scholarly journals Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Alberto Alvarellos-González ◽  
Alejandro Pazos ◽  
Ana B. Porto-Pazos
Keyword(s):  
Neural Network ◽  
Information Processing ◽  
Network Performance ◽  
Computational Models ◽  
Classification Problem ◽  
Artificial Neuron ◽  
Network Function ◽  
Problem Resolution ◽  
Artificial Neurons ◽  
The Brain

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem.

Deep Convolutional Neural Network for Object Classification

2021 ◽  
pp. 317-343
Author(s):  
Amira Ahmad Al-Sharkawy ◽  
Gehan A. Bahgat ◽  
Elsayed E. Hemayed ◽  
Samia Abdel-Razik Mashali
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Computational Models ◽  
Human Performance ◽  
Deep Neural Networks ◽  
Object Classification ◽  
Classification Problem ◽  
Deep Convolutional Neural Network ◽  
Object Appearance

Object classification problem is essential in many applications nowadays. Human can easily classify objects in unconstrained environments easily. Classical classification techniques were far away from human performance. Thus, researchers try to mimic the human visual system till they reached the deep neural networks. This chapter gives a review and analysis in the field of the deep convolutional neural network usage in object classification under constrained and unconstrained environment. The chapter gives a brief review on the classical techniques of object classification and the development of bio-inspired computational models from neuroscience till the creation of deep neural networks. A review is given on the constrained environment issues: the hardware computing resources and memory, the object appearance and background, and the training and processing time. Datasets that are used to test the performance are analyzed according to the images environmental conditions, besides the dataset biasing is discussed.

Neural networks and Keras

2019 ◽  
pp. 66-90
Author(s):  
Thomas P. Trappenberg
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Basic Operation ◽  
Neuron Models ◽  
Recent Success ◽  
Artificial Neurons ◽  
Artificial Neural ◽  
The Brain

This chapter discusses the basic operation of an artificial neural network which is the major paradigm of deep learning. The name derives from an analogy to a biological brain. The discussion begins by outlining the basic operations of neurons in the brain and how these operations are abstracted by simple neuron models. It then builds networks of artificial neurons that constitute much of the recent success of AI. The focus of this chapter is on using such techniques, with subsequent consideration of their theoretical embedding.

The puzzle of chaotic neurodynamics

2001 ◽  
Vol 24 (5) ◽  
pp. 812-813
Author(s):  
Roman Borisyuk
Keyword(s):  
Information Processing ◽  
Experimental Evidence ◽  
Computational Models ◽  
Dynamical Behaviour ◽  
Neural Systems ◽  
Dynamical Regime ◽  
The Brain

Experimental evidence and mathematical/computational models show that in many cases chaotic, nonregular oscillations are adequate to describe the dynamical behaviour of neural systems. Further work is needed to understand the meaning of this dynamical regime for modelling information processing in the brain.

scholarly journals It may be time to perfect the neuron of artificial neural network

2020 ◽  
Author(s):  
Gang Liu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Input Data ◽  
Basic Unit ◽  
Future Research ◽  
Brain Neuron ◽  
Artificial Neuron ◽  
The Neural Network ◽  
Artificial Neural ◽  
The Brain

In recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence. The basic unit of an ANN is to mimic neurons in the brain. Neuron in ANNs is expressed as f(wx+b) or f(wx).This structure does not consider the information processing capabilities of dendrites. However, recently, studies shown that dendrites participate in pre-calculation in the brain. Concretely, biological dendrites play a role in the pre-processing to the interaction information of input data. Therefore, it's time to perfect the neuron of the neural network. This paper, add dendrite processing section, presents a novel artificial neuron, according to our previous studies (CR-PNN or Gang transform). The dendrite processing section can be expressed as WA.X. Because I perfected the basic unit of ANNs-neuron, there are so many networks to try, this article gives the basic architecture for reference in future research.

scholarly journals Expectation of Tourism Demand in Iraq by Using Artificial Neural Network

2019 ◽  
Vol 2 (2) ◽  
pp. 1-7
Author(s):  
Rashid Anasari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Tourism Demand ◽  
Artificial Neuron ◽  
Tourism Research ◽  
Prediction Bias ◽  
Alternative Approach ◽  
Artificial Neurons ◽  
Artificial Neural ◽  
Travel Industry

This study survey and proves this effectiveness connected with artificial neural networks (ANNs) as an alternative approach in the tourism research. The learning utilizes the travel industry in the Japan being a method for estimating need to exhibit the solicitation. The outcome reveals the use of ANNs in tourism research might perhaps result in better quotations when it comes to prediction bias and accuracy. Even more applications of ANNs in the context of tourism demand examination is needed to establish and validate the effects. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it.

scholarly journals Brain Tumor Detection using Enhanced Convolution Neural Network for MR Images

2021 ◽  
Vol 10 (2) ◽  
pp. 10-13
Author(s):  
M.B. Bramarambika ◽  
◽  
M Sesha Shayee ◽  
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Detection System ◽  
Human Life ◽  
Classification Problem ◽  
Tumor Detection ◽  
Convolution Neural Network ◽  
Brain Images ◽  
The Brain

