Efficient Information Processing Research Articles

The dynamic shaping of local cortical circuitry by sex and age, and its relation to pattern comparison processing speed.

Previous resting-state functional magnetic resonance imaging (fMRI) studies have shown that the strength of local neural interactions decreases with distance. Here, we extend that line of research to evaluate effects of sex and age on local cortical circuitry in six cortical areas (superior frontal, precentral, postcentral, superior parietal, inferior parietal, and lateral occipital) using data acquired from 1,054 healthy young adults who participated in the Human Connectome Project. We confirmed previous findings that the strength of zero-lag correlations between prewhitened, resting-state, blood level oxygenation-dependent (BOLD) fMRI time series decreased with distance locally and documented that the rate of decrease with distance (spatial steepness) 1) was progressively lower from anterior to posterior areas, 2) was greater in women, especially in anterior areas, 3) increased with age, particularly for women, 4) was significantly correlated with percent inhibition, and 5) was positively and highly significantly correlated with pattern comparison processing speed (PCPS). A hierarchical tree clustering analysis of this dependence of PCPS on spatial steepness revealed a differential organization in processing that information between the two hemispheres, namely, a more independent vs. a more integrative processing in the left and right hemispheres, respectively. These findings document sex and age differences in dynamic local cortical interactions and provide evidence that spatial sharpening of these interactions may underlie cognitive processing speed differently organized in the two hemispheres.NEW & NOTEWORTHY Sex and age significantly affect shaping of local cortical interactions that are more limited in women and older brains. The net result is an increase in local spatial steepness of interactions, leading to a reduction of overlap among local ensembles and, hence, a more efficient information processing and an increase in the number of independent local cortical "processors." Remarkably, cognitive processing speed was positively associated with local spatial steepness, in keeping with the reasoning earlier.

Temporal Pattern Coding in Ionic Memristor‐Based Spiking Neurons for Adaptive Tactile Perception

AbstractBiological neurons encode signals through firing voltage spike trains having unique temporal patterns, enabling efficient information representation and processing. Realization of these rich neuronal firing characteristics in a single electronic device, without circuitry and software assistance, promise compact and functional neuromorphic hardware for advanced artificial intelligence applications. Here, a Pt/Co3O4‐x/ITO‐based ionic memristor is reported that can faithfully produce voltage spike trains exhibiting diverse temporal patterns of biological neurons, under electric current stimulation. The spiking behaviors stem from the redistribution of ions in the device, governed by the current induced electric field and Joule heating effects. Tonic, phasic, burst, and adaptive firing patterns of neurons are demonstrated. Particularly, the adaptive firing characteristics allow the memristor to reduce the response to invariant current stimulation and to respond to current changes with enhanced sensitivity, implementing neuronal adaptive coding function. Integrating such memristors with pressure sensors yields an artificial tactile sensory system that can adaptively perceive small pressure variations in the presence of strong static pressure backgrounds, enabling accurate identification of touched objects in ever‐changing environments. This work opens up an avenue toward advanced neuromorphic hardware for smart neural prosthetics and bionic robotics applications.

Percolation may explain efficiency, robustness, and economy of the brain.

The brain consists of billions of neurons connected by ultra-dense synapses, showing remarkable efficiency, robust flexibility, and economy in information processing. It is generally believed that these advantageous properties are rooted in brain connectivity; however, direct evidence remains absent owing to technical limitations or theoretical vacancy. This research explores the origins of these properties in the largest yet brain connectome of the fruit fly. We reveal that functional connectivity formation in the brain can be explained by a percolation process controlled by synaptic excitation-inhibition (E/I) balance. By increasing the E/I balance gradually, we discover the emergence of these properties as byproducts of percolation transition when the E/I balance arrives at 3:7. As the E/I balance keeps increase, an optimal E/I balance 1:1 is unveiled to ensure these three properties simultaneously, consistent with previous in vitro experimental predictions. Once the E/I balance reaches over 3:2, an intrinsic limitation of these properties determined by static (anatomical) brain connectivity can be observed. Our work demonstrates that percolation, a universal characterization of critical phenomena and phase transitions, may serve as a window toward understanding the emergence of various brain properties.

