• PLoS Comput Biol 2013, 9: processing capabilities [3,4]. (sagepub.com)
  • Deep supervised, but not unsupervised, models may explain IT cortical representation Khaligh-Razavi SM, Kriegeskorte N (2014) PLoS Comput Biol 10(11):e1003915. (columbia.edu)
  • Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform. (itm-conferences.org)
  • In addition, we use a document embedding representation via a recurrent neural networks with gated recurrent unit as the main architecture to provide richer representation. (ugm.ac.id)
  • A. A. Nugraha, A. Arifianto, and Suyanto, "Generating image description on Indonesian language using convolutional neural network and gated recurrent unit," 2019 7th Int. Conf. (umm.ac.id)
  • Two types of RNN models, the long short-term memory (LSTM) and the gated recurrent unit (GRU), were developed. (biomedcentral.com)
  • Neural Computation (2008) 20 (5): 1366-1383. (mit.edu)
  • Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, "trained" networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. (nih.gov)
  • Neural Computation (2009) 21 (2): 510-532. (mit.edu)
  • S. Guessasma, D. Bassir, Neural network computation for the evaluation of process rendering: application to thermally sprayed coatings, Int. J. Simul. (ijsmdo.org)
  • In this paper, an improved long short-term memory (LSTM)-based deep neural network structure is proposed for learning variable-length Chinese sentence semantic similarities. (fujipress.jp)
  • Siamese LSTM, a sequence-insensitive deep neural network model, has a limited ability to capture the semantics of natural language because it has difficulty explaining semantic differences based on the differences in syntactic structures or word order in a sentence. (fujipress.jp)
  • Herein we perform an extensive benchmark on models trained with subsets of GDB-13 of different sizes (1 million, 10,000 and 1000), with different SMILES variants (canonical, randomized and DeepSMILES), with two different recurrent cell types (LSTM and GRU) and with different hyperparameter combinations. (biomedcentral.com)
  • One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. (nih.gov)
  • Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. (nih.gov)
  • Reservoir computing (RC) is a branch of AI that offers a highly efficient framework for processing temporal inputs at a low training cost compared to conventional Recurrent Neural Networks (RNNs). (frontiersin.org)
  • The commonly known problem of exploding and vanishing gradients, arising in very deep FNNs and from cyclic connections in RNNs, results in network instability and less effective learning, making the training process complex and expensive. (frontiersin.org)
  • Recurrent neural networks (RNNs) have reached striking performance in many natural language processing tasks. (upf.edu)
  • Recurrent Neural Networks (RNNs) trained with a set of molecules represented as unique (canonical) SMILES strings, have shown the capacity to create large chemical spaces of valid and meaningful structures. (biomedcentral.com)
  • Physics-Informed Neural Networks (PINNs) for solving stochastic and fractional PDEs' Machine Learning for Multiscale Model Reduction Workshop, Harvard University, March 27-29, 2019, Cambridge, Massachusetts (Keynote). (tati.hu)
  • 2019 ). An admissible displacement-stress solution pair is obtained from a mixed form of physics-informed neural networks, and the proposed error bound is formulated as the constitutive relation error defined by the solution pair. (tati.hu)
  • Karniadakis, Physics -informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics, Volume 378, 2019. (tati.hu)
  • A.T.W. Wan Abdullah, Logic programming on a neural network, Int. J. Intell. (ijsmdo.org)
  • IEEE Transactions on Neural Networks 14(5): 1297-1307. (uni-bielefeld.de)
  • A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory", IEEE Transactions on Neural Networks , vol. 14, 2003, pp. 1297-1307. (uni-bielefeld.de)
  • Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. (nature.com)
  • Wermter S , Weber C , Duch W , Honkela T , Koprinkova-Hristova P , Magg S , Palm G , Villa AEP Artificial Neural Networks and Machine Learning - ICANN 2014. (amarsi-project.eu)
  • Artificial Neural Networks and Machine Learning - ICANN 2014. (amarsi-project.eu)
  • Artificial neural networks are inspired by the brain and their computations could be implemented in biological neurons. (columbia.edu)
  • Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. (wikipedia.org)
  • Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. (wikipedia.org)
