1993 neural network recurrent neural. Medical search. Frequent questions

LSTM combined with convolutional neural networks (CNNs) improved automatic image captioning. (wikipedia.org)
Bearing fault detection by one-dimensional convolutional neural networks. (sbc.org.br)
10] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks [J]. Advances in neural information processing systems, 2012, 25. (clausiuspress.com)
16] WANG J, GAO F, DONG J. Change detection from SAR images based on deformable residual convolutional neural networks[C]. Proceedings of the 2nd ACM International Conference on Multimedia in Asia. (clausiuspress.com)

Fully recurrent neural networks (FRNN) connect the outputs of all neurons to the inputs of all neurons. (wikipedia.org)
This is the most general neural network topology because all other topologies can be represented by setting some connection weights to zero to simulate the lack of connections between those neurons. (wikipedia.org)
Dynamics of a recurrent network of spiking neurons before and following learning. (billhowell.ca)
In 1951, Marvin Minsky, a graduate student inspired by earlier neuroscience research indicating that the brain was composed of an electrical network of neurons firing with all-or-nothing pulses, attempted to computationally model the behavior of a rat. (kdnuggets.com)
An excitatory pulse-coupled neural network is a network composed of neurons coupled via excitatory synapses, where the coupling among the neurons is mediated by the transmission of Excitatory Post-Synaptic Potentials (EPSPs). (scholarpedia.org)
2008). Of particular interest are the so-called Giant Depolarizing Potentials (GDPs), recurrent oscillations which repeatedly synchronizes a relatively small assembly of neurons and whose degree of synchrony is orchestrate by hub neurons (Bonifazi et al. (scholarpedia.org)
On the other hand, numerical and analytical studies of collective motions in networks made of simple spiking neurons have been mainly devoted to balanced excitatory-inhibitory configurations (Brunel, 2000), while few studies focused on the emergence of coherent activity in purely excitatory networks. (scholarpedia.org)
1995). Van Vreeswijk in 1996 has extended these analysis to globally (or fully) coupled excitatory networks of Leaky Integrate-and-Fire (LIF) neurons, where each neuron is connected to all the others. (scholarpedia.org)
However, while for massively connected networks, composed by a large number of neurons, the dynamics of the collective state (apart some trivial rescaling) essentially coincide with that observed in the fully coupled system (Olmi et al. (scholarpedia.org)
2012). This is due to the fact that, for sufficiently large networks, the synaptic currents, driving the dynamics of the single neurons, become essentially identical for massively connected networks, while the differences among them do not vanish for sparse networks. (scholarpedia.org)
However, this chaos is weak in the massively connected networks, vanishing for sufficiently large system sizes, while sparse networks remain chaotic for any large number of neurons and the chaotic dynamics is extensive. (scholarpedia.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)
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)
Poon, C.-S. & Zhou, K. Neuromorphic silicon neurons and large-scale neural networks: challenges and opportunities. (nature.com)
Information transmission in neural networks is often described in terms of the rate at which neurons emit action potentials. (frontiersin.org)
Our results demonstrate that cortical neurons can be conceptualized as multi-layered "deep" processing units, implying that the cortical networks they form have a non-classical architecture and are potentially more computationally powerful than previously assumed. (biorxiv.org)

Neural Computation, 5(1):140-153. (billhowell.ca)
Neural Computation, 10(2):251-276. (billhowell.ca)
Network: Computation in Neural Systems, 8(4):373-404. (billhowell.ca)
Neural Computation, 8(3):643-674. (billhowell.ca)
Neural Computation, 4:559-572. (billhowell.ca)
Recently, the dynamical neural networks (DNNs), which are firstly introduced by Hopfield in [ 1 ], have been extensively studied due to its wide applications in various areas such as associative memory, parallel computation, signal processing, optimization, and moving object speed detection. (hindawi.com)
For designing and understanding computation with spiking neural networks, our approach is based on phasor networks. (rctn.org)
Neural Computation, 30(6), 1449-1513. (rctn.org)

IEEE Transactions on Neural Networks, 11(3):697-709. (billhowell.ca)
IEEE Transactions on Neural Networks 14(5): 1297-1307. (uni-bielefeld.de)
Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 57(2): 924-935. (clausiuspress.com)