Information Visualization Design of Web under the Background of Big Data

With the rapid development of the Internet, the information on the Internet presents an explosive growth. Cloud computing and big data analysis technology based on Internet information rise accordingly. However, all web pages contain not only important information but also the noise information irrelevant to the subject information. They seriously affect the accuracy of information extraction, so the research of web page information extraction technology arises at the historic moment and becomes the research hotspot. The quality of web page text information will directly affect the accuracy of later information processing and decision-making. If we can accurately evaluate the information of the web pages captured from the Internet and classify the extracted web pages according to the corresponding characteristics, we can not only improve the efficiency of information processing, but also improve the practical value of the information decision-making system. From the practical application requirements and user-friendly operation point of view, the information visualization of web design based on big data is studied in this paper. Specifically, the system designed in this paper improves the traditional template-based web information extraction method, establishes a web information extraction rule scheme combined with templates, and achieves the goal of web information extraction rule selection and template generation in the visual environment. Finally, the visualization algorithm based on T-SNE verifies the effectiveness of the web page information visualization algorithm designed in this paper.

Extending the spin coherence lifetimes of Er3+167:Y2SiO5 at subkelvin temperatures

${\mathrm{Er}}^{3+}\text{:}{\mathrm{Y}}_{2}{\mathrm{SiO}}_{5}$ is a material of particular interest due to its suitability for telecom-band quantum memories and quantum transducers interfacing optical communication with quantum computers working in the microwave regime. Extending the coherence lifetimes of the electron spins and the nuclear spins is essential for implementing efficient quantum information processing based on such hybrid electron-nuclear spin systems. The electron spin coherence time of ${\mathrm{Er}}^{3+}\text{:}{\mathrm{Y}}_{2}{\mathrm{SiO}}_{5}$ is so far limited to several microseconds, and there are significant challenges in optimizing coherence lifetimes simultaneously for both the electron and nuclear spins. Here we perform a pulsed-electron-nuclear-double-resonance investigation for an ${\mathrm{Er}}^{3+}$-doped material at subkelvin temperatures. At the lowest working temperature, the electron spin coherence time reaches 290 $\ifmmode\pm\else\textpm\fi{}$ 17 $\mathrm{\ensuremath{\mu}}\mathrm{s}$, which has been enhanced by 40 times compared with the previous results. In the subkelvin regime, a rapid increase in the nuclear spin coherence time is observed, and the longest coherence time of 738 $\ifmmode\pm\else\textpm\fi{}$ 6 $\mathrm{\ensuremath{\mu}}\mathrm{s}$ is obtained. These extended coherence lifetimes could be valuable resources for further applications of ${\mathrm{Er}}^{3+}\text{:}{\mathrm{Y}}_{2}{\mathrm{SiO}}_{5}$ in fiber-based quantum networks.

Development of methodological principles of routing in networks of special communication in the conditions of fire damage and radio electronic flow

The object of research is a system of special communication. Decision making support systems (DMSS) are actively used in all spheres of human life. They are especially common in the processing of large data sets, forecasting processes, providing information support in the decision-making process by decision-makers. Systems of analysis of information transmission in special purpose radio communication systems are no exception. However, there are a number of problems in the transmission of information, namely: the transmission of information takes place in a complex electronic environment against the background of intentional and natural interference; elements of the radio communication system are the objects of primary fire damage due to high radio visibility for radio intelligence. The best solution in this situation is to integrate with the data of the information system analysis of the electronic environment, artificial neural networks and the ant algorithm. Their advantage is also the ability to work in real time and quickly adapt to specific situations. Therefore, in this paper the methodological principles of routing in special communication networks in the conditions of fire damage and electronic suppression are developed. Improving the efficiency of information processing (reducing error) evaluation is achieved through the use of evolving neuro-fuzzy artificial neural networks; learning not only the synaptic weights of the artificial neural network, but also the type and parameters of the membership function. Efficiency of information processing is also achieved through training in the architecture of artificial neural networks; taking into account the type of uncertainty of the information to be assessed; synthesis of rational structure of fuzzy cognitive model. It reduces the computational complexity of decision-making; absence of accumulation of an error of training of artificial neural networks as a result of processing of the information arriving on an input of artificial neural networks. The approbation of the use of the offered technique on the example of the estimation of information transfer in the conditions of influence of destabilizing factors is carried out. This example showed an increase in the efficiency of evaluation at the level of 15–25 % on the efficiency of information processing.