  • Specifically, artificial neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog. (wikipedia.org)
  • Most modern deep learning models are based on multi-layered artificial neural networks such as convolutional neural networks and transformers, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep Boltzmann machines. (wikipedia.org)
  • H. Sak, A. Senior, F. Beaufays, Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling . (springer.com)
  • 6] T. N. Kipf and M. Welling, "Semi-supervised classification with graph convolutional networks," 5th Int. Conf. (fujipress.jp)
  • When graph edges are weighted by inverse distance (within a cutoff), graph convolutional networks can encode the physics of multiphase flow interacting with solid obstacles, even without incorporating other physics-informed quantities, e.g., the curvature of the interface. (aps.org)
  • Y. Liu and Y. F. B. Wu, "Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks," in 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, 2018, pp. 354-361. (ijais.org)
  • 7] Y. Li, R. Yu, C. Shahabi, and Y. Liu, "Diffusion convolutional recurrent neural network: Data-driven traffic forecasting," 6th Int. Conf. (fujipress.jp)
  • Spectral Networks and Locally Connected Networks on Graphs," Int. Conf. (fujipress.jp)
  • Deep voice: Real-time neural text-to-speech," 34th Int. Conf. (ijcjournal.org)
  • In this paper, we propose a novel document-level deep learning method, called recurrent piecewise convolutional neural networks (RPCNN), for CID extraction. (biomedcentral.com)
  • J.H. Park, Y.S. Kim, I.K. Eom, K.Y. Lee, Economic load dispatch for piecewise quadratic cost function using Hopfield neural network, IEEE Trans. (ijsmdo.org)
  • In particular, the problem of null-controllability for systems with saturations (of a "neural network" type) is mentioned, as well as problems regarding piecewise linear (hybrid) systems. (sontaglab.org)
  • The number of neurons in the neural network is equal to that of decision variables in the linear programming problem. (mit.edu)
  • The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. (nih.gov)
  • Interestingly, some of these same correlations are maintained by distinct mechanisms in PD, suggesting that these motor networks use distinct feedback mechanisms to coordinate the same mRNA relationships across neuron types.NEW & NOTEWORTHY Neurons use various feedback mechanisms to adjust and maintain their output. (bvsalud.org)
  • Here, we demonstrate that different neurons within the same network can use distinct signaling mechanisms to regulate the same ion channel mRNA relationships. (bvsalud.org)
  • Such models however lack some fundamental biological constraints, and in particular represent individual neurons in terms of abstract units that communicate through continuous firing rates rather than discrete action potentials. (bvsalud.org)
  • Here we examine how far the theoretical insights obtained from low-rank rate networks transfer to more biologically plausible networks of spiking neurons. (bvsalud.org)
  • We studied the effect of synaptic inputs of different amplitude and duration on neural oscillators by simulating synaptic conductance pulses in a bursting conductance-based pacemaker model and by injecting artificial synaptic conductance pulses into pyloric pacemaker neurons of the lobster stomatogastric ganglion using the dynamic clamp. (jneurosci.org)
  • We describe the full custom analog very large-scale integration (VLSI) realization of a small network of integrate-and-fire neurons connected by bistable deterministic plastic synapses which can implement the idea of stochastic learning. (uni-bielefeld.de)
  • Our results demonstrate the wide range of neural activity patterns and behavior that can be modeled, and suggest a unified setting in which diverse cognitive computations and mechanisms can be studied. (nih.gov)
  • Understanding the neural mechanisms of invariant object recognition remains one of the major unsolved problems in neuroscience. (zotero.org)
  • However, these studies lack the guidance of neural mechanisms of affective empathy. (frontiersin.org)
  • Second, in the absence of neurally mechanistic models of behavior, it remains challenging to infer neural mechanisms from behavioral results and generate testable neural circuit level predictions that can be validated or falsified using neurophysiological approaches. (biorxiv.org)
  • ChatGPT is a GPT language model that understands and responds to natural language created using a transformer, which is a new artificial neural network algorithm first introduced by Google in 2017. (anesth-pain-med.org)
