scholarly journals On the complex interplay between spectral harmonicity and different types of cross frequency couplings in non linear oscillators and biologically plausible neural network models

2020 ◽  
Author(s):  
Damián Dellavale ◽  
Osvaldo Matías Velarde ◽  
Germán Mato ◽  
Eugenio Urdapilleta
Keyword(s):  
High Harmonic ◽  
Network Models ◽  
Necessary Condition ◽  
Complex Interplay ◽  
Neural Network Models ◽  
Oscillatory Dynamics ◽  
Harmonic Content ◽  
Non Linear ◽  
Nonlinear Processing ◽  
Non Linear Oscillator

AbstractBackgroundCross-frequency coupling (CFC) refers to the non linear interaction between oscillations in different frequency bands, and it is a rather ubiquitous phenomenon that has been observed in a variety of physical and biophysical systems. In particular, the coupling between the phase of slow oscillations and the amplitude of fast oscillations, referred as phase-amplitude coupling (PAC), has been intensively explored in the brain activity recorded from animals and humans. However, the interpretation of these CFC patterns remains challenging since harmonic spectral correlations characterizing non sinusoidal oscillatory dynamics can act as a confounding factor.MethodsSpecialized signal processing techniques are proposed to address the complex interplay between spectral harmonicity and different types of CFC, not restricted only to PAC. For this, we provide an in-depth characterization of the Time Locked Index (TLI) as a novel tool aimed to efficiently quantify the harmonic content of noisy time series. It is shown that the proposed TLI measure is more robust and outperform traditional phase coherence metrics (e.g. Phase Locking Value, Pairwise Phase Consistency) in several aspects.ResultsWe found that a non linear oscillator under the effect of additive noise can produce spurious CFC with low spectral harmonic content. On the other hand, two coupled oscillatory dynamics with independent fundamental frequencies can produce true CFC with high spectral harmonic content via a rectification mechanism or other post-interaction nonlinear processing mechanisms. These results reveal a complex interplay between CFC and harmonicity emerging in the dynamics of biologically plausible neural network models and more generic non linear and parametric oscillators.ConclusionsWe show that, contrary to what is usually assumed in the literature, the high harmonic content observed in non sinusoidal oscillatory dynamics, is neither sufficient nor necessary condition to interpret the associated CFC patterns as epiphenomenal. There is mounting evidence suggesting that the combination of multimodal recordings, specialized signal processing techniques and theoretical modeling is becoming a required step to completely understand CFC patterns observed in oscillatory rich dynamics of physical and biophysical systems.HighlightsTime locked index efficiently quantifies the harmonic content of noisy time series.A non linear oscillator under the effect of additive noise can produce spurious cross frequency couplings (CFC) with low spectral harmonic content.Two coupled oscillatory dynamics with independent fundamental frequencies can produce true CFC with high spectral harmonic content via rectification mechanisms or other post-interaction nonlinear processing mechanisms.A non sinusoidal oscillatory dynamics with high harmonic content is neither sufficient nor necessary condition for spurious CFC.A complex interplay between CFC and harmonicity emerges from the dynamics of nonlinear, parametric and biologically plausible oscillators.

scholarly journals Neural network emulation of a rainfall-runoff model

2007 ◽  
Vol 4 (1) ◽  
pp. 287-326 ◽  
Author(s):  
R. J. Abrahart ◽  
L. M. See
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Network Models ◽  
Exact Equation ◽  
Rainfall Runoff ◽  
Neural Network Models ◽  
Linear Relationships ◽  
Non Linear ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. The potential of an artificial neural network to perform simple non-linear hydrological transformations is examined. Four neural network models were developed to emulate different facets of a recognised non-linear hydrological transformation equation that possessed a small number of variables and contained no temporal component. The modeling process was based on a set of uniform random distributions. The cloning operation facilitated a direct comparison with the exact equation-based relationship. It also provided broader information about the power of a neural network to emulate existing equations and model non-linear relationships. Several comparisons with least squares multiple linear regression were performed. The first experiment involved a direct emulation of the Xinanjiang Rainfall-Runoff Model. The next two experiments were designed to assess the competencies of two neural solutions that were developed on a reduced number of inputs. This involved the omission and conflation of previous inputs. The final experiment used derived variables to model intrinsic but otherwise concealed internal relationships that are of hydrological interest. Two recent studies have suggested that neural solutions offer no worthwhile improvements in comparison to traditional weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. Yet such fundamental properties are intrinsic aspects of catchment processes that cannot be excluded or ignored. The results from the four experiments that are reported in this paper are used to challenge the interpretations from these two earlier studies and thus further the debate with regards to the appropriateness of neural networks for hydrological modelling.

scholarly journals Extreme learning machines for weather-based modelling of silk cocoon production

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
PRAMIT PANDIT ◽  
BISHVAJIT BAKSHI ◽  
SHILPA M.
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Extreme Learning Machines ◽  
Linear Regression Models ◽  
Weather Variables ◽  
Neural Network Models ◽  
Cocoon Production ◽  
Silk Cocoon ◽  
Learning Machines ◽  
Non Linear

