Neural Networks In Brain Research Articles

Integrated information theory and its application for analysis of brain neuron activity

Purpose of this review is to consider the possibility to apply the integrated information theory to investigate the brain neural activity. Earlier was shown that the integrated information amount Φ quantifies a degree of a dynamic complexity of a system and able to predict a level of its success defined by classic observable benchmarks. For this reason, a question arises about the application of the integrated information theory to analyse changes in brain spiking activity due the acquisition of new experience. Conclusion. The bases of the integrated information theory and its possible application in neurobiology to investigate the process of new experience acquisition were reviewed. It was shown that the amount of integrated information Φ is a metric which is able to quantify the dynamic complexity of brain neural networks increasing when the new experience is acquired. Methods, enaabling the practical calculation of Φ for spiking data, were proposed.

On the similarities of representations in artificial and brain neural networks for speech recognition.

In recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar level of performance, despite their huge differences in implementation, and so deep learning models can-in principle-serve as candidates for mechanistic models of the human auditory system. Utilizing high-performance automatic speech recognition systems, and advanced non-invasive human neuroimaging technology such as magnetoencephalography and multivariate pattern-information analysis, the current study aimed to relate machine-learned representations of speech to recorded human brain representations of the same speech. In one direction, we found a quasi-hierarchical functional organization in human auditory cortex qualitatively matched with the hidden layers of deep artificial neural networks trained as part of an automatic speech recognizer. In the reverse direction, we modified the hidden layer organization of the artificial neural network based on neural activation patterns in human brains. The result was a substantial improvement in word recognition accuracy and learned speech representations. We have demonstrated that artificial and brain neural networks can be mutually informative in the domain of speech recognition.

A low-power Si:HfO2 ferroelectric tunnel memristor for spiking neural networks

As key components of the human brain's neural network, synapses and neurons are important processing units that enable highly complex neuromorphic systems. Spiking neural network (SNN) is more powerful and efficient in terms of neuromorphic computing. Moreover, memristor-based neuromorphic computers can implement neural network algorithms more effectively than conventional hardware. However, the investigation on spiking neural network (SNN) based neuromorphic computing is still in the exploratory stage. Herein, a SNN based on ferroelectric Si:HfO2 film (∼ 6.8 nm) memristor was realized. The Si:HfO2-based memristor exhibits lower switching voltage (1.55/− 1.50 V) and super low power consumption (∼ 32.65 fJ). Additionally, it also shows superior conductance tunability and reliable realization of multiple synaptic functions. Especially, the highly linear conductance modulation of the Si:HfO2-based memristor results in a high accuracy of ∼ 96.23 % for handwritten digits. Spatiotemporal model recognition and unsupervised synaptic weight update functions were successfully implemented with the SNN constructed by these synaptic devices and artificial neuron models, which demonstrates the excellent adaptability and versatility of this SNN and paves the way for future neural network studies.

Correlation between Slow Electrical Activity and Brain Neural Networks in Patients with Chronic Cerebral Ischemia

Correlation between Slow Electrical Activity and Brain Neural Networks in Patients with Chronic Cerebral Ischemia

The Characteristics of Entorhinal Cortex Functional Connectivity in Alzheimer's Disease Patients with Depression.

Depression is one of the most common neuropsychiatric symptoms of Alzheimer's disease (AD) which decreases the life quality of both patients and caregivers. There are currently no effective drugs. It is therefore important to explore the pathogenesis of depression in AD patients. The present study aimed to investigate the characteristics of the entorhinal cortex (EC) functional connectivity (FC) in the whole brain neural network of AD patients with depression (D-AD). Twenty-four D-AD patients, 14 AD patients without depression (nD-AD), and 20 healthy controls underwent resting-state functional magnetic resonance imaging. We set the EC as the seed and used FC analysis. One-way analysis of variance was used to examine FC differences among the three groups. Using the left EC as the seed point, there were FC differences among the three groups in the left EC-inferior occipital gyrus. Using the right EC as the seed point, there were FC differences among the three groups in the right EC-middle frontal gyrus, -superior parietal gyrus, -superior medial frontal gyrus, and -precentral gyrus. Compared with the nD-AD group, the D-AD group had increased FC between the right EC and right postcentral gyrus. Asymmetry of FC in the EC and increased FC between the EC and right postcentral gyrus may be important in the pathogenesis of depression in AD.