  • N. Hamadneh, S. Sathasivam, S.L. Tilahun, O.H. Choon, Learning logic programming in radial basis function network via genetic algorithm, J. Appl. (ijsmdo.org)
  • The application of physics-informed neural networks to hydrodynamic voltammetry H. Chen, E. Ktelhn and R. G. Compton, Analyst, 2022, 147, 1881 DOI: 10.1039/D2AN00456A This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. (tati.hu)
  • Our work provides insight into the dynamic process of narrative listening comprehension in late bilinguals and sheds new light on the neural representation of language processing and related disorders. (bvsalud.org)
  • Recently, van der Velde and de Kamps ( 2006 ) proposed a neural blackboard architecture, which they assert to have satisfied the variable representation needs that Marcus and Jackendoff identified. (mit.edu)
  • We propose a learning-based method based on feature representation learning and deep neural network named DTI-CNN to predict the drug-target interactions. (biomedcentral.com)
  • In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation. (wikipedia.org)
  • In Advances in Neural Information Processing Systems Vol. 25 (eds Pereira, F. et al. (nature.com)
  • In Advances in Neural Information Processing Systems Vol. 27 (eds Ghahramani, Z. et al. (nature.com)
  • Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. (eneuro.org)
  • Recent advances in neural network modelling have enabled major strides in computer vision and other artificial intelligence applications. (columbia.edu)
  • We review two examples where the linear response of a neuronal network submitted to an external stimulus can be derived explicitly, including network parameters dependence. (aip.org)
  • A neuronal avalanche is a cascade of bursts of activity in neuronal networks whose size distribution can be approximated by a power law , as in critical sandpile models (Bak et al. (scholarpedia.org)
  • 2. Priesemann V, Valderrama M, Wibral M, Le Van Quyen M: Neuronal hypothesis is that neural networks assume a critical state Avalanches Differ from Wakefulness to Deep Sleep-Evidence from [1,2], because in models criticality maximizes information Intracranial Depth Recordings in Humans. (sagepub.com)
  • Zucker and Regehr, 2002 ), there are fewer direct assessments of the functional significance of these changes for neuronal or network dynamics. (jneurosci.org)
  • neural noise within pattern generating circuits is widely assumed to be the primary source of such variability, and statistical models that incorporate neural noise are successful at reproducing the full variation present in natural songs. (zotero.org)
  • Recurrent network models are instrumental in investigating how behaviorally-relevant computations emerge from collective neural dynamics. (bvsalud.org)
  • The use of deep neural network models to predict the properties of these molecules enabled more versatile and efficient molecular evaluations to be conducted by using the proposed method repeatedly. (nature.com)
  • Here, I have used brain-tissue mapped artificial neural network (ANN) models of primate vision to probe candidate neural and behavior markers of atypical facial emotion recognition in IwA at an image-by-image level. (biorxiv.org)
  • In sum, these results identify primate IT activity as a candidate neural marker and demonstrate how ANN models of vision can be used to generate neural circuit-level hypotheses and guide future human and non-human primate studies in autism. (biorxiv.org)
  • Recent work by H.T. Siegelmann and E.D. Sontag (1992) has demonstrated that polynomial time on linear saturated recurrent neural networks equals polynomial time on standard computational models: Turing machines if the weights of the net are rationals, and nonuniform circuits if the weights are real. (sontaglab.org)
  • We are entering an exciting new era, in which we will be able to build neurobiologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence. (columbia.edu)
  • We take a top-down approach to modelling, starting with models that perform the task, but abstract from much of the biological detail. (columbia.edu)
  • The lab develops neural net models, statistical inference and visualisation techniques, and visual stimuli and tasks, and measures brain activity with fMRI and MEG in humans and with array recordings in nonhuman primates. (columbia.edu)
  • This study aims to develop and validate interpretable recurrent neural network (RNN) models for dynamically predicting EF risk. (biomedcentral.com)