In contrast to the uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. (wikipedia.org)
A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural network, while an infinite impulse recurrent network is a directed cyclic graph that can not be unrolled. (wikipedia.org)
This is also called Feedforward Neural Network (FNN). (wikipedia.org)

Long short-term memory (LSTM) networks were invented by Hochreiter and Schmidhuber in 1997 and set accuracy records in multiple applications domains. (wikipedia.org)
Neural Comput 1997;9(8):1735-80. (springer.com)

Elman and Jordan networks are also known as "Simple recurrent networks" (SRN). (wikipedia.org)
The model, based on a simple recurrent network (SRN), is able to predict perfectly the successive elements of sequences generated from finite-state, grammars. (mit.edu)

Analisa kenaikan muka air laut di perairan Indonesia menggunakan data altimetri Topex/Poseidon dan Jason Series tahun 1993-2018, Tugas Akhir. (tci-thaijo.org)
Our most recent theory work is aiming to uncover algebraic structure in the dynamics of recurrent neural networks, which first appears messy, unreliable and unstructured, for example, in echo-state networks (Frady, Kleyko and Sommer, 2018) and in spiking neural networks (Frady & Sommer, 2019). (rctn.org)

Wang Y, Shen H, Liu S, Gao J, Cheng X. Cascade dynamics modeling with attention-based recurrent neural network. (springer.com)
The article focuses on the influence of dilution on the collective dynamics of these networks: a diluted network is a network where connections have been randomly pruned. (scholarpedia.org)
2009). These experimental results suggest that the macroscopic dynamics of excitatory networks can reveal unexpected behaviors. (scholarpedia.org)
Sparse and massively connected networks reveal even more striking differences at the microscopic level associated to the membrane potentials' dynamics. (scholarpedia.org)

In Advances in Neural Information Processing Systems (NIPS), pages 3084-3092. (billhowell.ca)
In Advances in neural information processing systems 12 (NIPS), pages 968-974. (billhowell.ca)
In Advances in neural information processing systems, pages 513-520. (sbc.org.br)

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)

To analyze the road crash data of Milan City, Italy, gathered between 2014-2017, this study used artificial neural networks (ANNs), generalized linear mixed-effects (GLME), multinomial regression (MNR), and general nonlinear regression (NLM), as the modelling tools. (mdpi.com)

Generative adversarial network-based glottal waveform model for statistical parametric speech synthesis. (crossref.org)
Such networks resemble statistical models of magnetic systems ("spin glasses"), with an atomic spin state (up or down) seen as analogous to the "firing" state of a neuron (on or off). (lu.se)

In 2009, a Connectionist Temporal Classification (CTC)-trained LSTM network was the first RNN to win pattern recognition contests when it won several competitions in connected handwriting recognition. (wikipedia.org)
Li Y, Yang L, Xu B, Wang J, Lin H. Improving user attribute classification with text and social network attention. (springer.com)
Classification was done by using the Recurrent neural Network. (imanagerpublications.com)

Thus the network can maintain a sort of state, allowing it to perform such tasks as sequence-prediction that are beyond the power of a standard multilayer perceptron. (wikipedia.org)
Topological recurrent neural network for diffusion prediction. (springer.com)
To reduce the complexity of workload management, his paper proposes an elaborate cost prediction model based on recurrent neural network through learning from operator behavior and detailed runtime information. (jos.org.cn)
uCloudlink Group Inc. American Depositary Shares prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Chi-Square 1,2,3,4 and it is concluded that the UCL stock is predictable in the short/long term. (ademcetinkaya.com)

In Network , 12 (1): R1-47, 2001. (panmental.de)

SAR Images Change Detection Based on Generative Adversarial Network and Non-Local Neural Network [D]. Xi'an: XIDIAN UNIVERSITY, 2019. (clausiuspress.com)

This was also called the Hopfield network (1982). (wikipedia.org)
For instance, Ahn incorporated robust training law in switched Hopfield neural networks with external disturbances to study boundedness and exponentially stability [ 12 ], and studied passivity in [ 13 ]. (hindawi.com)
Also, in [ 19 ] a new sufficient condition is derived to guarantee ISS of Takagi-Sugeno fuzzy Hopfield neural networks with time delay. (hindawi.com)