Forecasting macroscopic dynamics in adaptive Kuramoto network using reservoir computing.

Forecasting a system's behavior is an essential task encountering the complex systems theory. Machine learning offers supervised algorithms, e.g., recurrent neural networks and reservoir computers that predict the behavior of model systems whose states consist of multidimensional time series. In real life, we often have limited information about the behavior of complex systems. The brightest example is the brain neural network described by the electroencephalogram. Forecasting the behavior of these systems is a more challenging task but provides a potential for real-life application. Here, we trained reservoir computer to predict the macroscopic signal produced by the network of phase oscillators. The Lyapunov analysis revealed the chaotic nature of the signal and reservoir computer failed to forecast it. Augmenting the feature space using Takkens' theorem improved the quality of forecasting. RC achieved the best prediction score when the number of signals coincided with the embedding dimension estimated via the nearest false neighbors method. We found that short-time prediction required a large number of features, while long-time prediction utilizes a limited number of features. These results refer to the bias-variance trade-off, an important concept in machine learning.

Simultaneous electroencephalography-functional magnetic resonance imaging for assessment of human brain function.

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (MRI) have long been used as tools to examine brain activity. Since both methods are very sensitive to changes of synaptic activity, simultaneous recording of EEG and fMRI can provide both high temporal and spatial resolution. Therefore, the two modalities are now integrated into a hybrid tool, EEG-fMRI, which encapsulates the useful properties of the two. Among other benefits, EEG-fMRI can contribute to a better understanding of brain connectivity and networks. This review lays its focus on the methodologies applied in performing EEG-fMRI studies, namely techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. We will investigate simultaneous resting-state and task-based EEG-fMRI studies and discuss their clinical and technological perspectives. Moreover, it is established that the brain regions affected by a task-based neural activity might not be limited to the regions in which they have been initiated. Advanced methods can help reveal the regions responsible for or affected by a developed neural network. Therefore, we have also looked into studies related to characterization of structure and dynamics of brain networks. The reviewed literature suggests that EEG-fMRI can provide valuable complementary information about brain neural networks and functions.

Biomemristors-based synaptic devices for artificial intelligence applications

With the advent of high-tech eras with big data, artificial intelligence and 5G communications, people have higher and higher requirements for computer performance. The traditional von Neumann architecture, whose principle is the separation of the central processing unit (CPU) and memory, not only limits the performance of the computer, but also causes a lot of energy consumption. The next generation of brain-inspired computing chips promises to break the von Neumann bottleneck by simulating the brain's neural networks, enabling a new computer architecture known as neuromorphic computing. Memristors have been found to be one of the best pieces of hardware for neuromorphic computing and the best components for building artificial neural networks. This review systematically summarizes the research progress of biomemristors as synaptic devices. Memristors and bio-synapses are first introduced, and then the research progress of biomemristors based on biomaterials and polymers is reported. Finally, the application prospects and challenges of biomemristors in synapses are pointed out. This review provides a research perspective for the application of biomemristors-based synaptic devices in artificial intelligence.

Targeting attack hypergraph networks

In modern systems, from brain neural networks to social group networks, pairwise interactions are not sufficient to express higher-order relationships. The smallest unit of their internal function is not composed of a single functional node but results from multiple functional nodes acting together. Therefore, researchers adopt the hypergraph to describe complex systems. The targeted attack on random hypergraph networks is still a problem worthy of study. This work puts forward a theoretical framework to analyze the robustness of random hypergraph networks under the background of a targeted attack on nodes with high or low hyperdegrees. We discovered the process of cascading failures and the giant connected cluster (GCC) of the hypergraph network under targeted attack by associating the simple mapping of the factor graph with the hypergraph and using percolation theory and generating function. On random hypergraph networks, we do Monte-Carlo simulations and find that the theoretical findings match the simulation results. Similarly, targeted attacks are more effective than random failures in disintegrating random hypergraph networks. The threshold of the hypergraph network grows as the probability of high hyperdegree nodes being deleted increases, indicating that the network's resilience becomes more fragile. When considering real-world scenarios, our conclusions are validated by real-world hypergraph networks. These findings will help us understand the impact of the hypergraph's underlying structure on network resilience.

