scholarly journals Evolving super stimuli for real neurons using deep generative networks

2019 ◽  
Author(s):  
Carlos R. Ponce ◽  
Will Xiao ◽  
Peter F. Schade ◽  
Till S. Hartmann ◽  
Gabriel Kreiman ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Visual Cortex ◽  
Neural Coding ◽  
Deep Neural Network ◽  
Natural Images ◽  
List Type ◽  
Neuronal Responses ◽  
Diagnostic Features ◽  
Neural Selectivity

AbstractFinding the best stimulus for a neuron is challenging because it is impossible to test all possible stimuli. Here we used a vast, unbiased, and diverse hypothesis space encoded by a generative deep neural network model to investigate neuronal selectivity in inferotemporal cortex without making any assumptions about natural features or categories. A genetic algorithm, guided by neuronal responses, searched this space for optimal stimuli. Evolved synthetic images evoked higher firing rates than even the best natural images and revealed diagnostic features, independently of category or feature selection. This approach provides a way to investigate neural selectivity in any modality that can be represented by a neural network and challenges our understanding of neural coding in visual cortex.HighlightsA generative deep neural network interacted with a genetic algorithm to evolve stimuli that maximized the firing of neurons in alert macaque inferotemporal and primary visual cortex.The evolved images activated neurons more strongly than did thousands of natural images.Distance in image space from the evolved images predicted responses of neurons to novel images.

scholarly journals USE OF GENETIC ALGORITHM IN DEEP NEURAL NETWORKS CONFIGURATION FOR THE PURPOSES OF COMPUTER ATTACKS CLASSIFICATION

2020 ◽  
pp. 104-117
Author(s):  
O.S. Amosov ◽  
◽  
S.G. Amosova ◽  
D.S. Magola ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Classification Problem ◽  
Neural Network Architecture ◽  
Computer Attacks ◽  
Neural Network Technology

The task of multiclass network classification of computer attacks is given. The applicability of deep neural network technology in problem solving has been considered. Deep neural network architecture was chosen based on the strategy of combining a set of convolution and recurrence LSTM layers. Op-timization of neural network parameters based on genetic algorithm is proposed. The presented results of modeling show the possibility of solving the network classification problem in real time.

A study on global optimization and deep neural network modeling method in performance-seeking control

2019 ◽  
Vol 234 (1) ◽  
pp. 46-59 ◽  
Author(s):  
Qiangang Zheng ◽  
Dawei Fu ◽  
Yong Wang ◽  
Haoying Chen ◽  
Haibo Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Global Optimization ◽  
Interval Analysis ◽  
Sequential Quadratic Programming ◽  
Deep Neural Network ◽  
Network Modeling ◽  
Engine Performance ◽  
Neural Network Modeling ◽  
Swarm Optimization

In this article, a novel performance-seeking control method based on deep neural network and interval analysis is proposed to obtain a better engine performance. A deep neural network modeling method which has stronger representation capability than conventional neural network and can deal with big training data is adopted to establish an on-board model in the subsonic and supersonic cruising envelops. Meanwhile, a global optimization algorithm interval analysis is applied here to get a better engine performance. Finally, two simulation experiments are conducted to verify the effectiveness of the proposed methods. One is the on-board model modeling which compares the deep neural network with the conventional neural network, and the other is the performance-seeking control simulations comparing interval analysis with feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm, respectively. These two experiments show that the deep neural network has much higher precision than the conventional neural network and the interval analysis gets much better engine performance than feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm.

scholarly journals Modeling and Composition Design of Low-Alloy Steel’s Mechanical Properties Based on Neural Networks and Genetic Algorithms

Materials ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 5316
Author(s):  
Zhenlong Zhu ◽  
Yilong Liang ◽  
Jianghe Zou
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Heat Treatment ◽  
Genetic Algorithm ◽  
Alloy Steel ◽  
Deep Neural Network ◽  
Treatment Process ◽  
Mechanical Performance ◽  
Low Alloy Steel ◽  
Forward Selection

Accurately improving the mechanical properties of low-alloy steel by changing the alloying elements and heat treatment processes is of interest. There is a mutual relationship between the mechanical properties and process components, and the mechanism for this relationship is complicated. The forward selection-deep neural network and genetic algorithm (FS-DNN&GA) composition design model constructed in this paper is a combination of a neural network and genetic algorithm, where the model trained by the neural network is transferred to the genetic algorithm. The FS-DNN&GA model is trained with the American Society of Metals (ASM) Alloy Center Database to design the composition and heat treatment process of alloy steel. First, with the forward selection (FS) method, influencing factors—C, Si, Mn, Cr, quenching temperature, and tempering temperature—are screened and recombined to be the input of different mechanical performance prediction models. Second, the forward selection-deep neural network (FS-DNN) mechanical prediction model is constructed to analyze the FS-DNN model through experimental data to best predict the mechanical performance. Finally, the FS-DNN trained model is brought into the genetic algorithm to construct the FS-DNN&GA model, and the FS-DNN&GA model outputs the corresponding chemical composition and process when the mechanical performance increases or decreases. The experimental results show that the FS-DNN model has high accuracy in predicting the mechanical properties of 50 furnaces of low-alloy steel. The tensile strength mean absolute error (MAE) is 11.7 MPa, and the yield strength MAE is 13.46 MPa. According to the chemical composition and heat treatment process designed by the FS-DNN&GA model, five furnaces of Alloy1–Alloy5 low-alloy steel were smelted, and tensile tests were performed on these five low-alloy steels. The results show that the mechanical properties of the designed alloy steel are completely within the design range, providing useful guidance for the future development of new alloy steel.

scholarly journals Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings

2020 ◽  
Vol 131 ◽  
pp. 109980 ◽  
Author(s):  
X.J. Luo ◽  
Lukumon O. Oyedele ◽  
Anuoluwapo O. Ajayi ◽  
Olugbenga O. Akinade ◽  
Hakeem A. Owolabi ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Extraction ◽  
Energy Consumption ◽  
Deep Neural Network ◽  
Energy Consumption Prediction ◽  
Consumption Prediction
Sign in / Sign up

Export Citation Format

Share Document