability of deep learning models in real-world applications. In the case of regression tasks, prediction intervals (PIs) should be provided along with the deterministic predictions of deep lear…
  arxiv Giorgio Morales, John W. Sheppard 2022-12-13T05:03:16Z 置信度 0.78 cs.LGstat.ML
• Percolation Approach to Study Connectivity in Living Neural Networks
  We study neural connectivity in cultures of rat hippocampal neurons. We measure the neurons' response to an electric stimulation for gradual lower connectivity, and characterize the size of the giant cluster in the network. The connectivity undergoes a percola…
  arxiv Jordi Soriano, Ilan Breskin, Elisha Moses, Tsvi Tlusty 2010-07-28T15:58:03Z 置信度 0.78 q-bio.NCcond-mat.dis-nnphysics.bio-ph
• Development of a thermo-mechanically coupled crystal plasticity modeling framework: application to polycrystalline homogenization
  Accurate predictions of thermo-mechanically coupled process in metals can lead to a reduction of cost and an increase of productivity in manufacturing processes such as forming. For modeling these coupled processes with the finite element method, accurate desc…
  arxiv Jifeng Li, Ignacio Romero, Javier Segurado 2019-03-08T18:22:13Z 置信度 0.78 cond-mat.mtrl-sciphysics.comp-ph
• Hebbian imprinting and retrieval in oscillatory neural networks
  We introduce a model of generalized Hebbian learning and retrieval in oscillatory neural networks modeling cortical areas such as hippocampus and olfactory cortex. Recent experiments have shown that synaptic plasticity depends on spike timing, especially on sy…
  arxiv Silvia Scarpetta, Zhaoping Li, John Hertz 2001-11-02T15:55:49Z 置信度 0.78 cond-mat.dis-nnq-bio.NC
• Task learning through stimulation-induced plasticity in neural networks
  Synaptic plasticity dynamically shapes the connectivity of neural systems and is key to learning processes in the brain. To what extent the mechanisms of plasticity can be exploited to drive a neural network and make it perform some kind of computational task …
  arxiv Francesco Borra, Simona Cocco, Rémi Monasson 2024-12-02T16:29:51Z 置信度 0.78 q-bio.NCcond-mat.dis-nn
• Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation
  Several recent trends in machine learning theory and practice, from the design of state-of-the-art Gaussian Process to the convergence analysis of deep neural nets (DNNs) under stochastic gradient descent (SGD), have found it fruitful to study wide random neur…
  arxiv Greg Yang 2019-02-13T06:09:18Z 置信度 0.78 cs.NEcond-mat.dis-nncs.LGmath-phstat.ML
• Training a Hidden Markov Model with a Bayesian Spiking Neural Network
  It is of some interest to understand how statistically based mechanisms for signal processing might be integrated with biologically motivated mechanisms such as neural networks. This paper explores a novel hybrid approach for classifying segments of sequential…
  arxiv Amirhossein Tavanaei, Anthony S Maida 2016-06-02T19:48:22Z 置信度 0.78 cs.NE
• MomentumRNN: Integrating Momentum into Recurrent Neural Networks
  Designing deep neural networks is an art that often involves an expensive search over candidate architectures. To overcome this for recurrent neural nets (RNNs), we establish a connection between the hidden state dynamics in an RNN and gradient descent (GD). W…
  arxiv Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher 等 2020-06-12T03:02:29Z 置信度 0.78 cs.LGmath.DSstat.ML
• Spikes Synchronization in Neural Networks with Synaptic Plasticity
  In this paper, we investigated the neural spikes synchronisation in a neural network with synaptic plasticity and external perturbation. In the simulations the neural dynamics is described by the Hodgkin Huxley model considering chemical synapses (excitatory) …
  arxiv Rafael R. Borges, Kelly C. Iarosz, Antonio M. Batista, Iberê L. Caldas 等 2015-05-07T16:40:43Z 置信度 0.78 physics.bio-ph
• Wet TinyML: Chemical Neural Network Using Gene Regulation and Cell Plasticity
  In our earlier work, we introduced the concept of Gene Regulatory Neural Network (GRNN), which utilizes natural neural network-like structures inherent in biological cells to perform computing tasks using chemical inputs. We define this form of chemical-based …