FMRI Findings in Cortical Brain Networks Interactions in Migraine Following Repetitive Transcranial Magnetic Stimulation.

BackgroundRepetitive transcranial magnetic stimulation (rTMS) is one of the high-potential non-pharmacological methods for migraine treatment. The purpose of this study is to define the neuroimaging markers associated with rTMS therapy in patients with migraine based on data from functional MRI (fMRI).Materials and MethodsA total of 19 patients with episodic migraine without aura underwent a 5-day course of rTMS of the fronto-temporo-parietal junction bilaterally, at 10 Hz frequency and 60% of motor threshold response of 900 pulses. Resting-state functional MRI (1.5 T) and a battery of tests were carried out for each patient to clarify their diagnosis, qualitative and quantitative characteristics of pain, and associated affective symptoms. Changes in functional connectivity (FC) in the brain's neural networks before and after the treatment were identified through independent components analysis.ResultsOver the course of therapy, we observed an increase in FC of the default mode network within it, with pain system components and with structures of the visual network. We also noted a decrease in FC of the salience network with sensorimotor and visual networks, as well as an increase in FC of the visual network. Besides, we identified 5 patients who did not have a positive response to one rTMS course after the first week of treatment according to the clinical scales results, presumably because of an increasing trend of depressive symptoms and neuroimaging criteria for depressive disorder.ConclusionsOur results show that a 5-day course of rTMS significantly alters the connectivity of brain networks associated with pain and antinociceptive brain systems in about 70% of cases, which may shed light on the neural mechanisms underlying migraine treatment with rTMS.

Emotions Modulate Affordances-Related Motor Responses: A Priming Experiment.

Traditionally, research on affordances and emotions follows two separate routes. For the first time, this article explicitly links the two phenomena by investigating whether, in a discrimination task (artifact vs. natural object), the motivational states induced by emotional images can modulate affordances-related motor response elicited by dangerous and neutral graspable objects. The results show faster RTs: (i) for both neutral and dangerous objects with neutral images; (ii) for dangerous objects with pleasant images; (iii) for neutral objects with unpleasant images. Overall, these data support a significant effect of emotions on affordances. The article also proposes a brain neural network underlying emotions and affordance interplay.

Psychiatric and Mental Health Nursing based Nursing Students' coordination function Brain Neural Network Design: From a Brain Science Perspective

Psychiatric and Mental Health Nursing based Nursing Students' coordination function Brain Neural Network Design: From a Brain Science Perspective

Long-Tailed Characteristics of Neural Activity Induced by Structural Network Properties

Over the past few decades, neuroscience studies have elucidated the structural/anatomical network characteristics in the brain and their associations with functional networks and the dynamics of neural activity. These studies have been carried out at multiple spatial-temporal scale levels, including spikes at the neural microcircuit level, neural activity at the intra-brain regional level, and neural interactions at the whole-brain network level. One of the structural and functional neural characteristics widely observed among large spatial-temporal scale ranges is long-tail distribution, typified as power-low distribution, gamma distribution, and log-normal distribution. In particular, long-tailed distributions found in excitatory postsynaptic potentials (EPSP) induce various types of neural dynamics and functions. We reviewed recent studies on neural dynamics produced by the structural long-tailed characteristics of brain neural networks. In particular, the spiking neural network with a log-normal EPSP distribution was first introduced for the essential factors to produce spontaneous activity and was extended and utilized for studies on the association of neural dynamics with the network topology depending on EPSP amplitude. Furthermore, the characteristics of the response to a steady stimulus and its dependence on E/I balance, which are widely observed under pathological conditions, were described by the spiking neural networks with EPSP long-tailed distribution. Moreover, this spiking neural network has been utilized in modeling studies of mutual interactions among local microcircuit circuits. In future studies, the implementation of more global brain network architectures in modeling studies might reveal the mechanisms by which brain dynamics and brain functions emerge from the whole brain network architecture.

Different Approximation Methods for Calculation of Integrated Information Coefficient in the Brain during Instrumental Learning.

