Exploring BCI Control in Smart Environments

2021 ◽  
Vol 15 (5) ◽  
pp. 1-20
Author(s):  
Lin Yue ◽  
Hao Shen ◽  
Sen Wang ◽  
Robert Boots ◽  
Guodong Long ◽  
...  
Keyword(s):  
Recognition Task ◽  
Feature Representation ◽  
Smart Environments ◽  
Spatial Transformation ◽  
Image Objects ◽  
Image Mapping ◽  
Training Images ◽  
Intention Recognition ◽  
Multi Classification ◽  
The Brain

The brain–computer interface (BCI) control technology that utilizes motor imagery to perform the desired action instead of manual operation will be widely used in smart environments. However, most of the research lacks robust feature representation of multi-channel EEG series, resulting in low intention recognition accuracy. This article proposes an EEG2Image based Denoised-ConvNets (called EID) to enhance feature representation of the intention recognition task. Specifically, we perform signal decomposition, slicing, and image mapping to decrease the noise from the irrelevant frequency bands. After that, we construct the Denoised-ConvNets structure to learn the colorspace and spatial variations of image objects without cropping new training images precisely. Toward further utilizing the color and spatial transformation layers, the colorspace and colored area of image objects have been enhanced and enlarged, respectively. In the multi-classification scenario, extensive experiments on publicly available EEG datasets confirm that the proposed method has better performance than state-of-the-art methods.

scholarly journals A New Paradigm for Training Hyperactive Dopamine Transporter Knockout Rats: Influence of Novel Stimuli on Object Recognition

2021 ◽  
Vol 15 ◽  
Author(s):  
Natalia P. Kurzina ◽  
Anna B. Volnova ◽  
Irina Y. Aristova ◽  
Raul R. Gainetdinov
Keyword(s):  
Object Recognition ◽  
Dopamine Transporter ◽  
Cognitive Task ◽  
Recognition Task ◽  
Long Term Memory ◽  
New Paradigm ◽  
Dopaminergic Transmission ◽  
Novel Objects ◽  
High Level ◽  
The Brain

Attention deficit hyperactivity disorder (ADHD) is believed to be connected with a high level of hyperactivity caused by alterations of the control of dopaminergic transmission in the brain. The strain of hyperdopaminergic dopamine transporter knockout (DAT-KO) rats represents an optimal model for investigating ADHD-related pathological mechanisms. The goal of this work was to study the influence of the overactivated dopamine system in the brain on a motor cognitive task fulfillment. The DAT-KO rats were trained to learn an object recognition task and store it in long-term memory. We found that DAT-KO rats can learn to move an object and retrieve food from the rewarded familiar objects and not to move the non-rewarded novel objects. However, we observed that the time of task performance and the distances traveled were significantly increased in DAT-KO rats in comparison with wild-type controls. Both groups of rats explored the novel objects longer than the familiar cubes. However, unlike controls, DAT-KO rats explored novel objects significantly longer and with fewer errors, since they preferred not to move the non-rewarded novel objects. After a 3 months’ interval that followed the training period, they were able to retain the learned skills in memory and to efficiently retrieve them. The data obtained indicate that DAT-KO rats have a deficiency in learning the cognitive task, but their hyperactivity does not prevent the ability to learn a non-spatial cognitive task under the presentation of novel stimuli. The longer exploration of novel objects during training may ensure persistent learning of the task paradigm. These findings may serve as a base for developing new ADHD learning paradigms.

scholarly journals The relative coding strength of object identity and nonidentity features in human occipito-temporal cortex and convolutional neural networks

2020 ◽  
Author(s):  
Yaoda Xu ◽  
Maryam Vaziri-Pashkam
Keyword(s):  
Visual Processing ◽  
Visual Pathway ◽  
Feature Representation ◽  
Object Categorization ◽  
Object Identity ◽  
Visual Areas ◽  
Ventral Visual Pathway ◽  
Identity Representation ◽  
The Brain ◽  
Early Human