  arxiv Samitha Somathilaka, Adrian Ratwatte, Sasitharan Balasubramaniam, Mehmet Can Vuran 等 2024-03-13T14:00:18Z 置信度 0.78 cs.NEcs.AR
• A link between microstructure evolution and macroscopic response in elasto-plasticity: formulation and numerical approximation of the higher-dimensional Continuum Dislocation Dynamics theory
  Micro-plasticity theories and models are suitable to explain and predict mechanical response of devices on length scales where the influence of the carrier of plastic deformation - the dislocations - cannot be neglected or completely averaged out. To consider …
  arxiv Stefan Sandfeld, Ekkachai Thawinan, Christian Wieners 2015-01-09T13:35:23Z 置信度 0.78 cond-mat.mtrl-sci
• A Framework for Predicting Phishing Websites using Neural Networks
  In India many people are now dependent on online banking. This raises security concerns as the banking websites are forged and fraud can be committed by identity theft. These forged websites are called as Phishing websites and created by malicious people to mi…
  arxiv A. Martin, Na. Ba. Anutthamaa, M. Sathyavathy, Marie Manjari Saint Francois 等 2011-09-06T06:05:12Z 置信度 0.78 cs.NE
• Correlation-aware Unsupervised Change-point Detection via Graph Neural Networks
  Change-point detection (CPD) aims to detect abrupt changes over time series data. Intuitively, effective CPD over multivariate time series should require explicit modeling of the dependencies across input variables. However, existing CPD methods either ignore …
  arxiv Ruohong Zhang, Yu Hao, Donghan Yu, Wei-Cheng Chang 等 2020-04-24T18:28:57Z 置信度 0.78 cs.LGstat.ML
• Infinite-channel deep stable convolutional neural networks
  The interplay between infinite-width neural networks (NNs) and classes of Gaussian processes (GPs) is well known since the seminal work of Neal (1996). While numerous theoretical refinements have been proposed in the recent years, the interplay between NNs and…
  arxiv Daniele Bracale, Stefano Favaro, Sandra Fortini, Stefano Peluchetti 2021-02-07T08:12:46Z 置信度 0.78 stat.MLcs.LG
• Distantly-Supervised Neural Relation Extraction with Side Information using BERT
  Relation extraction (RE) consists in categorizing the relationship between entities in a sentence. A recent paradigm to develop relation extractors is Distant Supervision (DS), which allows the automatic creation of new datasets by taking an alignment between …
  arxiv Johny Moreira, Chaina Oliveira, David Macêdo, Cleber Zanchettin 等 2020-04-29T19:29:10Z 置信度 0.78 cs.CLcs.IRcs.LG
• Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity
  The recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear neuron model. As a heterogenous network model, ONNs are based on a generalized …
  arxiv Serkan Kiranyaz, Junaid Malik, Habib Ben Abdallah, Turker Ince 等 2020-08-21T19:03:23Z 置信度 0.78 cs.NEcs.LGstat.ML
• Music Genre Classification using Masked Conditional Neural Networks
  The ConditionaL Neural Networks (CLNN) and the Masked ConditionaL Neural Networks (MCLNN) exploit the nature of multi-dimensional temporal signals. The CLNN captures the conditional temporal influence between the frames in a window and the mask in the MCLNN en…
  arxiv Fady Medhat, David Chesmore, John Robinson 2018-02-18T19:55:09Z 置信度 0.78 cs.LGstat.ML
• EENA: Efficient Evolution of Neural Architecture
  Latest algorithms for automatic neural architecture search perform remarkable but are basically directionless in search space and computational expensive in training of every intermediate architecture. In this paper, we propose a method for efficient architect…
  arxiv Hui Zhu, Zhulin An, Chuanguang Yang, Kaiqiang Xu 等 2019-05-10T02:34:23Z 置信度 0.78 cs.NEcs.CVcs.LGstat.ML
• Learning with Spike Synchrony in Spiking Neural Networks
  Spiking neural networks (SNNs) promise energy-efficient computation by mimicking biological neural dynamics, yet existing plasticity rules focus on isolated spike pairs and fail to leverage the synchronous activity patterns that drive learning in biological sy…
  arxiv Yuchen Tian, Assel Kembay, Samuel Tensingh, Nhan Duy Truong 等 2025-04-14T04:01:40Z 置信度 0.78 cs.NEcs.AI
• Effect of dilution in asymmetric recurrent neural networks