Development of methodological principles of routing in networks of special communication in conditions of fire storm and radio-electronic suppression

Decision making support systems are actively used in the processing of large data sets, process forecasting, providing information support to the decision-making process by decision-makers. However, there are problems with the transmission of information: the transmission of information takes place in a complex electronic environment against the background of interference; radio communication systems are the objects of primary fire damage due to high radio visibility. This article develops the methodological principles of routing in special communication networks in the conditions of fire damage and electronic suppression. The purpose of this research is to increase the efficiency of information transfer under the influence of destabilizing factors. The proposed methodological principles are based on the theory of artificial intelligence. The research presents a mathematical formulation of the problem of routing in special-purpose radio networks and developed a method of routing in special-purpose radio networks.
 The efficiency of information processing is achieved through training in the architecture of artificial neural networks; taking into account the type of uncertainty of the information to be assessed; use of the ant algorithm. The approbation of the use of the offered technique on the example of the estimation of information transfer in the conditions of influence of destabilizing factors is carried out. The proposed methodological principles should be used in the development of software for programmable devices of communication and in the modernization of existing and development of new radio communication devices. This example showed an increase in the efficiency of information transmission in radio communication systems at the level of 15–25 % on the criterion of efficiency

Developmental organization of neural dynamics supporting auditory perception

Purpose: A prominent view of language acquisition involves learning to ignore irrelevant auditory signals through functional reorganization, enabling more efficient processing of relevant information. Yet, few studies have characterized the neural spatiotemporal dynamics supporting rapid detection and subsequent disregard of irrelevant auditory information, in the developing brain. To address this unknown, the present study modeled the developmental acquisition of cost-efficient neural dynamics for auditory processing, using intracranial electrocorticographic responses measured in individuals receiving standard-of-care treatment for drug-resistant, focal epilepsy. We also provided evidence demonstrating the maturation of an anterior-to-posterior functional division within the superior-temporal gyrus (STG), which is known to exist in the adult STG. Methods: We studied 32 patients undergoing extraoperative electrocorticography (age range: eight months to 28 years) and analyzed 2,039 intracranial electrode sites outside the seizure onset zone, interictal spike-generating areas, and MRI lesions. Patients were given forward (normal) speech sounds, backward-played speech sounds, and signal-correlated noises during a task-free condition. We then quantified sound processing-related neural costs at given time windows using high-gamma amplitude at 70–110 Hz and animated the group-level high-gamma dynamics on a spatially normalized three-dimensional brain surface. Finally, we determined if age independently contributed to high-gamma dynamics across brain regions and time windows. Results: Group-level analysis of noise-related neural costs in the STG revealed developmental enhancement of early high-gamma augmentation and diminution of delayed augmentation. Analysis of speech-related high-gamma activity demonstrated an anterior-to-posterior functional parcellation in the STG. The left anterior STG showed sustained augmentation throughout stimulus presentation, whereas the left posterior STG showed transient augmentation after stimulus onset. We found a double dissociation between the locations and developmental changes in speech sound-related high-gamma dynamics. Early left anterior STG high-gamma augmentation (i.e., within 200 ms post-stimulus onset) showed developmental enhancement, whereas delayed left posterior STG high-gamma augmentation declined with development. Conclusions: Our observations support the model that, with age, the human STG refines neural dynamics to rapidly detect and subsequently disregard uninformative acoustic noises. Our study also supports the notion that the anterior-to-posterior functional division within the left STG is gradually strengthened for efficient speech-sound perception after birth.

Learning different task spaces: how explored density aligns the Quiet Eye

In the current study, predictions of a theoretical account to the explanation of the Quiet Eye (QE) were investigated. To this end, by manipulating the learning environment, participants (n = 52) learned an underhand throwing task which required to explore task-solution spaces of low vs. high density over a 4-week training phase (640 training trials). Although throwing performance was improved, surprisingly, in posttest and retention test shorter QE durations were found. It is speculated that on a short-time learning scale this effect might be explained by more efficient information processing. Moreover, a trend was observed which suggests that—in line with the inhibition hypothesis—when exploring high-density task-solution spaces longer QE durations are required. However, the rather small effect sizes necessitate further research, which will allow to manipulate the response–effect mappings more directly as, for example, in virtual environments.