Brain tumor is a mass that grows unevenly in the brain and directly affects human life. The mass occurs spontaneously because of the tissues surrounding the brain or the skull. There are two types of Brain tumor such as Benign and Malignant. Malignant brain tumors contain cancer cells and grow quickly and spread through to other brain and spine regions as well. Accurate and prompt diagnosis of brain tumors is essential for implementing an effective treatment of this disease. Brain images produced by the Magnetic Resonance Imaging (MRI) technique are a rich source of data for brain tumor diagnosis and treatment in the medical field. Due to the existence of a large number of features compared to the other imaging types. The performance of existing methods is inadequate considering the medical significance of the classification problem. Earlier methods relied on manually delineated tumor regions, prior to classification. This prevented them from being fully automated. The automatic algorithms developed using CNN and its variants could not achieve an influential improvement in performance. In order to overcome such an issue, the proposed one is automatic brain tumor detection system, which is “ Enhanced Convolution Neural Network (CNN) Algorithm for MRI Images” for the detection of brain tumor is useful to detect and classify the Glioma part into low Glioma and high Glioma.

scholarly journals It may be time to perfect the neuron of artificial neural network

2020 ◽  
Author(s):  
Gang Liu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Input Data ◽  
Basic Unit ◽  
Future Research ◽  
Brain Neuron ◽  
Artificial Neuron ◽  
The Neural Network ◽  
Artificial Neural ◽  
The Brain

In recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence. The basic unit of an ANN is to mimic neurons in the brain. Neuron in ANNs is expressed as f(wx+b) or f(wx).This structure does not consider the information processing capabilities of dendrites. However, recently, studies shown that dendrites participate in pre-calculation in the brain. Concretely, biological dendrites play a role in the pre-processing to the interaction information of input data. Therefore, it's time to perfect the neuron of the neural network. This paper added dendrite processing section, and presented a novel artificial neuron, according to our previous studies (CR-PNN or Gang transform). The dendrite processing section can be expressed as WA.X. Because I perfected the basic unit of ANNs-neuron, there are so many networks to try, this article gives the basic architecture for reference in future research.

scholarly journals Neuromodulatory connectivity defines the structure of a behavioral neural network

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Feici Diao ◽  
Amicia D Elliott ◽  
Fengqiu Diao ◽  
Sarav Shah ◽  
Benjamin H White
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Synaptic Connectivity ◽  
Behavioral Sequence ◽  
Key Factors ◽  
Functional Architecture ◽  
Network Function ◽  
Input Layer ◽  
The Brain ◽  
Insight Into

Neural networks are typically defined by their synaptic connectivity, yet synaptic wiring diagrams often provide limited insight into network function. This is due partly to the importance of non-synaptic communication by neuromodulators, which can dynamically reconfigure circuit activity to alter its output. Here, we systematically map the patterns of neuromodulatory connectivity in a network that governs a developmentally critical behavioral sequence in Drosophila. This sequence, which mediates pupal ecdysis, is governed by the serial release of several key factors, which act both somatically as hormones and within the brain as neuromodulators. By identifying and characterizing the functions of the neuronal targets of these factors, we find that they define hierarchically organized layers of the network controlling the pupal ecdysis sequence: a modular input layer, an intermediate central pattern generating layer, and a motor output layer. Mapping neuromodulatory connections in this system thus defines the functional architecture of the network.

scholarly journals It may be time to perfect the neuron of artificial neural network

2020 ◽  
Author(s):  
Gang Liu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Input Data ◽  
Basic Unit ◽  
Future Research ◽  
Brain Neuron ◽  
Artificial Neuron ◽  
The Neural Network ◽  
Artificial Neural ◽  
The Brain

In recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence. The basic unit of an ANN is to mimic neurons in the brain. Neuron in ANNs is expressed as f(wx+b) or f(wx).This structure does not consider the information processing capabilities of dendrites. However, recently, studies shown that dendrites participate in pre-calculation in the brain. Concretely, biological dendrites play a role in the pre-processing to the interaction information of input data. Therefore, it's time to perfect the neuron of the neural network. This paper added dendrite processing section, and presented a novel artificial neuron, according to our previous studies (CR-PNN or Gang transform). The dendrite processing section can be expressed as W <sup>i,i-1</sup>A<sup>i-</sup><sup>1</sup> ◦A<sup>0|1|2|...|i-1</sup> . Because I perfected the basic unit of ANNs-neuron, there are so many networks to try, this article gives the basic architecture for reference in future research.

scholarly journals Computational Roles of Intrinsic Synaptic Dynamics

2021 ◽  
Author(s):  
Genki Shimizu ◽  
Kensuke Yoshida ◽  
Haruo Kasai ◽  
Taro Toyoizumi
Keyword(s):  
Information Processing ◽  
Network Performance ◽  
Information Storage ◽  
List Type ◽  
Stable Performance ◽  
Synaptic Dynamics ◽  
Experimental Findings ◽  
The Brain ◽  
Stable Network

AbstractConventional theories assume that long-term information storage in the brain is implemented by modifying synaptic efficacy. Recent experimental findings challenge this view by demonstrating that dendritic spine sizes, or their corresponding synaptic weights, are highly volatile even in the absence of neural activity. Here we review previous computational works on the roles of these intrinsic synaptic dynamics. We first present the possibility for neuronal networks to sustain stable performance in their presence and we then hypothesize that intrinsic dynamics could be more than mere noise to withstand, but they may actually improve information processing in the brain.Highlights- Synapses exhibit changes due to intrinsic as well as extrinsic dynamics- Computational frameworks suggest stable network performance despite intrinsic changes- Intrinsic dynamics might be beneficial to information processing

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