Chronic Co-Variation of Neural Network Configuration and Activity in Mature Dissociated Cultures

2015 ◽  
Vol 98 (5) ◽  
pp. 34-42
Author(s):  
SATORU OKAWA ◽  
TAKESHI MITA ◽  
DOUGLAS BAKKUM ◽  
URS FREY ◽  
ANDREAS HIERLEMANN ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Configuration

scholarly journals Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3691
Author(s):  
Jian Liang ◽  
Junchao Zhang ◽  
Jianbo Shao ◽  
Bofan Song ◽  
Baoli Yao ◽  
...  
Keyword(s):  
Neural Network ◽  
3D Imaging ◽  
Phase Unwrapping ◽  
Training Data ◽  
Fringe Projection ◽  
Second Step ◽  
Network Configuration ◽  
Simulation Data ◽  
The Neural Network ◽  
Key Features

Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data.

RELATIONSHIP BETWEEN A FUZZY LOGIC AND A STEEPEST DESCENT APPROACH TO OPTIMIZE A FEEDFORWARD ARTIFICIAL NEURAL NETWORK CONFIGURATION

1994 ◽  
Vol 05 (04) ◽  
pp. 299-312
Author(s):  
ROBERT N. SHARPE ◽  
MO-YUEN CHOW
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Mathematical Models ◽  
Cost Function ◽  
Evaluation Criteria ◽  
Descent Method ◽  
Steepest Descent ◽  
Steepest Descent Method ◽  
Network Configuration ◽  
Training Time

The neural network designer must take into consideration many factors when selecting an appropriate network configuration. The performance of a given network configuration is influenced by many different factors such as: accuracy, training time, sensitivity, and the number of neurons used in the implementation. Using a cost function based on the four criteria mentioned previously, the various network paradigms can be evaluated relative to one another. If the mathematical models of the evaluation criteria as functions of the network configuration are known, then traditional techniques (such as the steepest descent method) could be used to determine the optimal network configuration. The difficulty in selecting an appropriate network configuration is due to the difficulty involved in determining the mathematical models of the evaluation criteria. This difficulty can be avoided by using fuzzy logic techniques to perform the network optimization as opposed to the traditional techniques. Fuzzy logic avoids the need of a detailed mathematical description of the relationship between the network performance and the network configuration, by using heuristic reasoning and linguistic variables. A comparison will be made between the fuzzy logic approach and the steepest descent method for the optimization of the cost function. The fuzzy optimization procedure could be applied to other systems where there is a priori information about their characteristics.

scholarly journals Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Shinichi Tamura ◽  
Yoshi Nishitani ◽  
Chie Hosokawa ◽  
Tomomitsu Miyoshi ◽  
Hajime Sawai
Keyword(s):  
Neural Network ◽  
Neuronal Network ◽  
Neuronal Networks ◽  
Decay Constant ◽  
Spatial Decay ◽  
Network Configuration ◽  
Code Analysis ◽  
Refractory Periods ◽  
Integrate And Fire ◽  
Spectrum Curve

It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a “signature” of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence.

scholarly journals Prior Recognition of Flash Floods: Concrete Optimal Neural Network Configuration Analysis for Multi-Resolution Sensing

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 210006-210022
Author(s):  
Talha Ahmed Khan ◽  
Muhammad Mansoor Alam ◽  
Zeeshan Shahid ◽  
Mazliham Mohd Su'ud
Keyword(s):  
Neural Network ◽  
Flash Floods ◽  
Network Configuration

scholarly journals Subjective Air Traffic Complexity Estimation Using Artificial Neural Networks

2019 ◽  
Vol 31 (4) ◽  
pp. 377-386 ◽  
Author(s):  
Petar Andraši ◽  
Tomislav Radišić ◽  
Doris Novak ◽  
Biljana Juričić
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Models ◽  
Air Traffic ◽  
Traffic Situation ◽  
Network Configuration ◽  
Air Traffic Controllers ◽  
Complexity Estimation ◽  
Artificial Neural

Air traffic complexity is usually defined as difficulty of monitoring and managing a specific air traffic situation. Since it is a psychological construct, best measure of complexity is that given by air traffic controllers. However, there is a need to make a method for complexity estimation which can be used without constant controller input. So far, mostly linear models were used. Here, the possibility of using artificial neural networks for complexity estimation is explored. Genetic algorithm has been used to search for the best artificial neural network configuration. The conclusion is that the artificial neural networks perform as well as linear models and that the remaining error in complexity estimation can only be explained as inter-rater or intra-rater unreliability. One advantage of artificial neural networks in comparison to linear models is that the data do not have to be filtered based on the concept of operations (conventional vs. trajectory-based).

Chronic Co-variation of Neural Network Configuration and Activity in Mature Dissociated Cultures

2014 ◽  
Vol 134 (3) ◽  
pp. 338-344
Author(s):  
Satoru Okawa ◽  
Takeshi Mita ◽  
Douglas Bakkum ◽  
Urs Frey ◽  
Andreas Hierlemann ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Configuration

scholarly journals Household Power Demand Prediction Using Evolutionary Ensemble Neural Network Pool with Multiple Network Structures

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 721 ◽  
Author(s):  
Songpu Ai ◽  
Antorweep Chakravorty ◽  
Chunming Rong
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Low Voltage ◽  
Model Performance ◽  
Forecast Accuracy ◽  
Power Demand ◽  
Network Configuration ◽  
Demand Prediction ◽  
Home Energy Management ◽  
Ensemble Neural Network

The progress of technology on energy and IoT fields has led to an increasingly complicated electric environment in low-voltage local microgrid, along with the extensions of electric vehicle, micro-generation, and local storage. It is required to establish a home energy management system (HEMS) to efficiently integrate and manage household energy micro-generation, consumption and storage, in order to realize decentralized local energy systems at the community level. Domestic power demand prediction is of great importance for establishing HEMS on realizing load balancing as well as other smart energy solutions with the support of IoT techniques. Artificial neural networks with various network types (e.g., DNN, LSTM/GRU based RNN) and other configurations are widely utilized on energy predictions. However, the selection of network configuration for each research is generally a case by case study achieved through empirical or enumerative approaches. Moreover, the commonly utilized network initialization methods assign parameter values based on random numbers, which cause diversity on model performance, including learning efficiency, forecast accuracy, etc. In this paper, an evolutionary ensemble neural network pool (EENNP) method is proposed to achieve a population of well-performing networks with proper combinations of configuration and initialization automatically. In the experimental study, power demand predictions of multiple households are explored in three application scenarios: optimizing potential network configuration set, forecasting single household power demand, and refilling missing data. The impacts of evolutionary parameters on model performance are investigated. The experimental results illustrate that the proposed method achieves better solutions on the considered scenarios. The optimized potential network configuration set using EENNP achieves a similar result to manual optimization. The results of household demand prediction and missing data refilling perform better than the naïve and simple predictors.

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