Bio-Inspired In-Sensor Compression and Computing Based on Phototransistors.

The biological nervous system possesses a powerful information processing capability, and only needs a partial signal stimulation to perceive the entire signal. Likewise, the hardware implementation of an information processing system with similar capabilities is of great significance, for reducing the dimensions of data from sensors and improving the processing efficiency. Here, it is reported that indium-gallium-zinc-oxide thin film phototransistors exhibit the optoelectronic switching and light-tunable synaptic characteristics for in-sensor compression and computing. Phototransistor arrays can compress the signal while sensing, to realize in-sensor compression. Additionally, a reservoir computing network can also be implemented via phototransistors for in-sensor computing. By integrating these two systems, a neuromorphic system for high-efficiency in-sensor compression and computing is demonstrated. The results reveal that even for cases where the signal is compressed by 50%, the recognition accuracy of reconstructed signal still reaches ≈96%. The work paves the way for efficient information processing of human-computer interactions and the Internet of Things.

Research on improvement of DPoS consensus mechanism in collaborative governance of network public opinion.

With the increasingly complex social situation, the problems of traditional online public opinion governance are increasingly serious. Especially the problem of transmission efficiency, public opinion data management and user information security of Internet users is urgently needed. Here, we design a functional infrastructure framework of the network public opinion collaborative governance model based on the blockchain with strong practicality and comprehensiveness. In order to reach the consensus mechanism requirements under the framework, the algorithm is improved on the basis of the defects of the traditional DPoS consensus algorithm. Considering time dynamic factors in the process of reaching consensus, the paper proposes a reputation-based voting model. Furthermore, the paper purposes a rewards and punishments incentive mechanism, and also designs a new method of counting votes. From the simulation results, it was found that after the improvement of the algorithm, the enthusiasm of node participation was significantly increased, the proportion of error nodes was significantly reduced, and the operating efficiency was significantly improved. It shows that the improved consensus algorithm we propose applies to public opinion governance can not only improve the security of the system with the reduce of false public opinion spreading, but also improve the efficiency of information processing, so it can be well applied to information sharing and public opinion governance scenarios.

Series Editorial: Military Communications and Networks

Making good decisions in a timely manner is intrinsic to the success of military operations. Critical decisions need to be reliably communicated and executed, and may depend on efficient processing of information from multiple sources. Thus, there are tough requirements on military communications and networks, such as tactical networks, operating in the face of adversaries toward the limits imposed by physics. In this context, the articles in this issue cover important topics of cloud processing at the tactical edge, resistance to jamming, and radio communication beyond line of sight.

Analysis of Legal Basis for Computer Cyber Security Review

The development of computer and information technology enables the in-depth application for human beings in the network and information application, and accordingly, more and more fields in human society are deeply associated with computer networks. The emergence of computer network enables human beings to obtain higher efficiency in information processing and transaction. However, computer network is endowed with virtuality, complexity and openness, which pose itself in internal and external security threats. At the same time, the speed of technological development is always faster than the updating and optimization of administrative regulations, which makes it difficult to guarantee cyber security in terms of legal basis. This paper discusses the legal basis of computer cyber security review with the hope to provide support for the relevant work and theoretical research of computer cyber security review.

Event-Driven Intrinsic Plasticity for Spiking Convolutional Neural Networks.

The biologically discovered intrinsic plasticity (IP) learning rule, which changes the intrinsic excitability of an individual neuron by adaptively turning the firing threshold, has been shown to be crucial for efficient information processing. However, this learning rule needs extra time for updating operations at each step, causing extra energy consumption and reducing the computational efficiency. The event-driven or spike-based coding strategy of spiking neural networks (SNNs), i.e., neurons will only be active if driven by continuous spiking trains, employs all-or-none pulses (spikes) to transmit information, contributing to sparseness in neuron activations. In this article, we propose two event-driven IP learning rules, namely, input-driven and self-driven IP, based on basic IP learning. Input-driven means that IP updating occurs only when the neuron receives spiking inputs from its presynaptic neurons, whereas self-driven means that IP updating only occurs when the neuron generates a spike. A spiking convolutional neural network (SCNN) is developed based on the ANN2SNN conversion method, i.e., converting a well-trained rate-based artificial neural network to an SNN via directly mapping the connection weights. By comparing the computational performance of SCNNs with different IP rules on the recognition of MNIST, FashionMNIST, Cifar10, and SVHN datasets, we demonstrate that the two event-based IP rules can remarkably reduce IP updating operations, contributing to sparse computations and accelerating the recognition process. This work may give insights into the modeling of brain-inspired SNNs for low-power applications.

