Modules Reconsidered

2021 ◽  
pp. 66-84
Author(s):  
John Zerilli
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Brain Regions ◽  
Cortical Column ◽  
Input Output ◽  
Single Node ◽  
The Neural Network ◽  
The Brain

The previous chapter argued that we ought to regard dissociability as the sine qua non of modularity. As for what in the brain meets this standard of modularity, the only likely candidate will be something resembling a cortical column. But this is not guaranteed. The effects of the neural network context may so compromise a region’s ability to maintain a set of stable input–output relations that it cannot be considered a genuine module. The brain’s network structure poses particular difficulties for modularity, since even if we were to treat nodes as modules, still we could be missing the point—the key to networks lies not in their nodes, but in the structure of their interactions, and these interactions make pinning down what any single node “does” a fraught enterprise. The chapter includes a table of specificity for brain regions.

Sunlight Greenhouse Temperature Prediction Model Based on Bayesian Regularization BP Neural Network

2015 ◽  
Vol 740 ◽  
pp. 871-874
Author(s):  
Hui Zhao ◽  
Li Rong Shi ◽  
Hong Jun Wang
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Bp Neural Network ◽  
Principal Component ◽  
Coupling Factor ◽  
Bayesian Regularization ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Input Variables ◽  
Bp Neural Network Model

Directing against the problems of too large size of the neural network structure due to the existence of a complex relationship between the input coupling factor and too many input factors in establishing model for predicting temperature of sunlight greenhouse. This article chose the environmental factors that affect the sunlight greenhouse temperature as data sample. Through the principal component analysis of data samples, three main factors were extracted. These selected principal component values were taken as the input variables of BP neural network model. Use the Bayesian regularization algorithm to improve the BP neural network. The empirical results show that this method is utilized modify BP neural network, which can simplify network structure and smooth fitting curve, has good generalization capability.

Cognitive Enhancement Mediated Through Postsynaptic Actions of Norepinephrine on Ongoing Cortical Activity

2005 ◽  
Vol 17 (8) ◽  
pp. 1739-1775 ◽  
Author(s):  
Osamu Hoshino
Keyword(s):  
Neural Network ◽  
Cognitive Performance ◽  
Neuronal Activity ◽  
Dynamic Property ◽  
Cognitive Enhancement ◽  
Cortical Activity ◽  
Sensory Stimuli ◽  
The Neural Network ◽  
Higher Doses ◽  
The Brain

We propose two distinct types of norepinephrine (NE)-neuromodulatory systems: an enhanced-excitatory and enhanced-inhibitory (E-E/E-I) system and a depressed-excitatory and enhanced-inhibitory (D-E/E-I) system. In both systems, inhibitory synaptic efficacies are enhanced, but excitatory ones are modified in a contradictory manner: the E-E/E-I system enhances excitatory synaptic efficacies, whereas the D-E/E-I system depresses them. The E-E/E-I and D-E/E-I systems altered the dynamic property of ongoing (background) neuronal activity and greatly influenced the cognitive performance (S/N ratio) of a cortical neural network. The E-E/E-I system effectively enhanced S/N ratio for weaker stimuli with lower doses of NE, whereas the D-E/E-I system enhanced stronger stimuli with higher doses of NE. The neural network effectively responded to weaker stimuli if brief γ-bursts were involved in ongoing neuronal activity that is controlled under the E-E/E-I neuromodulatory system. If the E-E/E-I and the D-E/E-I systems interact within the neural network, depressed neurons whose activity is depressed by NE application have bimodal property. That is, S/N ratio can be enhanced not only for stronger stimuli as its original property but also for weaker stimuli, for which coincidental neuronal firings among enhanced neurons whose activity is enhanced by NE application are essential. We suggest that the recruitment of the depressed neurons for the detection of weaker (subthreshold) stimuli might be advantageous for the brain to cope with a variety of sensory stimuli.

scholarly journals Automatic substantia nigra segmentation in neuromelanin-sensitive MRI by deep neural network in patients with prodromal and manifest synucleinopathy