ABSTRACTAny given visual object input is characterized by multiple visual features, such as identity, position and size. Despite the usefulness of identity and nonidentity features in vision and their joint coding throughout the primate ventral visual processing pathway, they have so far been studied relatively independently. Here we document the relative coding strength of object identity and nonidentity features in a brain region and how this may change across the human ventral visual pathway. We examined a total of four nonidentity features, including two Euclidean features (position and size) and two non-Euclidean features (image statistics and spatial frequency content of an image). Overall, identity representation increased and nonidentity feature representation decreased along the ventral visual pathway, with identity outweighed the non-Euclidean features, but not the Euclidean ones, in higher levels of visual processing. A similar analysis was performed in 14 convolutional neural networks (CNNs) pretrained to perform object categorization with varying architecture, depth, and with/without recurrent processing. While the relative coding strength of object identity and nonidentity features in lower CNN layers matched well with that in early human visual areas, the match between higher CNN layers and higher human visual regions were limited. Similar results were obtained regardless of whether a CNN was trained with real-world or stylized object images that emphasized shape representation. Together, by measuring the relative coding strength of object identity and nonidentity features, our approach provided a new tool to characterize feature coding in the human brain and the correspondence between the brain and CNNs.SIGNIFICANCE STATEMENTThis study documented the relative coding strength of object identity compared to four types of nonidentity features along the human ventral visual processing pathway and compared brain responses with those of 14 CNNs pretrained to perform object categorization. Overall, identity representation increased and nonidentity feature representation decreased along the ventral visual pathway, with the coding strength of the different nonidentity features differed at higher levels of visual processing. While feature coding in lower CNN layers matched well with that of early human visual areas, the match between higher CNN layers and higher human visual regions were limited. Our approach provided a new tool to characterize feature coding in the human brain and the correspondence between the brain and CNNs.

scholarly journals Implicit and explicit systems differently predict possible dangers

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Eugenio Manassero ◽  
Ludovica Mana ◽  
Giulia Concina ◽  
Annamaria Renna ◽  
Benedetto Sacchetti
Keyword(s):  
Explicit Memory ◽  
Recognition Task ◽  
Response Patterns ◽  
Conditioning Paradigm ◽  
Defensive Responses ◽  
Memory Traces ◽  
Implicit And Explicit Memory ◽  
Threat Recognition ◽  
Implicit And Explicit ◽  
The Brain

Abstract One strategy to address new potential dangers is to generate defensive responses to stimuli that remind learned threats, a phenomenon called fear generalization. During a threatening experience, the brain encodes implicit and explicit memory traces. Nevertheless, there is a lack of studies comparing implicit and explicit response patterns to novel stimuli. Here, by adopting a discriminative threat conditioning paradigm and a two-alternative forced-choice recognition task, we found that the implicit reactions were selectively elicited by the learned threat and not by a novel similar but perceptually discriminable stimulus. Conversely, subjects explicitly misidentified the same novel stimulus as the learned threat. This generalization response was not due to stress-related interference with learning, but related to the embedded threatening value. Therefore, we suggest a dissociation between implicit and explicit threat recognition profiles and propose that the generalization of explicit responses stems from a flexible cognitive mechanism dedicated to the prediction of danger.

scholarly journals Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks

2021 ◽  
Vol 14 ◽  
Author(s):  
Hyojin Bae ◽  
Sang Jeong Kim ◽  
Chang-Eop Kim
Keyword(s):  
Neural Networks ◽  
Neural Coding ◽  
Deep Neural Networks ◽  
Neural Response ◽  
Feature Representation ◽  
Network Feature ◽  
Biological Neural Networks ◽  
Stimulus Features ◽  
The Comparative Study ◽  
The Brain

One of the central goals in systems neuroscience is to understand how information is encoded in the brain, and the standard approach is to identify the relation between a stimulus and a neural response. However, the feature of a stimulus is typically defined by the researcher's hypothesis, which may cause biases in the research conclusion. To demonstrate potential biases, we simulate four likely scenarios using deep neural networks trained on the image classification dataset CIFAR-10 and demonstrate the possibility of selecting suboptimal/irrelevant features or overestimating the network feature representation/noise correlation. Additionally, we present studies investigating neural coding principles in biological neural networks to which our points can be applied. This study aims to not only highlight the importance of careful assumptions and interpretations regarding the neural response to stimulus features but also suggest that the comparative study between deep and biological neural networks from the perspective of machine learning can be an effective strategy for understanding the coding principles of the brain.

scholarly journals Probing Brain Activation Patterns by Dissociating Semantics and Syntax in Sentences

2020 ◽  
Vol 34 (05) ◽  
pp. 9201-9208
Author(s):  
Shaonan Wang ◽  
Jiajun Zhang ◽  
Nan Lin ◽  
Chengqing Zong
Keyword(s):  
Brain Regions ◽  
Feature Representation ◽  
Semantic Feature ◽  
Imaging Data ◽  
Activation Patterns ◽  
Syntactic Feature ◽  
Syntactic Information ◽  
Brain Imaging Data ◽  
The Right ◽  
The Brain

