Dynamics of brain activation during a word and image recognition task: An electrophysiological study

Lactate is an efficient neuronal energy source, even in presence of glucose. However, the importance of lactate shuttling between astrocytes and neurons for brain activation and function remains to be established. For this purpose, metabolic and hemodynamic responses to sensory stimulation have been measured by functional magnetic resonance spectroscopy and blood oxygen level-dependent (BOLD) fMRI after down-regulation of either neuronal MCT2 or astroglial MCT4 in the rat barrel cortex. Results show that the lactate rise in the barrel cortex upon whisker stimulation is abolished when either transporter is down-regulated. Under the same paradigm, the BOLD response is prevented in all MCT2 down-regulated rats, while about half of the MCT4 down-regulated rats exhibited a loss of the BOLD response. Interestingly, MCT4 down-regulated animals showing no BOLD response were rescued by peripheral lactate infusion, while this treatment had no effect on MCT2 down-regulated rats. When animals were tested in a novel object recognition task, MCT2 down-regulated animals were impaired in the textured but not in the visual version of the task. For MCT4 down-regulated animals, while all animal succeeded in the visual task, half of them exhibited a deficit in the textured task, a similar segregation into two groups as observed for BOLD experiments. Our data demonstrate that lactate shuttling between astrocytes and neurons is essential to give rise to both neurometabolic and neurovascular couplings, which form the basis for the detection of brain activation by functional brain imaging techniques. Moreover, our results establish that this metabolic cooperation is required to sustain behavioral performance based on cortical activation.

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Empirical Examination of Color Spaces in Deep Convolution Networks

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b4038.079220 ◽

2020 ◽

Vol 9 (2) ◽

pp. 1011-1018

Keyword(s):

Deep Learning ◽

Image Recognition ◽

Color Space ◽

Classical Problem ◽

Recognition Task ◽

Test Accuracy ◽

Color Spaces ◽

Empirical Examination ◽

Data Set ◽

Different Color

In this paper we present an empirical examination of deep convolution neural network (DCNN) performance in different color spaces for the classical problem of image recognition/classification. Most such deep learning architectures or networks are applied on RGB color space image data set, so our objective is to study DCNNs performance in other color spaces. We describe the design of our novel experiment and present results on whether deep learning networks for image recognition task is invariant to color spaces or not. In this study, we have analyzed the performance of 3 popular DCNNs (VGGNet, ResNet, GoogleNet) by providing input images in 5 different color spaces(RGB, normalized RGB, YCbCr, HSV , CIE-Lab) and compared performance in terms of test accuracy, test loss, and validation loss. All these combination of networks and color spaces are investigated on two datasets- CIFAR 10 and LINNAEUS 5. Our experimental results show that CNNs are variant to color spaces as different color spaces have different performance results for image classification task.

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A novel image recognition approach using multiscale saliency model and GoogLeNet

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-097 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 97-1-97-8

Author(s):

Guoan Yang ◽

Libo Jian ◽

Zhengzhi Lu ◽

Junjie Yang ◽

Deyang Liu

Keyword(s):

Image Recognition ◽

Recognition Task ◽

Visual Saliency ◽

Image Database ◽

Visual Selective Attention ◽

Original Image ◽

Multi Scale ◽

Saliency Model ◽

Salient Regions ◽

Visual Saliency Model

It is very good to apply the saliency model in the visual selective attention mechanism to the preprocessing process of image recognition. However, the mechanism of visual perception is still unclear, so this visual saliency model is not ideal. To this end, this paper proposes a novel image recognition approach using multiscale saliency model and GoogLeNet. First, a multi-scale convolutional neural network was taken advantage of constructing multiscale salient maps, which could be used as filters. Second, an original image was combined with the salient maps to generate the filtered image, which highlighted the salient regions and suppressed the background in the image. Third, the image recognition task was implemented by adopting the classical GoogLeNet model. In this paper, many experiments were completed by comparing four commonly used evaluation indicators on the standard image database MSRA10K. The experimental results show that the recognition results of the test images based on the proposed method are superior to some stateof- the-art image recognition methods, and are also more approximate to the results of human eye observation.

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Brain Activation Patterns in Schizophrenic and Comparison Volunteers During a Matched-Performance Auditory Recognition Task

American Journal of Psychiatry ◽

10.1176/appi.ajp.157.10.1634 ◽

2000 ◽

Vol 157 (10) ◽

pp. 1634-1645 ◽

Cited By ~ 39

Author(s):

Henry H. Holcomb ◽

Adrienne C. Lahti ◽

Deborah R. Medoff ◽

Martin Weiler ◽

Robert F. Dannals ◽

...