The amount of integrated information, , proposed in an integrated information theory (IIT) is useful to describe the degree of brain adaptation to the environment. However, its computation cannot be precisely performed for a reasonable time for time-series spike data collected from a large count of neurons.. Therefore, was only used to describe averaged activity of a big group of neurons, and the behavior of small non-brain systems. In this study, we reported on ways for fast and precise calculation using different approximation methods for Φ calculation in neural spike data, and checked the capability of to describe a degree of adaptation in brain neural networks. We show that during instrumental learning sessions, all applied approximation methods reflect temporal trends of in the rat hippocampus. The value of is positively correlated with the number of successful acts performed by a rat. We also show that only one subgroup of neurons modulates their Φ during learning. The obtained results pave the way for application of to investigate plasticity in the brain during the acquisition of new tasks.

How the Brain Dynamically Constructs Sentence-Level Meanings From Word-Level Features.

How are words connected to the thoughts they help to express? Recent brain imaging studies suggest that word representations are embodied in different neural systems through which the words are experienced. Building on this idea, embodied approaches such as the Concept Attribute Representations (CAR) theory represents concepts as a set of semantic features (attributes) mapped to different brain systems. An intriguing challenge to this theory is that people weigh concept attributes differently based on context, i.e., they construct meaning dynamically according to the combination of concepts that occur in the sentence. This research addresses this challenge through the Context-dEpendent meaning REpresentations in the BRAin (CEREBRA) neural network model. Based on changes in the brain images, CEREBRA quantifies the effect of sentence context on word meanings. Computational experiments demonstrated that words in different contexts have different representations, the changes observed in the concept attributes reveal unique conceptual combinations, and that the new representations are more similar to the other words in the sentence than to the original representations. Behavioral analysis further confirmed that the changes produced by CEREBRA are actionable knowledge that can be used to predict human responses. These experiments constitute a comprehensive evaluation of CEREBRA's context-based representations, showing that CARs can be dynamic and change based on context. Thus, CEREBRA is a useful tool for understanding how word meanings are represented in the brain, providing a framework for future interdisciplinary research on the mental lexicon.

Regional Homogeneity in Patients With Mild Cognitive Impairment: A Resting-State Functional Magnetic Resonance Imaging Study.

ObjectiveTo analyze the potential changes in brain neural networks in resting state functional magnetic resonance imaging (rs-fMRI) scans by regional homogeneity (ReHo) in patients with mild cognitive impairment (MCI).MethodsWe recruited and selected 24 volunteers, including 12 patients (6 men and 6 women) with MCI and 12 healthy controls matched by age, sex, and lifestyle. All subjects were examined with rs-fMRI to evaluate changes in neural network connectivity, and the data were analyzed by ReHo method. Correlation analysis was used to investigate the relationship between ReHo values and clinical features in different brain regions of MCI patients. The severity of MCI was determined by the Mini-Mental State Examination (MMSE) scale.ResultsThe signals of the right cerebellum areas 4 and 5, left superior temporal, right superior temporal, left fusiform, and left orbital middle frontal gyri in the patient group were significantly higher than those in the normal group (P < 0.01 by t-test of paired samples). The signal intensity of the right inferior temporal and left inferior temporal gyri was significantly lower than that of the normal group (P < 0.01). The ReHO value for the left inferior temporal gyrus correlated negatively with disease duration, and the value for the right inferior temporal gyrus correlated positively with MMSE scores.ConclusionMild cognitive impairment in patients with pre- Alzheimer’s disease may be related to the excitation and inhibition of neural networks in these regions. This may have a certain guiding significance for clinical diagnosis.

Software Development, Configuration, Monitoring, and Management of Artificial Neural Networks

With the increasing demand for software systems, the software development industry is also developing rapidly. With the development of information technology, the more functions of the software, the more valuable it is, so the function design of the software becomes more complicated and difficult. The design of software system functions is increasingly large and complex. Scientific and effective use of software configuration management can well deal with collaborative work problems such as version management and change control in the software development process. In the process of software development and configuration, there will always be many problems that are difficult to detect. For example, when inputting the program code, there are not always some letter or space errors, and these errors are difficult to detect in time. For this reason, we need to establish a monitoring and management system for software development. As a computing model of human brain neural network, the artificial neural network can play the role of monitoring and management when it is applied to software development and configuration, which provides support for the security and scientificity of software development and configuration systems. This study studies the role and effectiveness of an artificial neural network in the monitoring and management of software development and configuration and validates it through experiments. The experimental results show that the artificial neural network has a strong ability to identify the problems in the software development configuration, which can improve the software development efficiency by at least 20%. It can improve the quality of software development and then improve the life cycle of software.