Identification of linear and nonlinear dynamic systems using recurrent neural networks. (auth.gr)
By employing the Lyapunov-Krasovskii functional method and linear matrix inequalities (LMIs) technique, some new sufficient conditions ensuring the input-to-state stability (ISS) property of the nonlinear network systems are obtained. (hindawi.com)
In this paper, to cope with severe ISI and nonlinear distortions a neural decision feedback equalizer (NDFE) is applied to the digital magnetic recording channel-partial erasure model. (yonsei.ac.kr)
We trained deep neural networks (DNNs) to mimic the I/O behavior of a detailed nonlinear model of a layer 5 cortical pyramidal cell, receiving rich spatio-temporal patterns of input synapse activations. (biorxiv.org)

Modeling the intensity function of point process via recurrent neural networks. (springer.com)

New results on recurrent network training: unifying the algorithms and accelerating convergence. (billhowell.ca)
However, conventional Artificial Neural Networks (ANNs) and machine learning algorithms cannot take advantage of this coding strategy, due to their rate-based representation of signals. (frontiersin.org)

Recently, Li (Bioinformatics 35:4408-4410, 2019) developed a novel software tool dna-brnn to annotate repetitive sequences using a recurrent neural network trained on sample annotations of repetitive elements. (biomedcentral.com)
This combines the basic concepts of Li (Bioinformatics 35:4408-4410, 2019) with current techniques developed for neural machine translation, the attention mechanism, for the task of nucleotide-level annotation of repetitive elements. (biomedcentral.com)
Analisis dinamika permukaan laut di Laut Jawa dengan Recurrent Neural Network periode 1993 sampai 2019.Indonesian Journal of Oceanography, 3(1),100-110. (tci-thaijo.org)

The term "recurrent neural network" is used to refer to the class of networks with an infinite impulse response, whereas "convolutional neural network" refers to the class of finite impulse response. (wikipedia.org)
Additional stored states and the storage under direct control by the network can be added to both infinite-impulse and finite-impulse networks. (wikipedia.org)
As a matter of fact, for finite networks chaotic evolution has been observed in both cases. (scholarpedia.org)

However, these studies lack the guidance of neural mechanisms of affective empathy. (frontiersin.org)
Sanchez-Vives MV, McCormick DA (2000) Cellular and network mechanisms of rhythmic recurrent activity in neocortex. (yale.edu)

This classroom-tested advanced undergraduate and graduate textbook, first published in 2000, provides a rigorous treatment of recently developed non-linear models, including regime-switching and artificial neural networks. (repec.org)

Neural networks: a comprehensive foundation by simon haykin, macmillan, 1994, isbn 0-02-352781-7. (sbc.org.br)

Ma Y, Peng H, Khan T, Cambria E, Hussain A. Sentic LSTM: a hybrid network for targeted Aspect-Based sentiment analysis. (springer.com)
In particular, the model uses a special kind of recurrent neural network, called long-short term memory (LSTM). (jos.org.cn)

We check by a suitable stimulation protocol that the stochastic synaptic plasticity produces the expected pattern of potentiation and depression in, the electronic network. (uni-bielefeld.de)
Mel BW (1993) Synaptic integration in an excitable dendritic tree. (yale.edu)

Both classes of networks exhibit temporal dynamic behavior. (wikipedia.org)
The model makes use of a popular class of networks pioneered by Jordan and Elman, but unlike most of the work with these networks, Cleereman's work fits the networks' behavior to a great deal of experimental data. (mit.edu)

In IEEE 1st International Conference on Neural Networks, San Diego, volume 2, pages 609-618. (billhowell.ca)

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. (wikipedia.org)
V. Pacelli, V. Bevilacqua and M. Azzollini, "An Artificial Neural Network Model to Forecast Exchange Rates," Journal of Intelligent Learning Systems and Applications , Vol. 3 No. 2, 2011, pp. 57-69. (scirp.org)
A modular neural network (MNN) is a type of artificial neural network that can be used for news feed sentiment analysis. (ademcetinkaya.com)
Artificial neural network (ANN) methods in general fall within this category, and par- ticularly interesting in the context of optimization are recurrent network methods based on deterministic annealing. (lu.se)

dblp: Deep sentiments in Roman Urdu text using Recurrent Convolutional Neural Network model. (dblp.org)

In STOC '93: Proceedings of the twenty-fifth annual ACM symposium on Theory of computing , New York, NY, USA, pages 325-334, 1993. (sontaglab.org)

Even in the case of artificial Spiking Neural Networks (SNNs), identifying applications where temporal coding outperforms the rate coding strategies of ANNs is still an open challenge. (frontiersin.org)