The relation between semantics and syntax and where they are represented in the neural level has been extensively debated in neurosciences. Existing methods use manually designed stimuli to distinguish semantic and syntactic information in a sentence that may not generalize beyond the experimental setting. This paper proposes an alternative framework to study the brain representation of semantics and syntax. Specifically, we embed the highly-controlled stimuli as objective functions in learning sentence representations and propose a disentangled feature representation model (DFRM) to extract semantic and syntactic information in sentences. This model can generate one semantic and one syntactic vector for each sentence. Then we associate these disentangled feature vectors with brain imaging data to explore brain representation of semantics and syntax. Results have shown that semantic feature is represented more robustly than syntactic feature across the brain including the default-mode, frontoparietal, visual networks, etc.. The brain representations of semantics and syntax are largely overlapped, but there are brain regions only sensitive to one of them. For instance, several frontal and temporal regions are specific to the semantic feature; parts of the right superior frontal and right inferior parietal gyrus are specific to the syntactic feature.

scholarly journals Microglia control glutamatergic synapses in the adult mouse hippocampus

2021 ◽  
Author(s):  
Bernadette Basilico ◽  
Laura Ferrucci ◽  
Patrizia Ratano ◽  
Maria T. Golia ◽  
Alfonso Grimaldi ◽  
...  
Keyword(s):  
Recognition Task ◽  
Adult Brain ◽  
The Novel ◽  
Spine Density ◽  
Synaptic Organization ◽  
Glutamatergic Synapses ◽  
Hippocampal Synapses ◽  
Hippocampal Ca3 ◽  
Novel Object Recognition Task ◽  
The Brain

ABSTRACTMicroglial cells are active players in regulating synaptic development and plasticity in the brain. However, how these cells influence the normal functioning of synapses is largely unknown. In this study, we characterized the effects of pharmacological depletion of microglia, achieved by administration of PLX5622, on hippocampal CA3-CA1 synapses of adult wild type mice. Following microglial depletion, we observed a reduction of spontaneous and evoked glutamatergic activity associated with a decrease of dendritic spine density. We also observed the appearance of immature synaptic features accompanied by higher levels of plasticity. In addition, microglia depleted mice showed a deficit in the acquisition of the Novel Object Recognition task. Remarkably, microglial repopulation after PLX5622 withdrawal was associated with the recovery of hippocampal synapses and learning functions. Altogether, these data demonstrate that microglia contribute to normal synaptic functioning in the adult brain and that their removal induces reversible changes in synaptic organization and activity of glutamatergic synapses.

scholarly journals Spatial Recognition Memory: Differential Brain Strategic Activation According to Sex

2021 ◽  
Vol 15 ◽  
Author(s):  
Joaquín Castillo ◽  
Isabel Carmona ◽  
Sean Commins ◽  
Sergio Fernández ◽  
Juan José Ortells ◽  
...  
Keyword(s):  
Recognition Task ◽  
Event Related Potentials ◽  
Spatial Task ◽  
Memory Process ◽  
Memory Research ◽  
Attentional Resources ◽  
Related Potentials ◽  
The Brain ◽  
Spatial Recognition ◽  
Three Phases

Human spatial memory research has significantly progressed since the development of computerized tasks, with many studies examining sex-related performances. However, few studies explore the underlying electrophysiological correlates according to sex. In this study event-related potentials were compared between male and female participants during the performance of an allocentric spatial recognition task. Twenty-nine university students took part in the research. Results showed that while general performance was similar in both sexes, the brain of males and females displayed a differential activation. Males showed increased N200 modulation than females in the three phases of memory process (encoding, maintenance, and retrieval). Meanwhile females showed increased activation of P300 in the three phases of memory process compared to males. In addition, females exhibited more negative slow wave (NSW) activity during the encoding phase. These differences are discussed in terms of attentional control and the allocation of attentional resources during spatial processing. Our findings demonstrate that sex modulates the resources recruited to performed this spatial task.

Predicting Alzheimer's Disease Based on Network Topological Latent Representations

2020 ◽  
Vol 10 (3) ◽  
pp. 667-671
Author(s):  
Jing Sun ◽  
Yanhui Ding ◽  
Kun Zhao ◽  
Haiyan Xu ◽  
Yongxin Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Parietal Lobe ◽  
Structural Features ◽  
Brain Network ◽  
Feature Representation ◽  
Support Vector ◽  
Functional Connection ◽  
Potential Biomarker ◽  
The Brain

Alzheimer's disease (AD) is a progressive neurodegenerative disease. Right now there is no cure for AD. As we know, the brain structure is constantly changing as it progresses to AD. If we can detect these changes, it may be helpful to discover new biomarkers and early diagnosis of AD. We use the deep learning method DeepWalk to extract the structural features of each subject based on brain function connection. The method takes a brain topology network based on functional connection transformations as input and outputs a potential feature representation for each brain region in the brain network structure. In the current research, a new potential biomarker is found, which is based on a structural feature that can represent changes in brain connectivity. In our experiment, 79 Resting-state fMRI were used from ADNI dataset, including 49 normal controls (NCs) and 30 AD patients. By analyzing the structural features between AD patients and NCs, obvious differences are detected in the temporal lobe, parietal lobe, and frontal lobe. We use a support vector machine (SVM) model to evaluate the performance of these structural features, and its classification performance achieves 80.36% accuracy (specificity = 73.67%, sensitivity = 87.17%). In general, these findings indicate that structural features may be a new potential biomarker between NCs and AD patients.

Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights

2017 ◽  
Vol 29 (3) ◽  
pp. 578-602 ◽  
Author(s):  
Arash Samadi ◽  
Timothy P. Lillicrap ◽  
Douglas B. Tweed
Keyword(s):  
Deep Learning ◽  
Recognition Task ◽  
Piecewise Smooth ◽  
Spiking Neurons ◽  
Alignment Algorithm ◽  
Smooth Functions ◽  
Digit Recognition ◽  
Piecewise Smooth Functions ◽  
Spiking Networks ◽  
The Brain

Recent work in computer science has shown the power of deep learning driven by the backpropagation algorithm in networks of artificial neurons. But real neurons in the brain are different from most of these artificial ones in at least three crucial ways: they emit spikes rather than graded outputs, their inputs and outputs are related dynamically rather than by piecewise-smooth functions, and they have no known way to coordinate arrays of synapses in separate forward and feedback pathways so that they change simultaneously and identically, as they do in backpropagation. Given these differences, it is unlikely that current deep learning algorithms can operate in the brain, but we that show these problems can be solved by two simple devices: learning rules can approximate dynamic input-output relations with piecewise-smooth functions, and a variation on the feedback alignment algorithm can train deep networks without having to coordinate forward and feedback synapses. Our results also show that deep spiking networks learn much better if each neuron computes an intracellular teaching signal that reflects that cell’s nonlinearity. With this mechanism, networks of spiking neurons show useful learning in synapses at least nine layers upstream from the output cells and perform well compared to other spiking networks in the literature on the MNIST digit recognition task.

scholarly journals TGFβRI antagonist inhibits HIV-1 Nef-induced CC chemokine family ligand 2 (CCL2) in the brain and prevents spatial learning impairment

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Gladys Chompre ◽  
Neysha Martinez-Orengo ◽  
Myrella Cruz ◽  
James T. Porter ◽  
Richard J. Noel
Keyword(s):  
Spatial Learning ◽  
Recognition Task ◽  
Cytokine Gene Expression ◽  
Spatial Learning And Memory ◽  
Dependent Manner ◽  
The Novel ◽  
Tgfβ Signaling ◽  
Location Recognition ◽  
The Brain ◽  
Hiv 1

Abstract Background HIV-1–associated neurocognitive disorders (HAND) progression is related to continued inflammation despite undetectable viral loads and may be caused by early viral proteins expressed by latently infected cells. Astrocytes represent an HIV reservoir in the brain where the early viral neurotoxin negative factor (Nef) is produced. We previously demonstrated that astrocytic expression of Nef in the hippocampus of rats causes inflammation, macrophage infiltration, and memory impairment. Since these processes are affected by TGFβ signaling pathways, and TGFβ-1 is found at higher levels in the central nervous system of HIV-1+ individuals and is released by astrocytes, we hypothesized a role for TGFβ-1 in our model of Nef neurotoxicity. Methods To test this hypothesis, we compared cytokine gene expression by cultured astrocytes expressing Nef or green fluorescent protein. To determine the role of Nef and a TGFβRI inhibitor on memory and learning, we infused astrocytes expressing Nef into the hippocampus of rats and then treated them daily with an oral dose of SD208 (10 mg/kg) or placebo for 7 days. During this time, locomotor activity was recorded in an open field and spatial learning tested in the novel location recognition paradigm. Postmortem tissue analyses of inflammatory and signaling molecules were conducted using immunohistochemistry and immunofluorescence. Results TGFβ-1 was induced in cultures expressing Nef at 24 h followed by CCL2 induction which was prevented by blocking TGFβRI with SD208 (competitive inhibitor). Interestingly, Nef seems to change the TGFβRI localization as suggested by the distribution of the immunoreactivity. Nef caused a deficit in spatial learning that was recovered upon co-administration of SD208. Brain tissue from Nef-treated rats given SD208 showed reduced CCL2, phospho-SMAD2, cluster of differentiation 163 (CD163), and GFAP immunoreactivity compared to the placebo group. Conclusions Consistent with our previous findings, rats treated with Nef showed deficits in spatial learning and memory in the novel location recognition task. In contrast, rats treated with Nef + SD208 showed better spatial learning suggesting that Nef disrupts memory formation in a TGFβ-1-dependent manner. The TGFβRI inhibitor further reduced the induction of inflammation by Nef which was concomitant with decreased TGFβ signaling. Our findings suggest that TGFβ-1 signaling is an intriguing target to reduce neuroHIV.

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