scholarly journals THE PHASE TRANSITION IN HUMAN COGNITION

2009 ◽  
Vol 05 (01) ◽  
pp. 197-220 ◽  
Author(s):  
MICHAEL J. SPIVEY ◽  
SARAH E. ANDERSON ◽  
RICK DALE
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Phase Transition ◽  
Cognitive Psychology ◽  
Computational Neuroscience ◽  
Response Selection ◽  
Human Cognition ◽  
Natural Explanation ◽  
Linear Filters ◽  
Traditional Approaches

This article attempts to build a bridge between cognitive psychology and computational neuroscience, perhaps allowing each group to understand the other's theoretical insights and sympathize with the other's methodological challenges. In briefly discussing a collection of conceptual demonstrations, neural network and dynamical system simulations, and human experimental results, we highlight the importance of the concept of phase transition to understand cognitive function. Our goal is to show that viewing cognition as a self-organizing process (involving phase transitions, criticality, and autocatalysis) affords a more natural explanation of these data over traditional approaches inspired by a sequence of linear filters (involving detection, recognition, and then response selection).

A stable adaptive neural-network-based scheme for dynamical system control

2005 ◽  
Vol 285 (3) ◽  
pp. 653-667 ◽  
Author(s):  
X. Xu ◽  
Y.C. Liang ◽  
H.P. Lee ◽  
W.Z. Lin ◽  
S.P. Lim ◽  
...  
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
System Control ◽  
Adaptive Neural Network

scholarly journals International agricultural trade forecasting using machine learning

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Munisamy Gopinath ◽  
Feras A. Batarseh ◽  
Jayson Beckman ◽  
Ajay Kulkarni ◽  
Sei Jeong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Agricultural Trade ◽  
Supervised Machine Learning ◽  
Trade Patterns ◽  
Subjective Assessments ◽  
History Of ◽  
Network Approaches ◽  
Traditional Approaches

Abstract Focusing on seven major agricultural commodities with a long history of trade, this study employs data-driven analytics to decipher patterns of trade, namely using supervised machine learning (ML), as well as neural networks. The supervised ML and neural network techniques are trained on data until 2010 and 2014, respectively. Results show the high relevance of ML models to forecasting trade patterns in near- and long-term relative to traditional approaches, which are often subjective assessments or time-series projections. While supervised ML techniques quantified key economic factors underlying agricultural trade flows, neural network approaches provide better fits over the long term.

ALGORITHMS FOR FINGERPRINT CLASSIFICATION

2021 ◽  
Vol 335 (1) ◽  
pp. 39-44
Author(s):  
D. Lebedev ◽  
A. Abzhalilova
Keyword(s):  
Neural Network ◽  
Gabor Filter ◽  
Recognition System ◽  
Personal Identification ◽  
External Object ◽  
Biometric Identification ◽  
Fingerprint Classification ◽  
Combined Application ◽  
Traditional Approaches

Currently, biometric methods of personality are becoming more and more relevant recognition technology. The advantage of biometric identification systems, in comparison with traditional approaches, lies in the fact that not an external object belonging to a person is identified, but the person himself. The most widespread technology of personal identification by fingerprints, which is based on the uniqueness for each person of the pattern of papillary patterns. In recent years, many algorithms and models have appeared to improve the accuracy of the recognition system. The modern algorithms (methods) for the classification of fingerprints are analyzed. Algorithms for the classification of fingerprint images by the types of fingerprints based on the Gabor filter, wavelet - Haar, Daubechies transforms and multilayer neural network are proposed. Numerical and results of the proposed experiments of algorithms are carried out. It is shown that the use of an algorithm based on the combined application of the Gabor filter, a five-level wavelet-Daubechies transform and a multilayer neural network makes it possible to effectively classify fingerprints.

Finding Short-Range Parity-Time Phase-Transition Points with a Neural Network

2021 ◽  
Vol 38 (5) ◽  
pp. 051101
Author(s):  
Songju Lei ◽  
Dong Bai ◽  
Zhongzhou Ren ◽  
Mengjiao Lyu
Keyword(s):  
Neural Network ◽  
Phase Transition ◽  
Short Range ◽  
Transition Points

scholarly journals Simulation of speed neural control for irrigation machines

2020 ◽  
pp. 81-84
Author(s):  
Dmitry Aleksandrovich Solovyev ◽  
Galina Nickolaevna Kamyshova ◽  
Nadezhda Nickolaevna Terekhova ◽  
Sergey Mudarisovich Bakirov
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Physical Modeling ◽  
Neural Control ◽  
Speed Control ◽  
Effective Management ◽  
Traditional Approaches

The results of the simulation of speed control of an irrigation machine on neural network basis are presented. Traditional approaches based only on physical modeling of technical processes and relationships often make it difficult to find effective solutions. The proposed approach is based on the model of data mining, namely, on the model of speed neural control. Neural control, leads to the implementation of better and more effective management of irrigation equipment.

Dynamics of Neural Networks as Nonlinear Systems with Several Equilibria

2011 ◽  
pp. 331-357 ◽  
Author(s):  
Daniela Danciu
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Neural Networks ◽  
Time Delay ◽  
Dynamical Systems ◽  
Qualitative Properties ◽  
The Neural Network ◽  
The Neural Networks ◽  
Global Asymptotics ◽  
Dynamical Properties

Neural networks—both natural and artificial, are characterized by two kinds of dynamics. The first one is concerned with what we would call “learning dynamics”. The second one is the intrinsic dynamics of the neural network viewed as a dynamical system after the weights have been established via learning. The chapter deals with the second kind of dynamics. More precisely, since the emergent computational capabilities of a recurrent neural network can be achieved provided it has suitable dynamical properties when viewed as a system with several equilibria, the chapter deals with those qualitative properties connected to the achievement of such dynamical properties as global asymptotics and gradient-like behavior. In the case of the neural networks with delays, these aspects are reformulated in accordance with the state of the art of the theory of time delay dynamical systems.

scholarly journals Fast and Deep Graph Neural Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 3898-3905 ◽  
Author(s):  
Claudio Gallicchio ◽  
Alessio Micheli
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Neural Networks ◽  
State Of The Art ◽  
Input Graph ◽  
Sparse Networks ◽  
Set Up ◽  
The Stability ◽  
Graph Neural Networks ◽  
Architectural Organization

We address the efficiency issue for the construction of a deep graph neural network (GNN). The approach exploits the idea of representing each input graph as a fixed point of a dynamical system (implemented through a recurrent neural network), and leverages a deep architectural organization of the recurrent units. Efficiency is gained by many aspects, including the use of small and very sparse networks, where the weights of the recurrent units are left untrained under the stability condition introduced in this work. This can be viewed as a way to study the intrinsic power of the architecture of a deep GNN, and also to provide insights for the set-up of more complex fully-trained models. Through experimental results, we show that even without training of the recurrent connections, the architecture of small deep GNN is surprisingly able to achieve or improve the state-of-the-art performance on a significant set of tasks in the field of graphs classification.

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