scholarly journals Redes de neurônios

1992 ◽  
Vol 14 (14) ◽  
pp. 07
Author(s):  
Rita M. C. de Almeida
Keyword(s):  
Neural Networks ◽  
Spin Glass ◽  
Physical Problem ◽  
Information Storage ◽  
Hopfield Model ◽  
The Brain ◽  
Scientific Advances ◽  
The Way

In the last ten years many scientific advances regarding neurons and the way they are interconnected has mad o it possible to study the dynamics of storage and Processing of information in the brain. In particular, the physicist J. J. Hopfield proposed a formal minimalist model to these neural networks reducing the problem to a particular case of a well – defined physical problem – the spin glass. Although the problem í s well defined, its solution is far from being trivial.Here we introduce the problem, describe Hopfield model, with its achievements and limitations, and present our contribution to the description of information storage in neural networks.

scholarly journals Do deep neural networks see the way we do?

2019 ◽  
Author(s):  
Georgin Jacob ◽  
R. T. Pramod ◽  
Harish Katti ◽  
S. P. Arun
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Relative Size ◽  
Training Data ◽  
Object Representations ◽  
Multiple Dimensions ◽  
Deep Networks ◽  
The Brain ◽  
Global Advantage ◽  
The Way

ABSTRACTDeep neural networks have revolutionized computer vision, and their object representations match coarsely with the brain. As a result, it is widely believed that any fine scale differences between deep networks and brains can be fixed with increased training data or minor changes in architecture. But what if there are qualitative differences between brains and deep networks? Do deep networks even see the way we do? To answer this question, we chose a deep neural network optimized for object recognition and asked whether it exhibits well-known perceptual and neural phenomena despite not being explicitly trained to do so. To our surprise, many phenomena were present in the network, including the Thatcher effect, mirror confusion, Weber’s law, relative size, multiple object normalization and sparse coding along multiple dimensions. However, some perceptual phenomena were notably absent, including processing of 3D shape, patterns on surfaces, occlusion, natural parts and a global advantage. Our results elucidate the computational challenges of vision by showing that learning to recognize objects suffices to produce some perceptual phenomena but not others and reveal the perceptual properties that could be incorporated into deep networks to improve their performance.

Material Demands for Optical Neural Networks

MRS Bulletin ◽  
1988 ◽  
Vol 13 (8) ◽  
pp. 30-35 ◽  
Author(s):  
Dana Z. Anderson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Speech Processing ◽  
Visual Processing ◽  
Network Models ◽  
Human Memory ◽  
Information Storage ◽  
Physical Damage ◽  
Neural Network Models ◽  
The Brain

From the time of their conception, holography and holograms have evolved as a metaphor for human memory. Holograms can be made so that the information they contain is distributed throughout the holographic medium—destroy part of the hologram and the stored information remains wholly intact, except for a loss of detail. In this property holograms evidently have something in common with human memory, which is to some extent resilient against physical damage to the brain. There is much more to the metaphor than simply that information is stored in a distributed manner.Research in the optics community is now looking to holography, in particular dynamic holography, not only for information storage, but for information processing as well. The ideas are based upon neural network models. Neural networks are models for processing that are inspired by the apparent architecture of the brain. This is a processing paradigm that is new to optics. From within this network paradigm we look to build machines that can store and recall information associatively, play back a chain of recorded events, undergo learning and possibly forgetting, make decisions, adapt to a particular environment, and self-organize to evolve some desirable behavior. We hope that neural network models will give rise to optical machines for memory, speech processing, visual processing, language acquisition, motor control, and so on.

