Class of neural networks neural comput abstract recurrent neural. Medical search. Frequent questions

The algorithm extracts local features through a convolutional neural network and then extracts temporal features through bi-directional long short-term memory. (frontiersin.org)
We propose various convolutional neural network (CNN) based models such as CNN, single exponential smoothing CNN (S-CNN), moving average CNN (MA-CNN), smoothed moving average CNN (SMA-CNN), and moving average smoothed CNN (MAS-CNN). (ijain.org)
The main difficulty of using a neural network for this problem is that a scatterer has a global impact on the scattered wave field, rendering a typical convolutional neural network with local connections inapplicable. (siam.org)

The critical state is assumed to be optimal for any computation in recurrent neural networks, because criticality maximizes a number of abstract computational properties. (nature.com)
Neural Computation (2009) 21 (2): 510-532. (mit.edu)

Zhao, W., Global exponential stability analysis of Cohen-Grossberg neural network with delays, Commun. (zbmath.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)

Zhang, H., Globally exponential stability of neural network with constant and variable delays, Phys. (zbmath.org)

Recurrent network models are instrumental in investigating how behaviorally-relevant computations emerge from collective neural dynamics. (bvsalud.org)
A recently developed class of models based on low-rank connectivity provides an analytically tractable framework for understanding of how connectivity structure determines the geometry of low-dimensional dynamics and the ensuing computations. (bvsalud.org)
We challenge this assumption by evaluating the performance of a spiking recurrent neural network on a set of tasks of varying complexity at - and away from critical network dynamics. (nature.com)
FastICA was chosen as a method which tries to find new representations of data with minimal dependency between components without employing any kind of competition in the neural dynamics, but it enforces independent components via the learning rule. (frontiersin.org)
Zucker and Regehr, 2002 ), there are fewer direct assessments of the functional significance of these changes for neuronal or network dynamics. (jneurosci.org)
Zhao, W., Dynamics of Cohen-Grossberg neural network with variable coefficients and time-varying delays, Nonlinear Anal. (zbmath.org)

Comput Intell Neurosci. (nih.gov)

Similar experiments with some common network structures and other advanced electrocardiogram classification algorithms show that the proposed model performs favourably against other counterparts in F1 score. (frontiersin.org)
Experiments have proved that compared with other existing prediction algorithms, the method has higher accuracy in network security situation prediction. (springeropen.com)
Currently, many researchers use deep learning-related algorithms to study network security situation prediction. (springeropen.com)
Deep learning is a class of machine learning algorithms that: 199-200 uses multiple layers to progressively extract higher-level features from the raw input. (wikipedia.org)

Hani Bani-Salameh, Mohammed Sallam and Bashar Al shboul, "A Deep-Learning-Based Bug Priority Prediction Using RNN-LSTM Neural Networks", In e-Informatica Software Engineering Journal, vol. 15, no. 1, pp. 29-45, 2021. (ijritcc.org)

solving forward/inverse ordinary/partial differential equations (ODEs/PDEs) [ SIAM Rev.] Physics-informed neural networks are a popular approach, but here, the failures of such an approach are characterized and better solutions and training procedures are provided. (tati.hu)
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)
We introduce physics-informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. (tati.hu)
Physics-informed neural networks (PINNs) are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs). (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)
I. E. Lagaris, A. Likas, and D. I. Fotiadis, Artificial neural networks for solving ordinary and partial differential equations , IEEE Trans. (siam.org)

To this end, we propose a novel 3D-CNN (3D Convolutional Neural Networks) model, which extends the idea of multi-scale feature fusion to the spatio-temporal domain, and enhances the feature extraction ability of the network by combining feature maps of different convolutional layers. (mdpi.com)
In this paper, we propose a novel document-level deep learning method, called recurrent piecewise convolutional neural networks (RPCNN), for CID extraction. (biomedcentral.com)
We propose a novel neural network architecture, SwitchNet, for solving wave equation based inverse scattering problems via providing maps between the scatterers and the scattered field (and vice versa). (siam.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)

Border ownership is signaled by a class of V2 neurons, even though its value depends on information coming from well outside their classical receptive fields. (zotero.org)
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)
The locality of the learning rules is key for biological and artificial networks where global information (e.g., task-performance error or activity of distant neurons) may be unavailable or costly to distribute. (nature.com)
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)
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)
Cell assemblies and central pattern generators (CPGs) are related types of neuronal networks: both consist of interacting groups of neurons whose collective activities lead to defined functional outputs. (degruyter.com)

25] Hornik K., Approximation capabilities of multilayer feedforward networks, Neural Netw. (tati.hu)
For a feedforward neural network, the depth of the CAPs is that of the network and is the number of hidden layers plus one (as the output layer is also parameterized). (wikipedia.org)

Deep voice: Real-time neural text-to-speech," 34th Int. Conf. (ijcjournal.org)

IEEE Transactions on Neural Networks 14(5): 1297-1307. (uni-bielefeld.de)

A central challenge in the design of an artificial network is to initialize it such that it quickly reaches optimal performance for a given task. (nature.com)
Such learning could strongly speed up convergence, and enables a preshaping of the artificial network-akin to the shaping of biological networks during development by spontaneous activity. (nature.com)

H. Sak, A. Senior, F. Beaufays, Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling . (springer.com)

Here, we propose a new deep learning method---physics-informed neural networks with hard constraints (hPINNs)---for solving topology optimization. (tati.hu)
Optimization of the neural network is done using the Cuckoo Search. (imanagerpublications.com)
The results show that the radial basis neural network optimized by particle swarm optimization algorithm had better performance, but the number of data samples used in the experiment was too small. (springeropen.com)

