The three attentional networks and the two hemispheric mechanisms

1995 ◽  
Vol 18 (2) ◽  
pp. 343-344 ◽  
Author(s):  
Uri Fidelman
Keyword(s):  
Neural Networks ◽  
Data Processing ◽  
Methodological Problem ◽  
Neural System ◽  
Attentional Networks ◽  
Hemispheric Mechanisms ◽  
The Brain ◽  
Processing Mechanism

AbstractA methodological problem may distort the implications derived from the metabolism scans of the brain, but Posner & Raichle may have found neural networks which underlie the analytical and synthetical hemispheric data processing mechanism. This methodological problem is that a large regional consumption of energy, detected by the PET technique, is not necessarily related to more data processing. It may be related to the inefficiency of the neural system at this region.

scholarly journals Classical Soliton Theory for Studying the Dynamics and Evolution of in Network

2021 ◽  
Vol 8 (4) ◽  
pp. 01-06
Author(s):  
Sergey Belyakin
Keyword(s):  
Neural Networks ◽  
Data Processing ◽  
Dynamic Model ◽  
Mammalian Brain ◽  
Complex Task ◽  
Process Data ◽  
Brain Neurons ◽  
Soliton Theory ◽  
External Stimuli ◽  
The Brain

This paper presents the dynamic model ofthe soliton. Based on this model, it is supposed to study the state of the network. The term neural networks refersto the networks of neurons in the mammalian brain. Neurons are its main units of computation. In the brain, they are connected together in a network to process data. This can be a very complex task, and so the dynamics of neural networks in the mammalian brain in response to external stimuli can be quite complex. The inputs and outputs of each neuron change as a function of time, in the form of so-called spike chains, but the network itself also changes. We learn and improve our data processing capabilities by establishing reconnections between neurons.

How the Brain Makes Potential

2017 ◽  
pp. 168-198
Author(s):  
Ken Richardson
Keyword(s):  
Neural Networks ◽  
Individual Differences ◽  
Brain Networks ◽  
Neural System ◽  
Dynamical Processes ◽  
Brain Functions ◽  
Unpredictable Environments ◽  
The Brain

Chapter 6 describes how a “neural” system of intelligence emerged as more changeable environments were encountered. It contrasts the traditional mechanical and computational metaphors of brain functions (largely based on ideological preconceptions) with the emerging concepts of dynamical processes in neural networks. Only the latter can deal with rapidly changing, unpredictable environments. The chapter goes on to critique efforts to relate individual differences in IQ to differences in brain networks using MRI scanning and related methods.

Relation Between Level of Prefrontal Activity and Subject's Performance

1999 ◽  
Vol 13 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Laurence Casini ◽  
Françoise Macar ◽  
Marie-Hélène Giard
Keyword(s):  
Brain Activity ◽  
Sensory Information ◽  
Practice Phase ◽  
Trained Subjects ◽  
Anagram Task ◽  
Economic Information ◽  
Long Duration ◽  
Level Of Activity ◽  
The Brain ◽  
Processing Mechanism

Abstract The experiment reported here was aimed at determining whether the level of brain activity can be related to performance in trained subjects. Two tasks were compared: a temporal and a linguistic task. An array of four letters appeared on a screen. In the temporal task, subjects had to decide whether the letters remained on the screen for a short or a long duration as learned in a practice phase. In the linguistic task, they had to determine whether the four letters could form a word or not (anagram task). These tasks allowed us to compare the level of brain activity obtained in correct and incorrect responses. The current density measures recorded over prefrontal areas showed a relationship between the performance and the level of activity in the temporal task only. The level of activity obtained with correct responses was lower than that obtained with incorrect responses. This suggests that a good temporal performance could be the result of an efficacious, but economic, information-processing mechanism in the brain. In addition, the absence of this relation in the anagram task results in the question of whether this relation is specific to the processing of sensory information only.

Dream Bizarreness as the Cognitive Correlate of Altered Neuronal Behavior in REM Sleep

1989 ◽  
Vol 1 (3) ◽  
pp. 201-222 ◽  
Author(s):  
Adam N. Mamelak ◽  
J. Allan Hobson
Keyword(s):  
Neural Networks ◽  
Cerebral Cortex ◽  
Rem Sleep ◽  
Neuronal Dynamics ◽  
Neurophysiological Model ◽  
The Relationship ◽  
Cognitive Feature ◽  
The Brain ◽  
Cognitive Correlate ◽  
Raphe Neurons

Bizarreness is a cognitive feature common to REM sleep dreams, which can be easily measured. Because bizarreness is highly specific to dreaming, we propose that it is most likely brought about by changes in neuronal activity that are specific to REM sleep. At the level of the dream plot, bizarreness can be defined as either discontinuity or incongruity. In addition, the dreamer's thoughts about the plot may be logically deficient. We propose that dream bizarreness is the cognitive concomitant of two kinds of changes in neuronal dynamics during REM sleep. One is the disinhibition of forebrain networks caused by the withdrawal of the modulatory influences of norepinephrine (NE) and serotonin (5HT) in REM sleep, secondary to cessation of firing of locus coeruleus and dorsal raphe neurons. This aminergic demodulation can be mathematically modeled as a shift toward increased error at the outputs from neural networks, and these errors might be represented cognitively as incongruities and/or discontinuities. We also consider the possibility that discontinuities are the cognitive concomitant of sudden bifurcations or “jumps” in the responses of forebrain neuronal networks. These bifurcations are caused by phasic discharge of pontogeniculooccipital (PGO) neurons during REM sleep, providing a source of cholinergic modulation to the forebrain which could evoke unpredictable network responses. When phasic PGO activity stops, the resultant activity in the brain may be wholly unrelated to patterns of activity dominant before such phasic stimulation began. Mathematically such sudden shifts from one pattern of activity to a second, unrelated one is called a bifurcation. We propose that the neuronal bifurcations brought about by PGO activity might be represented cognitively as bizarre discontinuities of dream plot. We regard these proposals as preliminary attempts to model the relationship between dream cognition and REM sleep neurophysiology. This neurophysiological model of dream bizarreness may also prove useful in understanding the contributions of REM sleep to the developmental and experiential plasticity of the cerebral cortex.