8. Retrospect of Mental Philosophy (Periodical Literature.)

1880 ◽  
Vol 26 (113) ◽  
pp. 119
Author(s):  
B. F. C. Costelloe
Keyword(s):  
Periodical Literature ◽  
Main Interest ◽  
Intended Meaning ◽  
Border Line ◽  
Emotional Language ◽  
Ordinary Power ◽  
The Moment ◽  
The Brain ◽  
The Way

The first number for the year is not remarkable for any paper of striking value. Readers of the Journal will be chiefly attracted by the long and clearly written resumé of Dr. Hughlings Jackson's recent studies “On Affections of Speech from Disease of the Brain,” which is contributed by Mr. James Sully. He remarks on the great value of Dr. Jackson's attempts to classify the different forms of aphasia under the three main heads or stages of—(1) Defect of Speech, in which the patient has a full vocabulary, but confuses words; (2) Loss of Speech, in which the patient is practically speechless, and his pantomimic power is impaired as well; and (3) Loss of Language, in which, besides being speechless, he has altogether lost the power of pantomime, and even his faculty of emotional language is deeply involved in the wreck. All these states or stages again are, properly speaking, to be distinguished altogether from affections of speech in the way of loss of articulation (owing to paralysis of the tongue, &c.), or loss of vocalisation (owing to disease of the larynx); whereas the three degrees or stages of aphasia proper are due to a deep-seated and severe disorganisation of the brain. The main interest of the theory lies in the ingenious and carefully-argued analysis of the symptoms, by which Dr. Jackson arrives at the theory that as the process of destruction goes on, the superior “layers” or strata of speech fail first—those namely which involve the ordinary power of adapting sounds to the circumstances of the moment as they arise; after them fail the “more highly organized utterances” those, namely, which have in any way become automatic, such as “come on,” “wo! wo!” and even “yes” and “no,” which stand on the border-line between emotional and intellectual language; next fails the power of adapting other than vocal signs to convey an intended meaning, which is called, rather clumsily, “pantomimic propositionising;” and last of all dies out the power of uttering sounds or making signs expressive merely of emotion—a power which, of course, is not true speech at all.

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 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.

scholarly journals Inferring brain-computational mechanisms with models of activity measurements

2016 ◽  
Vol 371 (1705) ◽  
pp. 20160278 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Jörn Diedrichsen
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Summary Statistics ◽  
Activity Data ◽  
Individual Measurement ◽  
Activity Measurements ◽  
The Brain ◽  
The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

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.

Endovascular Advances for Brain Arteriovenous Malformations

Neurosurgery ◽  
2014 ◽  
Vol 74 (suppl_1) ◽  
pp. S74-S82 ◽  
Author(s):  
R. Webster Crowley ◽  
Andrew F. Ducruet ◽  
Cameron G. McDougall ◽  
Felipe C. Albuquerque
Keyword(s):  
Endovascular Treatment ◽  
Surgical Resection ◽  
Arteriovenous Malformations ◽  
Treatment Options ◽  
Brain Arteriovenous Malformations ◽  
Short Period ◽  
Cerebral Avms ◽  
Brain Avms ◽  
The Brain ◽  
The Way

Abstract Arteriovenous malformations (AVMs) of the brain represent unique challenges for treating physicians. Although these lesions have traditionally been treated with surgical resection alone, advancements in endovascular and radiosurgical therapies have greatly expanded the treatment options for patients harboring brain AVMs. Perhaps no subspecialty within neurosurgery has seen as many advancements over a relatively short period of time as the endovascular field. A number of these endovascular innovations have been designed primarily for cerebral AVMs, and even those advancements that are not particular to AVMs have resulted in substantial changes to the way cerebral AVMs are treated. These advancements have enabled the embolization of cerebral AVMs to be performed either as a stand-alone treatment, or in conjunction with surgery or radiosurgery. Perhaps nothing has impacted the treatment of brain AVMs as substantially as the development of liquid embolics, most notably Onyx and n-butyl cyanoacrylate. However, of near-equal impact has been the innovations seen in the catheters that help deliver the liquid embolics to the AVMs. These developments include flow-directed catheters, balloon-tipped catheters, detachable-tipped catheters, and distal access catheters. This article aims to review some of the more substantial advancements in the endovascular treatment of brain AVMs and to discuss the literature surrounding the expanding indications for endovascular treatment of these lesions.

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