  • What matters in a transferable neural network model for relation classification in the biomedical domain? (crossref.org)
  • 3] Z. Yan and Y. Wu, "A Neural N-Gram Network for Text Classification," J. Adv. Comput. (fujipress.jp)
  • G. Pinkas, Symmetric neural networks and propositional logic SAT, Neural Comput. (ijsmdo.org)
  • Vision is of interest in its own right, but also provides a model for understanding, more generally, how the brain computes and how it might perform probabilistic inference through parallel and recurrent computations. (columbia.edu)
  • 25] Hornik K., Approximation capabilities of multilayer feedforward networks, Neural Netw. (tati.hu)
  • 17 ] built multilayer perceptron (MLP) neural network model for predicting the outcome of extubation among patients in ICU, and showed that MLP outperformed conventional predictors including RSBI, maximum inspiratory and expiratory pressure. (biomedcentral.com)
  • Enhanced Network Anomaly Detection Based on Deep Neural Networks. (edu.ua)
  • In this paper, we use the advancement of deep neural networks to predict whether a sentence contains a hate speech and abusive tone. (ugm.ac.id)
  • 6] H. Palangi, L. Deng, Y. Shen et al, "Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval," IEEE/ACM Trans. (fujipress.jp)
  • 13] E. Hoffer and N. Ailon, "Deep metric learning using triplet network," Int. Workshop on Similarity-Based Pattern Recognition (SIMBAD 2015), pp. 84-92, 2015. (fujipress.jp)
  • M. Raissi, P. Perdikaris, G.E. Origen.AI, an artificial intelligence startup focusing on the energy sector, is looking for one or several (senior) deep learning researchers specializing in physics-informed neural networks (PINNs) to join our team. (tati.hu)
  • Here, we propose a new deep learning method---physics-informed neural networks with hard constraints (hPINNs)---for solving topology optimization. (tati.hu)
  • 10] J. Zhang, Y. Zheng, J. Sun, and D. Qi, "Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning," IEEE Trans. (fujipress.jp)
  • H. Hejazi and K. Shaalan, "Deep Learning for Arabic Image Captioning: A Comparative Study of Main Factors and Preprocessing Recommendations," Int. J. Adv. Comput. (umm.ac.id)
  • In this paper, we implemented a novel deep neural network model, DeepRKE, which combines primary RNA sequence and secondary structure information to effectively predict RBP binding sites. (biomedcentral.com)
  • Using graph convolutions with skip connections makes the use of very deep networks unnecessary. (aps.org)
  • The performance of these optimized networks is compared to deep, densely connected neural networks and deep graph networks. (aps.org)
  • The adjective "deep" in deep learning refers to the use of multiple layers in the network. (wikipedia.org)
  • Examples of deep structures that can be trained in an unsupervised manner are deep belief networks. (wikipedia.org)
  • J. 12 , 1-24 (1972)] and a conductance based Integrate and Fire model [M. Rudolph and A. Destexhe, Neural Comput. (aip.org)
  • We propose a new computational model for recurrent contour processing in which normalized activities of orientation selective contrast cells are fed forward to the next processing stage. (zotero.org)
  • We present a recurrent neural network for feature binding and sensory segmentation: the competitive -layer model (CLM). (lookformedical.com)
  • A directed network model is used to represent the influence relations between artists as nodes and edges. (mdpi.com)
  • Inspired by this neural mechanism, we constructed a brain-inspired affective empathy computational model, this model contains two submodels: (1) We designed an Artificial Pain Model inspired by the Free Energy Principle (FEP) to the simulate pain generation process in living organisms. (frontiersin.org)
  • Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. (nature.com)
  • This paper proposes a traffic flow prediction model (BAT-GCN) which is based on drivers' cognition of the road network. (fujipress.jp)
  • Finally, drivers obtain the probability distribution of different paths in the regional road network and build the prediction model by combining the spatiotemporal directed graph convolution neural network. (fujipress.jp)
  • Develop, as a proof of concept, a recurrent neural network model using electronic medical records data capable of continuously assessing an individual child's risk of mortality throughout their ICU stay as a proxy measure of severity of illness. (lww.com)