Keyword(s):

Recognition Task ◽

Brain Activation ◽

Activation Patterns ◽

Auditory Recognition

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Longitudinal brain changes in MDD during emotional encoding: effects of presence and persistence of symptomatology

Psychological Medicine ◽

10.1017/s0033291719001259 ◽

2019 ◽

Vol 50 (8) ◽

pp. 1316-1326 ◽

Cited By ~ 2

Author(s):

Hui Ai ◽

Esther M. Opmeer ◽

Jan-Bernard C. Marsman ◽

Dick J. Veltman ◽

Nic J. A. van der Wee ◽

...

Keyword(s):

Depressive Symptoms ◽

Symptom Severity ◽

Recognition Task ◽

Brain Activation ◽

Emotional Memory ◽

Symptomatic Improvement ◽

Memory Formation ◽

State Dependency ◽

State Dependent ◽

Over Time

AbstractBackgroundThe importance of the hippocampus and amygdala for disrupted emotional memory formation in depression is well-recognized, but it remains unclear whether functional abnormalities are state-dependent and whether they are affected by the persistence of depressive symptoms.MethodsThirty-nine patients with major depressive disorder and 28 healthy controls were included from the longitudinal functional magnetic resonance imaging (fMRI) sub-study of the Netherlands Study of Depression and Anxiety. Participants performed an emotional word-encoding and -recognition task during fMRI at baseline and 2-year follow-up measurement. At baseline, all patients were in a depressed state. We investigated state-dependency by relating changes in brain activation over time to changes in symptom severity. Furthermore, the effect of time spent with depressive symptoms in the 2-year interval was investigated.ResultsSymptom change was linearly associated with higher activation over time of the left anterior hippocampus extending to the amygdala during positive and negative word-encoding. Especially during positive word encoding, this effect was driven by symptomatic improvement. There was no effect of time spent with depression in the 2-year interval on change in brain activation. Results were independent of medication- and psychotherapy-use.ConclusionUsing a longitudinal within-subjects design, we showed that hippocampal–amygdalar activation during emotional memory formation is related to depressive symptom severity but not persistence (i.e. time spent with depression or ‘load’), suggesting functional activation patterns in depression are not subject to functional ‘scarring’ although this hypothesis awaits future replication.

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Image Recognition Task in the Solution of Automation of Information Processes

2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus) ◽

10.1109/elconrus51938.2021.9396149 ◽

2021 ◽

Author(s):

Liudmila P. Kozlova ◽

Aleksandr M. Belov ◽

Olga A. Kozlova

Keyword(s):

Image Recognition ◽

Recognition Task ◽

Information Processes

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Electrophysiological study of contextual variations in a short-term face recognition task

Cognitive Brain Research ◽

10.1016/j.cogbrainres.2004.10.009 ◽

2005 ◽

Vol 22 (3) ◽

pp. 471-487 ◽

Cited By ~ 13

Author(s):

Fabrice Guillaume ◽

Guy Tiberghien

Keyword(s):

Face Recognition ◽

Electrophysiological Study ◽

Recognition Task ◽

Short Term

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Remaining Useful Life Estimation Using Neural Ordinary Differential Equations

International Journal of Prognostics and Health Management ◽

10.36001/ijphm.2021.v12i2.2938 ◽

2021 ◽

Vol 12 (2) ◽

Author(s):

Marco Star ◽

Kristoffer McKee

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Differential Equations ◽

Ordinary Differential Equations ◽

Image Recognition ◽

State Of The Art ◽

Recognition Task ◽

Remaining Useful Life ◽

Learning Methods ◽

Time Frequency

Data-driven machinery prognostics has seen increasing popularity recently, especially with the effectiveness of deep learning methods growing. However, deep learning methods lack useful properties such as the lack of uncertainty quantification of their outputs and have a black-box nature. Neural ordinary differential equations (NODEs) use neural networks to define differential equations that propagate data from the inputs to the outputs. They can be seen as a continuous generalization of a popular network architecture used for image recognition known as the Residual Network (ResNet). This paper compares the performance of each network for machinery prognostics tasks to show the validity of Neural ODEs in machinery prognostics. The comparison is done using NASA’s Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset, which simulates the sensor information of degrading turbofan engines. To compare both architectures, they are set up as convolutional neural networks and the sensors are transformed to the time-frequency domain through the short-time Fourier transform (STFT). The spectrograms from the STFT are the input images to the networks and the output is the estimated RUL; hence, the task is turned into an image recognition task. The results found NODEs can compete with state-of-the-art machinery prognostics methods. While it does not beat the state-of-the-art method, it is close enough that it could warrant further research into using NODEs. The potential benefits of using NODEs instead of other network architectures are also discussed in this work.

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