L1-norm based time-varying brain neural network and its application to dynamic analysis for motor imagery

Objective . Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface offers a promising way to improve the efficiency of motor rehabilitation and motor skill learning. In recent years, the power of dynamic network analysis for MI classification has been proved. In fact, its usability mainly depends on the accurate estimation of brain connection. However, traditional dynamic network estimation strategies such as adaptive directed transfer function (ADTF) are designed in the L2-norm. Usually, they estimate a series of pseudo connections caused by outliers, which results in biased features and further limits its online application. Thus, how to accurately infer dynamic causal relationship under outlier influence is urgent. Approach . In this work, we proposed a novel ADTF, which solves the dynamic system in the L1-norm space (L1-ADTF), so as to restrict the outlier influence. To enhance its convergence, we designed an iteration strategy with the alternating direction method of multipliers, which could be used for the solution of the dynamic state-space model restricted in the L1-norm space. Furthermore, we compared L1-ADTF to traditional ADTF and its dual extension across both simulation and real EEG experiments. Main results . A quantitative comparison between L1-ADTF and other ADTFs in simulation studies demonstrates that fewer bias errors and more desirable dynamic state transformation patterns can be captured by the L1-ADTF. Application to real MI EEG datasets seriously noised by ocular artifacts also reveals the efficiency of the proposed L1-ADTF approach to extract the time-varying brain neural network patterns, even when more complex noises are involved. Significance . The L1-ADTF may not only be capable of tracking time-varying brain network state drifts robustly but may also be useful in solving a wide range of dynamic systems such as trajectory tracking problems and dynamic neural networks.

Brain neural network, development, microbiome, microbial toxins and COVID-19

Although almost 2 years have passed since the beginning of the coronavirus disease 2019 (COVID-19) pandemic in the world, there is still a threat to the health of people at risk and patients. Specialists in various sciences conduct various researches in order to eliminate or reduce the problems caused by this disease. Neural network science plays a vital role in this regard. It is important to note the key points of neuro-microbial involvement in the diagnosis and management of COVID-19 therapy by physicians and patients whose nervous systems are challenged. The relationship between COVID-19, microbiome and the profile of microbial toxins in the body is one of the factors that can directly or indirectly play a key role in the body's resistance to Covid-19 and changes in the neural network of the brain. In this article, we introduce the relationship and behavioral and mood problems that can result from neuronal changes. In linking the components of this network, artificial intelligence (AI), machine learning (ML) and data mining (DM) can be important strategies to assist health providers to choose best decision based on patient’s history.

Flexible Neural Network Realized by the Probabilistic SiOx Memristive Synaptic Array for Energy-Efficient Image Learning.

The human brain's neural networks are sparsely connected via tunable and probabilistic synapses, which may be essential for performing energy‐efficient cognitive and intellectual functions. In this sense, the implementation of a flexible neural network with probabilistic synapses is a first step toward realizing the ultimate energy‐efficient computing framework. Here, inspired by the efficient threshold‐tunable and probabilistic rod‐to‐rod bipolar synapses in the human visual system, a 16 × 16 crossbar array comprising the vertical form of gate‐tunable probabilistic SiO x memristive synaptic barristor utilizing the Si/graphene heterojunction is designed and fabricated. Controllable stochastic switching dynamics in this array are achieved via various input voltage pulse schemes. In particular, the threshold tunability via electrostatic gating enables the efficient in situ alteration of the probabilistic switching activation (P Act ) from 0 to 1.0, and can even modulate the degree of the P Act change. A drop‐connected algorithm based on the P Act is constructed and used to successfully classify the shapes of several fashion items. The suggested approach can decrease the learning energy by up to ≈2,116 times relative to that of the conventional all‐to‐all connected network while exhibiting a high recognition accuracy of ≈93 %.