  • The recurrent neural network model can process hundreds of input variables contained in a patient's electronic medical record and integrate them dynamically as measurements become available. (lww.com)
  • Evaluation of Recurrent Neural Network Model Training for Health Care Suggestions, Smart Innov. (itm-conferences.org)
  • In this study, we used the Flickr8k dataset and the VGG16 Convolutional Neural Networks (CNN) model as an encoder to generate feature extraction from images. (umm.ac.id)
  • We first extract the relevant features of drugs and proteins from heterogeneous networks by using the Jaccard similarity coefficient and restart random walk model. (biomedcentral.com)
  • Third, based on the features obtained from last step, we constructed a convolutional neural network model to predict the interaction between drugs and proteins. (biomedcentral.com)
  • Graves, A. Generating sequences with recurrent neural networks. (nature.com)
  • U.P. Wen, K.M. Lan, H.S. Shih, A review of Hopfield neural networks for solving mathematical programming problems, Eur. (ijsmdo.org)
  • M.Q. Nguyen, P.M. Atkinson, H.G. Lewis, Superresolution mapping using a Hopfield neural network with fused images, IEEE Trans. (ijsmdo.org)
  • C.H. Fung, M.S. Wong, P.W. Chan, Spatio-temporal data fusion for satellite images using Hopfield neural network, Remote Sens. (ijsmdo.org)
  • A. Kzar, M. Jafri, K. Mutter, S. Syahreza, A modified Hopfield neural network algorithm (MHNNA) using ALOS image for water quality mapping, Int. J. Environ. (ijsmdo.org)
  • S. Salcedo-Sanz, R.R. Santiago-Mozos, C. Bousono-Calzon, A hybrid Hopfield network-simulated annealing approach for frequency assignment in satellite communications systems, IEEE Trans. (ijsmdo.org)
  • M. Velavan, Z.R. Yahya, M.N. Abdul Halif, S. Sathasivam, Mean-field theory in doing logic programming using a Hopfield network, Mod. (ijsmdo.org)
  • Continuous Prediction of Mortality in the PICU: A Recurrent. (lww.com)
  • First, network-based approaches have a good prediction performance even without the three-dimensional structure of the target. (biomedcentral.com)
  • Wang et.al used a heterogeneous network data to obtain the diffusion feature and directly use the obtained diffusion distributions to derive the prediction scores of DTIs [ 3 ]. (biomedcentral.com)
  • Recurrent Neural Network (RNN) uses the Bidirectional Long-Short Term Memory (BiLSTM) method as a decoder. (umm.ac.id)
  • The distributed representations are taken as input of convolutional neural networks (CNN) and bidirectional long-term short-term memory networks (BiLSTM) to identify RBP binding sites. (biomedcentral.com)
  • Its application to linear assignment is discussed to demonstrate the utility of the neural network. (mit.edu)
  • Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory. (nature.com)
  • Here, we demonstrate that homeostatic plasticity [9] assures that networks assume a slightly sub-critical state, independently of the initial configuration. (sagepub.com)
  • Improving the performance of neural decoding algorithms allows neuroscientists to better understand the information contained in a neural population and can help to advance engineering applications such as brain-machine interfaces. (eneuro.org)
  • IEEE Transactions on Network and Service Management. (edu.ua)
  • Bhuyan M.H., Bhattacharyya D.K., Kalita J.K. Network Anomaly Detection: Methods, Systems and Tools. (edu.ua)
  • Molina-Coronado B., Mori U., Mendiburu A., Miguel-Alonso J. Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process. (edu.ua)
  • Moreover, it is argued here that these newly proposed variants present a severe challenge not only for eliminative connectionism but for all network training methods that require iterative tuning of synaptic strengths. (mit.edu)
  • We provide descriptions, best practices, and code for applying common machine learning methods, including neural networks and gradient boosting. (eneuro.org)
  • Modern methods, particularly neural networks and ensembles, significantly outperform traditional approaches, such as Wiener and Kalman filters. (eneuro.org)
  • The straight line is indicative of a power law, suggesting that the network is operating near the critical point (unpublished data recorded by W. Chen, C. Haldeman, S. Wang, A. Tang, J.M. Beggs). (scholarpedia.org)
  • Physics-Informed Neural Network (PINN) presents a unified framework to solve partial differential equations (PDEs) and to perform identification (inversion) (Raissi et al. (tati.hu)
  • This paper introduces for the first time, to our knowledge, a framework for physics-informed neural networks in power system applications. (tati.hu)