Aggregation of multidimensional data for the decision support process for the management of microgrids with renewable energy sources

The object of research is the process of processing and storing data when making decisions on managing the life cycle of electricity generation and consumption in microgrids with renewable energy sources. The prospects of the study are due to the fact that in order to provide a full-fledged decision support process in the management of microgrids with renewable energy sources, it is necessary to consolidate and manipulate multidimensional data in multithreading and online information processing. To solve the problem, the theoretical methods of analysis, abstraction, induction and deduction were used. To ensure multidimensionality and multithreading of data processing, it is proposed to develop a data warehouse based on the snowflake data model. Efficiency of information processing in real time is provided by an operational database built on the principle of OLTP. The organization of the joint work of the data warehouse with the operational database, the consolidation and manipulation of data is provided by triggers. The result of the work is a data warehouse that will be used in the decision support system for managing energy microgrids, which will improve the efficiency of data processing and storage. This is achieved by combining the work of a centralized data warehouse with an operational database, as well as the use of a separate data mart for each user of the system. The practical significance of the work lies in the fact that the data warehouse will become part of the decision support system for processing information about the life cycle of energy in the management of energy infrastructure. Compared to using a single database for a decision support system, this approach ensures the speed of working with data and allows differentiating between the use of a data warehouse for analytics and data manipulation operations. The data warehouse was deployed in a cloud environment on the Amazon Web Services (AWS) platform and the Amazon Relational Database Service (Amazon RDS) web service. Secure access to client data is implemented using data marts.

A Secure and Stable Humanoid Healthcare Information Processing and Supervisory Method with IoT-Based Sensor Network

Nowadays, the Internet of things- (IoT-) based emerging technologies are playing a very pervasive role in healthcare systems where information is required on a real-time basis. These systems may provide real-time alerts with continuous monitoring to the concern in the odd conditions. Thus, these systems help the elderly and ill person with a good quality life and reduce the number of caregiver persons. Based on the above facts, this paper proposes a secure, efficient, and stable humanoid healthcare information processing and supervisory method with an IoT-based sensor network. Here, five different parameters specifically network residual energy, node density, node average energy, number of neighbors surrounded of a node, and distance between the sensor node and sink are considered for effective utilization of the energy of the sensor nodes. This method also reduces the communication distance of the nodes which helps in proficient data collection from the human body. The performance of the proposed and the existing work is considered the stability period, number of alive and dead nodes, total energy consumption, number of packets send to cluster heads, and base station metrics with two different monitoring areas. Thus, the stability period of the proposed method is increased by 144.52% with respect to the existing method protocol.

Sex interaction of white matter microstructure and verbal IQ in corpus callosum in typically developing children and adolescents

BackgroundChildhood is an extremely important time for neural development that has a critical role in human intelligence. Efficient information processing is crucial for higher intelligence, so the intra- or inter-hemispheric interaction is vital. However, the relationship between neuroanatomical connections and intelligence in typically developing children, as well as sex differences in this relationship, remains unknown. MethodsParticipants were 253 typically developing children (121 boys and 132 girls) aged 5–18. We acquired diffusion tensor imaging data and intelligence using an age-appropriate version of the IQ test; Wechsler Intelligence Scale for Children (WISC) or Wechsler Adult Intelligence Scale (WAIS). We conducted whole-brain multiple regression analysis to investigate the association between fractional anisotropy (FA), which reflects white matter microstructural properties, and each composite score of IQ test (full-scale IQ, performance IQ, and verbal IQ). ResultsFA was positively correlated with full-scale IQ in bilateral inferior occipitofrontal fasciculus, genu, and splenium of corpus callosum (CC). FA in the right superior longitudinal fasciculus, bilateral inferior longitudinal fasciculus, and splenium of CC were also positively correlated with performance IQ. Furthermore, we found significant sex interaction between FA in the CC and verbal IQ. FA was positively correlated in boys, and negatively correlated in girls. ConclusionResults suggest that efficient anatomical connectivity between parietal and frontal regions is crucial for children’s intelligence. Moreover, inter-hemispheric connections play a critical role in verbal abilities in boys.