Optimization of the neural network is done using the Cuckoo Search. (imanagerpublications.com)
[12] To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques -- including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics , probability and economics . (wikipredia.net)

[TR1-6] In 1993, however, to combine the best of both recurrent NNs (RNNs) and fast weights, I collapsed all of this into a single RNN that could rapidly reprogram all of its own fast weights through additive outer product-based weight changes . (idsia.ch)

Patients with multiple sclerosis are classified according to their clinical phenotype, with ~85% following a relapsing-remitting course (relapsing-remitting multiple sclerosis) characterized by recurrent, acute neurological deficits punctuating periods of latency or remission (Lublin and Reingold, 1996). (medscape.com)

Recurrent neural networks are theoretically Turing complete and can run arbitrary programs to process arbitrary sequences of inputs. (wikipedia.org)

26 March 1991: Neural nets learn to program neural nets with fast weights-the first Transformer variants. (idsia.ch)
The slow pace of global vaccination and the rapid emergence of SARS-CoV-2 variants suggest recurrent waves of COVID-19 in coming years. (bvsalud.org)

For the purposes of this research, the optimal MLP neural network topology has been designed and tested by means the specific genetic algorithm multi-objective Pareto-Based. (scirp.org)

Adaptive dropout for training deep neural networks. (billhowell.ca)
This work concerns the study of problems relating to the adaptive internal model control of DC motor in both cases conventional and neural. (scirp.org)
To overcome these limitations, we chose the architectures of neural networks deduced from the conventional models and the Levenberg-marquardt during the adjustment of system parameters of the adaptive neural internal model control. (scirp.org)
F. Zouari, K. Ben Saad and M. Benrejeb, "Adaptive Internal Model Control of a DC Motor Drive System Using Dynamic Neural Network," Journal of Software Engineering and Applications , Vol. 5 No. 3, 2012, pp. 168-189. (scirp.org)

The second reason is that it is an excellent example of using a connectionist or neural network model as theory in cognitive science. (mit.edu)
http://dx.doi.org/10.1137/S0363012901384302 ] Keyword(s): machine learning, theory of computing and complexity, VC dimension, neural networks. (sontaglab.org)
http://doi.acm.org/10.1145/167088.167192 ] Keyword(s): machine learning, neural networks, theory of computing and complexity, real-analytic functions. (sontaglab.org)
E. P. Frady, D. Kleyko, F. T. Sommer: A theory of sequence indexing and working memory in recurrent neural networks. (rctn.org)
To appear in The Handbook of Brain Theory and Neural Networks, (2nd edition), M.A. Arbib (ed. (lu.se)

Thus, we suggest that migraine should be considered a brain disease and not simply a recurrent acute pain syndrome. (biomedcentral.com)

The recent recurrent temporal point process (RTPP) methods exploited recurrent neural network (RNN) to get rid of the parametric form assumption in the density functions of TPP. (springer.com)
Du N, Dai H, Trivedi R, Upadhyay U, Gomez-Rodriguez M, Song L. Recurrent marked temporal point processes: embedding event history to vector. (springer.com)

A class of dynamical neural network models with time-varying delays is considered. (hindawi.com)
Accordingly, dynamical behaviors of neural networks with time-varying delays have been discussed in the last decades (see, e.g., [ 3 , 8 - 11 ], etc. (hindawi.com)
By using Lyapunov-Krasovskii functional technique, ISS conditions for the considered dynamical neural networks are given in terms of LMIs, which can be easily calculated by certain standard numerical packages. (hindawi.com)
In Section 2 , our mathematical model of dynamical neural networks is presented and some preliminaries are given. (hindawi.com)
In Section 3 , the main results for both ISS and asymptotically stability of dynamical neural networks with time-varying delays are proposed. (hindawi.com)
Salazar-Ciudad, I., Newman, S. A. and Sole, R. V. Phenotypic and dynamical transitions in model genetic networks I. Emergence of patterns and genotype-phenotype relationships . (panmental.de)

More recently, a decreased functional connectivity was demonstrated within the fronto-parietal network (FPN) in patients with migraine without and with aura in the absence of clinically relevant executive deficits. (biomedcentral.com)