A Dual-Attention Recurrent Neural Network Method for Deep Cone Thickener Underflow Concentration Prediction. (nih.gov)
Continuous Prediction of Mortality in the PICU: A Recurrent. (lww.com)
In order to realize and maintain the security and privacy of smart cities, we intend to use the technology of network security situation prediction. (springeropen.com)
The first is the extraction of network security situation elements, the second is the evaluation of the network security situation, and the third is the prediction of the network security situation. (springeropen.com)
The main work of this paper is network security situation prediction. (springeropen.com)
There are many researches on network security situation prediction. (springeropen.com)
4 ] compared several network security situation prediction methods. (springeropen.com)

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)

In order to reduce the computational complexity of the network, we further improved the multi-fiber network, and finally established an architecture-3D convolution Two-Stream model based on multi-scale feature fusion. (mdpi.com)

Spike-based neuromorphic hardware holds promise for more energy-efficient implementations of deep neural networks (DNNs) than standard hardware such as GPUs. (nature.com)
Furthermore, it provides the basis for an energy-efficient implementation of an important class of large DNNs that extract relations between words and sentences in order to answer questions about the text. (nature.com)

To that end, we developed a plastic spiking network on a neuromorphic chip. (nature.com)

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)
Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. (wikipedia.org)
In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation. (wikipedia.org)

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)
The approach utilized at TOPO can best be summarized in two steps, the first being a coordinate regression by means of a deep neural network followed by the extraction of the pose by a PnP solver (Figure 1). (epfl.ch)
The adjective "deep" in deep learning refers to the use of multiple layers in the network. (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)
Examples of deep structures that can be trained in an unsupervised manner are deep belief networks. (wikipedia.org)

Classification was done by using the Recurrent neural Network. (imanagerpublications.com)

Understanding the neural mechanisms of invariant object recognition remains one of the major unsolved problems in neuroscience. (zotero.org)
Therefore, we compared four methods employing different competition mechanisms, namely, independent component analysis, non-negative matrix factorization with sparseness constraint, predictive coding/biased competition, and a Hebbian neural network with lateral inhibitory connections. (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)

Huang, X., LMI-based criteria for global robust stability of bidirectional associative memory networks with time delay, Nonlinear Anal. (zbmath.org)
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)

While [ 11 ] mainly describes structural properties of two of the four repetitive element classes, we additionally highlight the biological importance and, if possible, function of specific repetitive elements. (biomedcentral.com)
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)

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)

Thereby, we challenge the general assumption that criticality would be beneficial for any task, and provide instead an understanding of how the collective network state should be tuned to task requirement. (nature.com)

Connections to space-bounded classes, simulation of parallel computational models such as Vector Machines, and a discussion of the characterizations of various nonuniform classes in terms of Kolmogorov complexity are presented. (sontaglab.org)

New results of global robust exponential stability of neural networks with delays. (zbmath.org)
By homeomorphism techniques and Lyapunov functions, sufficient conditions for the existence, uniqueness and global rust exponential stability of interval neural networks with delays are presented. (zbmath.org)
Tan, Y., Harmless delays for global exponential stability of Cohen-Grossberg neural networks, Math. (zbmath.org)
Zhou, Q., Global exponential stability of BAM neural networks with distributed delays and impulses, Nonlinear Anal. (zbmath.org)

http://dx.doi.org/10.1137/S0363012901384302 ] Keyword(s): machine learning, theory of computing and complexity, VC dimension, neural networks. (sontaglab.org)
Here, further connections between the languages recognized by such neural nets and other complexity classes are developed. (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)

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)
J. 12 , 1-24 (1972)] and a conductance based Integrate and Fire model [M. Rudolph and A. Destexhe, Neural Comput. (aip.org)
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)
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)
We present a recurrent neural network for feature binding and sensory segmentation: the competitive -layer model (CLM). (lookformedical.com)
Firstly, according to the fact that the intrusion activity is a time series event, recurrent neural network (RNN) or RNN variant is used to stack the model. (springeropen.com)

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)

5 ] used manually-constructed graphical patterns derived from syntactic parse trees to extract causal relations between drugs and adverse events in MEDLINE abstracts. (biomedcentral.com)

Recently, it has been shown that specific local-learning rules can even be harnessed more flexibly: a theoretical study suggests that recurrent networks with local, homeostatic learning rules can be tuned toward and away from criticality by simply adjusting the input strength 17 . (nature.com)

It is Machine learning which predict the future nature of education environment by adapting new advanced intelligent technologies. (springer.com)
This work explores the application of Machine Learning in teaching and learning for further improvement in the learning environment in higher education. (springer.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)

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)
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)
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)
Bayesian segmentation models provide additional advantages, drawing on empirical information as to the location of tissue classes in stereotaxic space (4) . (ajnr.org)

This paper presents a complete derivation and design of a physics-informed neural network (PINN) applicable to solve initial and boundary value problems described by linear ordinary differential equations. (tati.hu)

However, in the mammalian auditory system many aspects of this hierarchical organization remain undiscovered, including the prominent classes of high-level representations (that would be analogous to face selectivity in the visual system or selectivity to bird's own song in the bird) and the dominant types of invariant transformations. (zotero.org)
Author SummaryA key question in visual neuroscience is how neural representations achieve invariance against appearance changes of objects. (zotero.org)

Anatomy15

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Analytical, Diagnostic and Therapeutic Techniques and Equipment38

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Phenomena and Processes36

Disciplines and Occupations4

Anthropology, Education, Sociology and Social Phenomena2

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Convolutional neural network3

Computation2

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20171

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Propose a novel3

Piecewise1

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Feedforward2

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