  We study with numerical simulation the possible limit behaviors of synchronous discrete-time deterministic recurrent neural networks composed of N binary neurons as a function of a network's level of dilution and asymmetry. The network dilution measures the fr…
  arxiv Viola Folli, Giorgio Gosti, Marco Leonetti, Giancarlo Ruocco 2018-05-10T08:41:27Z 置信度 0.78 cond-mat.dis-nncs.NEq-bio.NC
• MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks
  Heterogeneous graph neural networks (HGNNs) were proposed for representation learning on structural data with multiple types of nodes and edges. To deal with the performance degradation issue when HGNNs become deep, researchers combine metapaths into HGNNs to …
  arxiv Xinyu Fu, Irwin King 2022-11-23T09:13:33Z 置信度 0.78 cs.LG
• Towards Neural Programming Interfaces
  It is notoriously difficult to control the behavior of artificial neural networks such as generative neural language models. We recast the problem of controlling natural language generation as that of learning to interface with a pretrained language model, jus…
  arxiv Zachary C. Brown, Nathaniel Robinson, David Wingate, Nancy Fulda 2020-12-10T21:17:04Z 置信度 0.78 cs.CLcs.AI
• A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis
  This paper proposes a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series …
  arxiv Hideaki Hayashi, Taro Shibanoki, Keisuke Shima, Yuichi Kurita 等 2019-11-14T09:48:41Z 置信度 0.78 cs.LGstat.ML
• Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks
  Neural networks that can capture key principles underlying brain computation offer exciting new opportunities for developing artificial intelligence and brain-like computing algorithms. Such networks remain biologically plausible while leveraging localized for…
  arxiv Naresh Ravichandran, Anders Lansner, Pawel Herman 2024-06-07T08:32:30Z 置信度 0.78 cs.NEq-bio.NC
• Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model
  The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfy constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning wi…
  arxiv Alexander Woodward, Tom Froese, Takashi Ikegami 2014-09-01T16:20:41Z 置信度 0.78 nlin.AOcs.NEq-bio.NC
• PC-SNN: Predictive Coding-based Local Hebbian Plasticity Learning in Spiking Neural Networks
  Spiking Neural Networks (SNNs), regarded as the third generation of neural networks, emulate the brain's information processing with unparalleled biological plausibility compared to traditional neural networks. However, their non-linear, event-driven dynamics …
  arxiv Haidong Wang, Xiaogang Xiong, Mengting Lan, Yinghao Chu 等 2022-11-24T09:56:02Z 置信度 0.78 cs.NE
• A plasticity model with yield surface distortion for non proportional loading
  In order to enhance the modeling of metallic materials behavior in non proportional loadings, a modification of the classical elastic-plastic models including distortion of the yield surface is proposed. The new yield criterion uses the same norm as in the cla…
  arxiv Marc Louis Maurice François 2010-02-04T07:51:22Z 置信度 0.78 physics.class-ph
• Indirect and Direct Training of Spiking Neural Networks for End-to-End Control of a Lane-Keeping Vehicle
  Building spiking neural networks (SNNs) based on biological synaptic plasticities holds a promising potential for accomplishing fast and energy-efficient computing, which is beneficial to mobile robotic applications. However, the implementations of SNNs in rob…
  arxiv Zhenshan Bing, Claus Meschede, Guang Chen, Alois Knoll 等 2020-03-10T09:35:46Z 置信度 0.78 cs.NE
• On Information Plane Analyses of Neural Network Classifiers -- A Review
  We review the current literature concerned with information plane analyses of neural network classifiers. While the underlying information bottleneck theory and the claim that information-theoretic compression is causally linked to generalization are plausible…
  arxiv Bernhard C. Geiger 2020-03-21T14:43:45Z 置信度 0.78 cs.LGcs.CVcs.ITstat.ML
• A Neural Networks Committee for the Contextual Bandit Problem
  This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to modelize the value of rewards knowing the context. Two variants, based on multi-…
  arxiv Robin Allesiardo, Raphael Feraud, Djallel Bouneffouf 2014-09-29T17:08:21Z 置信度 0.78 cs.NEcs.LG
• Can We Understand Plasticity Through Neural Collapse?