In spite of the immense popularity and sheer power of the neural network models, their application in sericulture is still very much limited. With this backdrop, this study evaluates the suitability of neural network models in comparison with the linear regression models in predicting silk cocoon production of the selected six districts (Kolar, Chikballapur, Ramanagara, Chamarajanagar, Mandya and Mysuru) of Karnataka by utilising weather variables for ten consecutive years (2009-2018). As the weather variables are found to be correlated, principal components are obtained and fed into the linear (principal component regression) and non-linear models (back propagation-artificial neural network and extreme learning machine) as inputs. Outcomes emanated from this experiment have revealed the clear advantages of employing extreme learning machines (ELMs) for weather-based modelling of silk cocoon production. Application of ELM would be particularly useful, when the relation between production and its attributing characters is complex and non-linear.

scholarly journals Comparison of Non Linear Models and Artificial Neural Network to Describe the Liveweight from Birth to Maturity in Female Jersey Cattle

2021 ◽  
pp. 114-119
Author(s):  
D. O. Omoniwa ◽  
J. E. T. Akinsola ◽  
R. O. Okeke ◽  
J. M. Madu ◽  
D. S. Bunjah Umar
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Inflection Point ◽  
Linear Models ◽  
Network Models ◽  
Linear Functions ◽  
Growth Data ◽  
Neural Network Models ◽  
Jersey Cattle ◽  
Non Linear

Evaluation of growth data is an important strategy to manage gross feed requirement in female Jersey cattle in the New Derived Guinea Savannah Zone of Nigeria. Two non-linear functions (Gompertz and Logistic) and Neural network models were used to fit liveweight (LW)-age data using the non linear procedure of JMP statistical software. Data used for this study were collected from 150 Jersey female cattle in Shonga Dairy Farm, Kwara, State from 2010-2018. The Neural network function showedthe best goodness of fit. Both the Gompertz and Logistic functions overestimated LW at birth, 3, 36, 48, 60 and 72months respectively. NN function overestimated the LW at 0, 3, 24, 36 and 72 months. The Gompertzfunction had the best estimation of asymptotic weight (649.51 kg) with average absolute growth rate (0.061 kg/day).The inflection point was 15.95, 9.55 and 34.5 months in Logistic, Gompertz and neural network models, respectively. A strong and positive correlation was observed between asymptote and inflection point in Gompertz functions. The metrics of goodness of fit criteria (R2 and RMSE), showed that NN with multilayer perceptron was superior to the other models but Gompertz model, was best in its ability to approximate complex functions of growth curve parametersin female Jersey cattle.

scholarly journals Neural network modelling of non-linear hydrological relationships

2007 ◽  
Vol 11 (5) ◽  
pp. 1563-1579 ◽  
Author(s):  
R. J. Abrahart ◽  
L. M. See
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Network Models ◽  
Target Surface ◽  
General Information ◽  
Neural Network Models ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
Linear Relationships ◽  
Non Linear

Abstract. Two recent studies have suggested that neural network modelling offers no worthwhile improvements in comparison to the application of weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. The potential of an artificial neural network to perform simple non-linear hydrological transformations under controlled conditions is examined in this paper. Eight neural network models were developed: four full or partial emulations of a recognised non-linear hydrological rainfall-runoff model; four solutions developed on an identical set of inputs and a calculated runoff coefficient output. The use of different input combinations enabled the competencies of solutions developed on a reduced number of parameters to be assessed. The selected hydrological model had a limited number of inputs and contained no temporal component. The modelling process was based on a set of random inputs that had a uniform distribution and spanned a modest range of possibilities. The initial cloning operations permitted a direct comparison to be performed with the equation-based relationship. It also provided more general information about the power of a neural network to replicate mathematical equations and model modest non-linear relationships. The second group of experiments explored a different relationship that is of hydrological interest; the target surface contained a stronger set of non-linear properties and was more challenging. Linear modelling comparisons were performed against traditional least squares multiple linear regression solutions developed on identical datasets. The reported results demonstrate that neural networks are capable of modelling non-linear hydrological processes and are therefore appropriate tools for hydrological modelling.

scholarly journals Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1909 ◽  
Author(s):  
Javier Estévez ◽  
Juan Antonio Bellido-Jiménez ◽  
Xiaodong Liu ◽  
Amanda Penélope García-Marín
Keyword(s):  
Neural Networks ◽  
Correlation Coefficients ◽  
Network Models ◽  
Semiarid Region ◽  
Monthly Precipitation ◽  
Neural Network Models ◽  
Non Linear ◽  
Effective Performance ◽  
Accurate Forecast ◽  
Input Variables

Accurate forecast of hydrological data such as precipitation is critical in order to provide useful information for water resources management, playing a key role in different sectors. Traditional forecasting methods present many limitations due to the high-stochastic property of precipitation and its strong variability in time and space: not identifying non-linear dynamics or not solving the instability of local weather situations. In this work, several alternative models based on the combination of wavelet analysis (multiscalar decomposition) with artificial neural networks have been developed and evaluated at sixteen locations in Southern Spain (semiarid region of Andalusia), representative of different climatic and geographical conditions. Based on the capability of wavelets to describe non-linear signals, ten wavelet neural network models (WNN) have been applied to predict monthly precipitation by using short-term thermo-pluviometric time series. Overall, the forecasting results show differences between the ten models, although an effective performance (i.e., correlation coefficients ranged from 0.76 to 0.90 and Root Mean Square Error values ranged from 6.79 to 29.82 mm) was obtained at each of the locations assessed. The most appropriate input variables to obtain the best forecasts are analyzed, according to the geo-climatic characteristics of the sixteen sites studied.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper
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