2019 ◽  
pp. S453-S458
Author(s):  
R. Krupička ◽  
S. Mareček ◽  
C. Malá ◽  
M. Lang ◽  
O. Klempíř ◽  
...  
Keyword(s):  
Neural Network ◽  
Substantia Nigra ◽  
Signal Intensity ◽  
Substantia Nigra Pars Compacta ◽  
The Neural Network ◽  
Intensity Information ◽  
Ideal Tool ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Neuromelanin (NM) is a black pigment located in the brain in substantia nigra pars compacta (SN) and locus coeruleus. Its loss is directly connected to the loss of nerve cells in this part of the brain, which plays a role in Parkinson’s Disease. Magnetic resonance imaging (MRI) is an ideal tool to monitor the amount of NM in the brain in vivo. The aim of the study was the development of tools and methodology for the quantification of NM in a special neuromelanin-sensitive MRI images. The first approach was done by creating regions of interest, corresponding to the anatomical position of SN based on an anatomical atlas and determining signal intensity threshold. By linking the anatomical and signal intensity information, we were able to segment the SN. As a second approach, the neural network U-Net was used for the segmentation of SN. Subsequently, the volume characterizing the amount of NM in the SN region was calculated. To verify the method and the assumptions, data available from various patient groups were correlated. The main benefit of this approach is the observer-independency of quantification and facilitation of the image processing process and subsequent quantification compared to the manual approach. It is ideal for automatic processing many image sets in one batch.

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 CHOOSING A NEURAL NETWORK STRUCTURE FOR SIGNAL PROCESSING AS AN EXPERIMENT PLANNING

2021 ◽  
pp. 123-132
Author(s):  
Leonid A. Slavutskii ◽  
Elena V. Slavutskaya
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Network Structure ◽  
Electric Power Industry ◽  
Planning Theory ◽  
Experiment Planning ◽  
Harmonic Amplitude ◽  
The Neural Network ◽  
Nonlinear Distortions ◽  
Neural Network Structure

The paper is devoted to the use of artificial neural networks for signal processing in electrical engineering and electric power industry. Direct propagation neural network (perceptron) is considered as an object in the theory of experiment planning. The variants of the neural network structure empirical choice, the quality criteria of its training and testing are analyzed. It is shown that the perceptron structure choice, the training sample, and the training algorithms require planning. Variables and parameters of neuro algorithm that can act as factors, state parameters, and disturbing influences in the framework of the experimental planning theory are discussed. The proposed approach is demonstrated by the example of neural network analysis of the industrial frequency signal of 50 Hz nonlinear distortions. The possibility of using an elementary perceptron with one hidden layer and a minimum number of neurons to correct the transformer saturation current is analyzed. The conditions under which the neuro algorithm allows one to restore the values of the main harmonic amplitude, frequency and phase with an error of no more than one percent are revealed. The signal processing in a «sliding window» with a duration of a fraction of the fundamental frequency period is proposed, and the neuro algorithm accuracy characteristics are estimated. The possibility to automate the neural network structure choosing for signal processing is discussed.

scholarly journals Language switching training modulates the neural network of non-linguistic cognitive control

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0247100
Author(s):  
Mo Chen ◽  
Fengyang Ma ◽  
Zhaoqi Zhang ◽  
Shuhua Li ◽  
Man Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Cognitive Control ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Language Switching ◽  
Dorsal Anterior Cingulate Cortex ◽  
The Neural Network ◽  
The Impact

Bilingual language experience, such as switching between languages, has been shown to shape both cognitive and neural mechanisms of non-linguistic cognitive control. However, the neural adaptations induced by language switching remain unclear. Using fMRI, the current study examined the impact of short-term language switching training on the neural network of domain-general cognitive control for unbalanced Chinese-English bilinguals. Effective connectivity maps were constructed by using the extended unified structural equation models (euSEM) within 10 common brain regions involved in both language control and domain-general cognitive control. Results showed that, the dorsal anterior cingulate cortex/pre-supplementary motor area (dACC/pre-SMA) lost connection from the right thalamus after training, suggesting that less neural connectivity was required to complete the same domain-general cognitive control task. These findings not only provide direct evidence for the modulation of language switching training on the neural interaction of domain-general cognitive control, but also have important implications for revealing the potential neurocognitive adaptation effects of specific bilingual language experiences.

scholarly journals DuFeNet: Improve the Accuracy and Increase Shape Bias of Neural Network Models