Retinal Glutamate Neurotransmission: From Physiology to Pathophysiological Mechanisms of Retinal Ganglion Cell Degeneration.

Glutamate neurotransmission and metabolism are finely modulated by the retinal network, where the efficient processing of visual information is shaped by the differential distribution and composition of glutamate receptors and transporters. However, disturbances in glutamate homeostasis can result in glutamate excitotoxicity, a major initiating factor of common neurodegenerative diseases. Within the retina, glutamate excitotoxicity can impair visual transmission by initiating degeneration of neuronal populations, including retinal ganglion cells (RGCs). The vulnerability of RGCs is observed not just as a result of retinal diseases but has also been ascribed to other common neurodegenerative and peripheral diseases. In this review, we describe the vulnerability of RGCs to glutamate excitotoxicity and the contribution of different glutamate receptors and transporters to this. In particular, we focus on the N-methyl-d-aspartate (NMDA) receptor as the major effector of glutamate-induced mechanisms of neurodegeneration, including impairment of calcium homeostasis, changes in gene expression and signalling, and mitochondrial dysfunction, as well as the role of endoplasmic reticular stress. Due to recent developments in the search for modulators of NMDA receptor signalling, novel neuroprotective strategies may be on the horizon.

Assessing Human Information Processing in Lending Decisions: A Machine Learning Approach

ABSTRACTEffective financial reporting requires efficient information processing. This paper studies factors that determine efficient information processing. I exploit a unique small business lending setting where I am able to observe the entire codified demographic and accounting information set that loan officers use to make decisions. I decompose the loan officers’ decisions into a part driven by codified hard information and a part driven by uncodified soft information. I show that a machine learning model substantially outperforms loan officers in processing hard information. Loan officers can only process a sparse set of useful hard information identified by the machine learning model and focus their attention on salient signals such as large jumps in cash flows. However, the loan officers use salient hard information as “red flags” to highlight where to acquire more soft information. This result suggests that salient information is an attention allocation device: It guides humans to allocate their limited cognitive resources to acquire soft information, a task in which humans have an advantage over machines.

Effects of Transcranial Direct Current Stimulation on Attentional Bias to Methamphetamine Cues and Its Association With EEG-Derived Functional Brain Network Topology.

Although transcranial direct current stimulation (tDCS) has shown to potentially mitigate drug craving and attentional bias to drug-related stimuli, individual differences in such modulatory effects of tDCS are less understood. In this study, we aimed to investigate a source of the inter-subject variability in the tDCS effects that can be useful for tDCS-based treatments of individuals with methamphetamine (MA) use disorder (IMUD). Forty-two IMUD (all male) were randomly assigned to receive a single-session of either sham or real bilateral tDCS (anodal right/cathodal left) over the dorsolateral prefrontal cortex. The tDCS effect on MA craving and biased attention to drug stimuli were investigated by quantifying EEG-derived P3 (a measure of initial attentional bias) and late positive potential (LPP; a measure of sustained motivated attention) elicited by these stimuli. To assess the association of changes in P3 and LPP with brain connectivity network (BCN) topology, the correlation between topology metrics, specifically those related to the efficiency of information processing, and the tDCS effect was investigated. The P3 amplitude significantly decreased following the tDCS session, whereas the amplitudes increased in the sham group. The changes in P3 amplitudes were significantly correlated with communication efficiency measured by BCN topology metrics (r = -0.47, P = .03; r = -0.49, P = .02). There was no significant change in LPP amplitude due to the tDCS application. These findings validate that tDCS mitigates initial attentional bias, but not the sustained motivated attention, to MA stimuli. Importantly, however, results also show that the individual differences in the effects of tDCS may be underpinned by communication efficiency of the BCN topology, and therefore, these BCN topology metrics may have the potential to robustly predict the effectiveness of tDCS-based interventions on MA craving and attentional bias to MA stimuli among IMUD.