  This paper explores the connection between two recently identified phenomena in deep learning: plasticity loss and neural collapse. We analyze their correlation in different scenarios, revealing a significant association during the initial training phase on th…
  arxiv Guglielmo Bonifazi, Iason Chalas, Gian Hess, Jakub Łucki 2024-04-03T13:21:58Z 置信度 0.78 cs.LGcs.AI
• Characterization of Generalizability of Spike Timing Dependent Plasticity trained Spiking Neural Networks
  A Spiking Neural Network (SNN) is trained with Spike Timing Dependent Plasticity (STDP), which is a neuro-inspired unsupervised learning method for various machine learning applications. This paper studies the generalizability properties of the STDP learning p…
  arxiv Biswadeep Chakraborty, Saibal Mukhopadhyay 2021-05-31T02:19:06Z 置信度 0.78 cs.NEcs.AI
• Neural Network Plasticity and Loss Sharpness
  In recent years, continual learning, a prediction setting in which the problem environment may evolve over time, has become an increasingly popular research field due to the framework's gearing towards complex, non-stationary objectives. Learning such objectiv…
  arxiv Max Koster, Jude Kukla 2024-09-25T19:20:09Z 置信度 0.78 cs.LGcs.AI
• A New Chief Editor for <i>Neural Plasticity</i> Growth
  Neural Plasticity is pleased to announce the appointment of Dr. Michel Baudry as its new Chief Editor. Dr. Baudry is currently University Professor at Western University of Health Sciences in Pomona, CA. In this Editorial, Dr. Baudry describes some of the jour…
  crossref Michel Baudry 2020-08-01T23:31:18Z 置信度 0.70
• Neurorehabilitation: Neural Plasticity and Functional Recovery
  crossref Toshiyuki Fujiwara, Nam-Jong Paik, Thomas Platz 2017-04-20T17:00:44Z 置信度 0.70
• The Effects of Leptin Replacement on Neural Plasticity
  Leptin, an adipokine synthesized and secreted mainly by the adipose tissue, has multiple effects on the regulation of food intake, energy expenditure, and metabolism. Its recently-approved analogue, metreleptin, has been evaluated in clinical trials for the tr…
  crossref Gilberto J. Paz-Filho 2016-01-03T16:02:34Z 置信度 0.70
• Retracted: Molecular Basis of GABA Hypofunction in Adolescent Schizophrenia-Like Animals.
  [This retracts the article DOI: 10.1155/2021/9983438.].