scholarly journals Assessing the Three Attentional Networks and Vigilance in the Adolescence Stages

2021 ◽  
Vol 11 (4) ◽  
pp. 503
Author(s):  
Jasmine Giovannoli ◽  
Diana Martella ◽  
Maria Casagrande
Keyword(s):  
Neural Networks ◽  
Early Adolescence ◽  
Executive Control ◽  
Early Adolescents ◽  
Response Strategy ◽  
Late Adolescents ◽  
Attentional Networks ◽  
Attentional Network Test ◽  
Middle Adolescence ◽  
Age Range

Attention involves three functionally and neuroanatomically distinct neural networks: alerting, orienting, and executive control. This study aimed to assess the attentional networks and vigilance in adolescents aged between 10 and 19 years using the attentional network test for interaction and vigilance (ANTI-V). One hundred and eighty-two adolescents divided into three groups (early adolescents, middle adolescents, late adolescents) participated in the study. The results indicate that after age 15, adolescents adopt a more conservative response strategy and increase the monitoring of self-errors. All the attentional networks seem to continue to develop during the age range considered in this study (10–19 y). Performance improved from early adolescence to middle adolescence and began to stabilize in late adolescence. Moreover, a low level of vigilance seems to harm alerting and orienting abilities.

scholarly journals Generalization of Patterns by Identification with Polynomial Neural Network

2010 ◽  
Vol 61 (2) ◽  
pp. 120-124 ◽  
Author(s):  
Ladislav Zjavka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Functional Dependence ◽  
Polynomial Neural Network ◽  
New Type ◽  
Input Variables ◽  
The Brain ◽  
Functional Output ◽  
Simplified Form

Generalization of Patterns by Identification with Polynomial Neural Network Artificial neural networks (ANN) in general classify patterns according to their relationship, they are responding to related patterns with a similar output. Polynomial neural networks (PNN) are capable of organizing themselves in response to some features (relations) of the data. Polynomial neural network for dependence of variables identification (D-PNN) describes a functional dependence of input variables (not entire patterns). It approximates a hyper-surface of this function with multi-parametric particular polynomials forming its functional output as a generalization of input patterns. This new type of neural network is based on GMDH polynomial neural network and was designed by author. D-PNN operates in a way closer to the brain learning as the ANN does. The ANN is in principle a simplified form of the PNN, where the combinations of input variables are missing.

Multi-organ coordination of lipoprotein secretion by hormones, nutrients and neural networks

2021 ◽  
Author(s):  
Priska Stahel ◽  
Changing Xiao ◽  
Avital Nahmias ◽  
Lili Tian ◽  
Gary Franklin Lewis
Keyword(s):  
Neural Networks ◽  
Adipose Tissue ◽  
Peptide Yy ◽  
Atherosclerotic Cardiovascular Disease ◽  
Neural Signals ◽  
Nutritional Factors ◽  
Lipoprotein Secretion ◽  
The Central Nervous System ◽  
Regulatory Effects ◽  
The Brain

Abstract Plasma triglyceride-rich lipoproteins (TRL), particularly atherogenic remnant lipoproteins, contribute to atherosclerotic cardiovascular disease (ASCVD). Hypertriglyceridemia may arise in part from hypersecretion of TRLs by the liver and intestine. Here we focus on the complex network of hormonal, nutritional, and neuronal interorgan communication that regulates secretion of TRLs, and provide our perspective on the relative importance of these factors. Hormones and peptides originating from the pancreas (insulin, glucagon), gut (GLP-1, GLP-2, ghrelin, CCK, peptide YY), adipose tissue (leptin, adiponectin) and brain (GLP-1) modulate TRL secretion by receptor-mediated responses and indirectly via neural networks. In addition, the gut microbiome and bile acids influence lipoprotein secretion in humans and animal models. Several nutritional factors modulate hepatic lipoprotein secretion through effects on the central nervous system. Vagal afferent signalling from the gut to the brain and efferent signals from the brain to the liver and gut are modulated by hormonal and nutritional factors to influence TRL secretion. Some of these factors have been extensively studied and shown to have robust regulatory effects whereas others are ‘emerging’ regulators, whose significance remains to be determined. The quantitative importance of these factors relative to one another and relative to the key regulatory role of lipid availability remains largely unknown. Our understanding of the complex interorgan regulation of TRL secretion is rapidly evolving to appreciate the extensive hormonal, nutritional and neural signals emanating not only from gut and liver but also from the brain, pancreas, and adipose tissue.

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