2022 ◽  
Author(s):  
Zecong Ye ◽  
Zhiqiang Gao ◽  
Xiaolong Cui ◽  
Yaojie Wang ◽  
Nanliang Shan
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Network Structure ◽  
Network Performance ◽  
Human Visual Perception ◽  
Neural Network Models ◽  
Bias Effect ◽  
Shape Information ◽  
Shape Bias ◽  
The Neural Network

AbstractIn image classification field, existing work tends to modify the network structure to obtain higher accuracy or faster speed. However, some studies have found that the neural network usually has texture bias effect, which means that the neural network is more sensitive to the texture information than the shape information. Based on such phenomenon, we propose a new way to improve network performance by making full use of gradient information. The dual features network (DuFeNet) is proposed in this paper. In DuFeNet, one sub-network is used to learn the information of gradient features, and the other is a traditional neural network with texture bias. The structure of DuFeNet is easy to implement in the original neural network structure. The experimental results clearly show that DuFeNet can achieve better accuracy in image classification and detection. It can increase the shape bias of the network adapted to human visual perception. Besides, DuFeNet can be used without modifying the structure of the original network at lower additional parameters cost.

scholarly journals A Comprehensive Analysis of Metabolomics and Transcriptomics Reveals Novel Biomarkers and Mechanistic Insights on Lorlatinib Crosses the Blood-Brain Barrier

2021 ◽  
Vol 12 ◽  
Author(s):  
Wei Chen ◽  
Chunyu Li ◽  
Yafei Shi ◽  
Yujun Zhang ◽  
Dujia Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Interaction Network ◽  
Gene Interaction ◽  
Accuracy Rate ◽  
Gene Interaction Network ◽  
The Neural Network ◽  
Novel Biomarkers ◽  
And Control ◽  
The Brain

Of late, lorlatinib has played an increasingly pivotal role in the treatment of brain metastasis from non-small cell lung cancer. However, its pharmacokinetics in the brain and the mechanism of entry are still controversial. The purpose of this study was to explore the mechanisms of brain penetration by lorlatinib and identify potential biomarkers for the prediction of lorlatinib concentration in the brain. Detection of lorlatinib in lorlatinib-administered mice and control mice was performed using liquid chromatography and mass spectrometry. Metabolomics and transcriptomics were combined to investigate the pathway and relationships between metabolites and genes. Multilayer perceptron was applied to construct an artificial neural network model for prediction of the distribution of lorlatinib in the brain. Nine biomarkers related to lorlatinib concentration in the brain were identified. A metabolite-reaction-enzyme-gene interaction network was built to reveal the mechanism of lorlatinib. A multilayer perceptron model based on the identified biomarkers provides a prediction accuracy rate of greater than 85%. The identified biomarkers and the neural network constructed with these metabolites will be valuable for predicting the concentration of drugs in the brain. The model provides a lorlatinib to treat tumor brain metastases in the clinic.

Fault Detection for Nonlinear Systems

2001 ◽  
Author(s):  
Tor Fretheim ◽  
Rahmat Shoureshi ◽  
Tyrone Vincent
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Input Output ◽  
Feasible Method ◽  
Detection Techniques ◽  
Process Dynamics ◽  
The Neural Network ◽  
Non Linear Systems

Abstract A new fault detection and isolation scheme has been developed to enable automatic detection of faulty conditions in linear or non-linear systems. The focus of this paper is on the development of a general, and feasible method for nonlinear system fault detection which can be easily implemented on input/output models. The method proposed here is different in that the neural network is used to model the process dynamics, while a dead-beat observer is implemented by solving a set of coupled nonlinear equations. This enables the introduction of constraints into the problem that can improve the power of the fault detection techniques.

A Neural Network Structure Decomposition Based on Pruning and its Visualization Method

2013 ◽  
Vol 17 (3) ◽  
pp. 443-449
Author(s):  
Atsushi Shibata ◽  
◽  
Jiajun Lu ◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Network Structure ◽  
Robot Motion ◽  
Network Structures ◽  
Visualization Method ◽  
Pruning Method ◽  
The Neural Network ◽  
Structure Decomposition ◽  
Neural Network Structure

To decompose neural network structures for composite tasks, a pruning method and its visualization method are proposed. Visualization by placing the neurons on a 2D plane clarifies the structure related to each composited task. Experiments on a composite task using two tasks from a UCI dataset show that the neural network of the composite task contains more than 80% of neurons. The proposed methods target the transfer learning of robot motion, and results of an adaptation experiments are also referred.

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