  europepmc 2024 置信度 0.80
• Neural Plasticity in Mood Disorders
  The contribution of differences in cellular and anatomical function to mood disorders has been a focus of research attention in biological psychiatry for a very long time.Some of the earliest structural imaging studies demonstrated differences in the size of c…
  crossref F. Scott Hall, Aijun Li, Bingjin Li 2018-05-09T19:31:17Z 置信度 0.70
• Learning following Brain Injury: Neural Plasticity Markers
  crossref Leeanne Carey, Michael Nilsson, Lara Boyd 2019-09-02T19:31:16Z 置信度 0.70
• Plasticity and Anxiety
  crossref Patrice Venault, Georges Chapouthier 2007-08-07T04:54:17Z 置信度 0.70
• Neurorehabilitation: Neural Plasticity and Functional Recovery 2018
  crossref Toshiyuki Fujiwara, Junichi Ushiba, Surjo R. Soekadar 2019-01-21T21:03:24Z 置信度 0.70
• Mechanisms of GABAergic Homeostatic Plasticity
  Homeostatic plasticity ensures that appropriate levels of activity are maintained through compensatory adjustments in synaptic strength and cellular excitability. For instance, excitatory glutamatergic synapses are strengthened following activity blockade and …
  crossref Peter Wenner 2011-08-17T19:01:47Z 置信度 0.70
• Imaging Neural Plasticity following Brain Injury
  crossref Lijun Bai, Lin Ai, Kevin K. W. Wang 2017-02-28T19:45:12Z 置信度 0.70
• Neural Plasticity after Congenital Brain Lesions
  crossref Simona Fiori, Martin Staudt, Roslyn N. Boyd, Andrea Guzzetta 2019-05-02T19:38:16Z 置信度 0.70
• Neural Plasticity during Aging
  The percentage of older people is increasing steadily in the proportion of the total population of the world. In a recent report published by the National Institute on Aging, in March of 2016, it was estimated that 8.5 percent of people, globally accounting fo…
  crossref Mauricio Arcos-Burgos, Francisco Lopera, Diego Sepulveda-Falla, Claudio Mastronardi 2019-03-26T19:36:58Z 置信度 0.70
• Neural plasticity: cellular and molecular mechanisms of neural plasticity
  crossref 2014-05-09T05:43:36Z 置信度 0.70
• Plasticity of Neural Systems in Tinnitus
  This special issue of the journal is dedicated to tinnitus and the role of neuroplasticity in its symptomology. In western industrial countries with a steadily aging population, the number of individuals who suffer from tinnitus is immense. Approximately 50 mi…
  crossref Martin Meyer, Berthold Langguth, Tobias Kleinjung, Aage R. Møller 2014-09-08T17:04:46Z 置信度 0.70
• Neural Plasticity on Body Representations: Advancing Translational Rehabilitation
  crossref Naoyuki Takeuchi, Shin-Ichi Izumi, Jun Ota, Jun Ueda 2016-09-29T18:24:47Z 置信度 0.70
• Modelling
  Simultaneous recording of spike trains is a common practice in several neurophysiological experiments.Interactions between single units in the time domain are performed by cross-correlogram analysis.The purpose of the current study is to develop a system for a…
  crossref 2007-03-15T09:22:50Z 置信度 0.70
• Glial Plasticity
  crossref Tomas C. Bellamy, Anna Dunaevsky, H. Rheinallt Parri 2015-08-05T21:02:26Z 置信度 0.70
• Plasticity and Awareness of Bodily Distortion
  Knowledge of the body is filtered by perceptual information, recalibrated through predominantly innate stored information, and neurally mediated by direct sensory motor information. Despite multiple sources, the immediate prediction, construction, and evaluati…
  crossref Mariella Pazzaglia, Marta Zantedeschi 2016-08-19T06:08:59Z 置信度 0.70
• NGF, Brain and Behavioral Plasticity
  Nerve Growth Factor (NGF) was initially studied for its role as a key player in the regulation of peripheral innervations. However, the successive finding of its release in the bloodstream of male mice following aggressive encounters and its presence in the ce…
  crossref Alessandra Berry, Erika Bindocci, Enrico Alleva 2012-02-16T21:02:35Z 置信度 0.70
• Neural Plasticity and Neurogenesis in Mental Disorders
  Adult neurogenesis, the continuous generation of newborn neurons in discrete regions of the brain throughout life, is now widely regarded as a fundamental mechanism of neural plasticity. This phenomenon, and in particular the integration of new neurons into th…
  crossref Graham Cocks, Mauro G. Carta, Oscar Arias-Carrión, Antonio E. Nardi 2016-03-28T17:01:02Z 置信度 0.70
• Neural Plasticity in Multiple Sclerosis: The Functional and Molecular Background
  Multiple sclerosis is an autoimmune neurodegenerative disorder resulting in motor dysfunction and cognitive decline. The inflammatory and neurodegenerative changes seen in the brains of MS patients lead to progressive disability and increasing brain atrophy. T…
  crossref Dominika Justyna Ksiazek-Winiarek, Piotr Szpakowski, Andrzej Glabinski 2015-07-02T21:02:15Z 置信度 0.70