The illustration to the right may be misleading to many because practical neural network topologies are frequently organized in "layers" and the drawing gives that appearance. (wikipedia.org)
In the recent years, following the seminal study by van Vreeswijk, the robustness of the partially synchronized regime has been examined by considering the influence of external noise and the level of dilution in networks of different topologies. (scholarpedia.org)

Evaluation of secondary structure of proteins from UV circular dichroism spectra using an unsupervised learning neural network. (billhowell.ca)

Poirazi P, Brannon T, Mel BW (2003) Pyramidal neuron as two-layer neural network. (yale.edu)
Delays are generally varied with time because information transmission from one neuron to another neuron may make the response of networks with time-varying delays. (hindawi.com)

Computational Capabilities of Graph Neural Networks. (vldb.org)
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)
Artificial neural networks are computational network models inspired by signal processing in the brain. (nature.com)
Significant effort has been made towards developing electronic architectures tuned to implement artificial neural networks that exhibit improved computational speed and accuracy. (nature.com)
Here, we propose a new architecture for a fully optical neural network that, in principle, could offer an enhancement in computational speed and power efficiency over state-of-the-art electronics for conventional inference tasks. (nature.com)

The Graph Neural Network Model. (vldb.org)
Convolutional Neural Network to Model Articulation Impairments in Patients with Parkinson’s Disease. (crossref.org)
Joachimczak, M. and Wrobel, B. Evo-devo in silico: a model of a gene network regulating multicellular development in 3D space with artificial physics. (panmental.de)

An Elman network is a three-layer network (arranged horizontally as x, y, and z in the illustration) with the addition of a set of context units (u in the illustration). (wikipedia.org)
Jordan networks are similar to Elman networks. (wikipedia.org)

Event sequences with marker and timing information are available in a wide range of domains, from machine log in automatic train supervision systems to information cascades in social networks. (springer.com)
Given the historical event sequences, predicting what event will happen next and when it will happen can benefit many useful applications, such as maintenance service schedule for mass rapid transit trains and product advertising in social networks. (springer.com)

Gabriele Monfardini , Vincenzo Di Massa , Franco Scarselli , Marco Gori: Graph Neural Networks for Object Localization. (vldb.org)
Lagrange programming neural network approaches for robust time-of-arrival localization. (springer.com)

A Neural Network Approach to Web Graph Processing. (vldb.org)
This approach potentially opens up radically new avenues for understanding neural recording data, and for neural engineering. (rctn.org)

Here, further connections between the languages recognized by such neural nets and other complexity classes are developed. (sontaglab.org)

A Deep Learning Method for Pathological Voice Detection Using Convolutional Deep Belief Networks. (crossref.org)

Neural networks continuously analyze real-time OTC data. (ademcetinkaya.com)

In 1993, a neural history compressor system solved a "Very Deep Learning" task that required more than 1000 subsequent layers in an RNN unfolded in time. (wikipedia.org)
Mastering the game of go with deep neural networks and tree search. (nature.com)

Trigeminal neuralgia (TN), also known as tic douloureux, is a distinctive facial pain syndrome that may become recurrent and chronic. (medscape.com)

The context units in a Jordan network are also referred to as the state layer. (wikipedia.org)
[FWP0-2] Their context-dependent attention weights used for weighted averages of value vectors are like NN-programmed fast weights that change for each new input (compare Sec. 2 and Sec. 4 on attention terminology since 1993). (idsia.ch)

Kalman filters and neural-network schemes for sensor validation in flight control systems. (sbc.org.br)
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)

Anatomy13

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Analytical, Diagnostic and Therapeutic Techniques and Equipment33

Psychiatry and Psychology8

Phenomena and Processes22

Disciplines and Occupations3

Anthropology, Education, Sociology and Social Phenomena1

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Convolutional neural networks4

Neurons16

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IEEE Transactions3

Feedforward Neural3

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Simple recurrent2

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Dynamics4

Advances3

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Statistical2

Classification3

Prediction4

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Generative adversarial network1

Hopfield3

Nonlinear4

Modeling1

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Finite3

Mechanisms2

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LSTM2

Synaptic2

Behavior2

Diego1

Artificial neural4

Dblp1

Proceedings1

ANNs1

Optimization2

RNNs1

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Turing1

Variants2

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Adaptive4

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Acute1

Temporal2

Dynamical6

Deficits1

Topologies2

Spectra1

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Computational5

Model3

Elman2

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Deep2

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Context2

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