Multiple Information Processing Research Articles

3D-integrated multilayered physical reservoir array for learning and forecasting time-series information

A wide reservoir computing system is an advanced architecture composed of multiple reservoir layers in parallel, which enables more complex and diverse internal dynamics for multiple time-series information processing. However, its hardware implementation has not yet been realized due to the lack of a high-performance physical reservoir and the complexity of fabricating multiple stacks. Here, we achieve a proof-of-principle demonstration of such hardware made of a multilayered three-dimensional stacked 3 × 10 × 10 tungsten oxide memristive crossbar array, with which we further realize a wide physical reservoir computing for efficient learning and forecasting of multiple time-series data. Because a three-layer structure allows the seamless and effective extraction of intricate three-dimensional local features produced by various temporal inputs, it can readily outperform two-dimensional based approaches extensively studied previously. Our demonstration paves the way for wide physical reservoir computing systems capable of efficiently processing multiple dynamic time-series information.

Differences in resting state functional connectivity relative to multiple sclerosis and impaired information processing speed.

Fifty-one percent of individuals with multiple sclerosis (MS) develop cognitive impairment (CI) in information processing speed (IPS). Although IPS scores are associated with health and well-being, neural changes that underlie IPS impairments in MS are not understood. Resting state fMRI can provide insight into brain function changes underlying impairment in persons with MS. We aimed to assess functional connectivity (FC) differences in (i) persons with MS compared to healthy controls (HC), (ii) persons with both MS and CI (MS-CI) compared to HC, (iii) persons with MS that are cognitively preserved (MS-CP) compared to HC, (iv) MS-CI compared to MS-CP, and (v) in relation to cognition within the MS group. We included 107 participants with MS (age 49.5 ± 12.9, 82% women), and 94 controls (age 37.9 ± 15.4, 66% women). Each participant was administered the Symbol Digit Modalities Test (SDMT) and underwent a resting state fMRI scan. The MS-CI group was created by applying a z-score cut-off of -1.5 to locally normalized SDMT scores. The MS-CP group was created by applying a z-score of ≥0. Control groups (HCMS-CI and HCMS-CP) were based on the nearest age-matched HC participants. A whole-brain ROI-to-ROI analysis was performed followed by specific contrasts and a regression analysis. Individuals with MS showed FC differences compared to HC that involved the cerebellum, visual and language-associated brain regions, and the thalamus, hippocampus, and basal ganglia. The MS-CI showed FC differences compared to HCMS-CI that involved the cerebellum, visual and language-associated areas, thalamus, and caudate. SDMT scores were correlated with FC between the cerebellum and lateral occipital cortex in MS. No differences were observed between the MS-CP and HCMS-CP or MS-CI and MS-CP groups. Our findings emphasize FC changes of cerebellar, visual, and language-associated areas in persons with MS. These differences were apparent for (i) all MS participants compared to HC, (ii) MS-CI subgroup and their matched controls, and (iii) the association between FC and SDMT scores within the MS group. Our findings strongly suggest that future work that examines the associations between FC and IPS impairments in MS should focus on the involvement of these regions.

Retracted: Correlation Analysis between Early Reading Amount and Expressive Ability of Young Children Aided by Multiple Information Processing Techniques under the Heuristic Pattern

Retracted: Correlation Analysis between Early Reading Amount and Expressive Ability of Young Children Aided by Multiple Information Processing Techniques under the Heuristic Pattern

Multiple Fano Resonances in a Metal–Insulator–Metal Waveguide for Nano-Sensing of Multiple Biological Parameters and Tunable Slow Light

A surface plasmonic waveguide made of metal–insulator–metal (MIM) capable of generating triple Fano resonances is proposed and numerically investigated for multi-biological parameter sensing as well as tunable slow light. The waveguide is made up of a bus waveguide with a silver baffle, a square split-ring cavity with a square center (SSRCSC), and a circular ring cavity with a square center (CRCSC). Based on the triple Fano resonances, human blood temperature and plasma concentration are measured simultaneously at different locations in the waveguide, and the maximum sensitivities were 0.25 nm/°C and 0.2 nm·L/g, respectively. Furthermore, the two biological parameters can be used to achieve tunable slow light, and it was found that the group delay responses to human blood temperature and plasma concentration all conformed to cubic functions. The MIM waveguide may have great applications in future nano-sensing of multiple biological parameters and information processing of optical chips or bio-optical chips.

Single trial variability in neural activity during a working memory task reveals multiple distinct information processing sequences

Successful encoding, maintenance, and retrieval of information stored in working memory requires persistent coordination of activity among multiple brain regions. It is generally assumed that the pattern of such coordinated activity remains consistent for a given task. Thus, to separate this task-relevant signal from noise, multiple trials of the same task are completed, and the neural response is averaged across trials to generate an event-related potential (ERP). However, from trial to trial, the neuronal activity recorded with electroencephalogram (EEG) is actually spatially and temporally diverse, conflicting with the assumption of a single pattern of activity for a given task. Here, we show that variability in neuronal activity among single time-locked trials arises from the presence of multiple forms of stimulus dependent synchronized activity (i.e., distinct ERPs). We develop a data-driven classification method based on community detection to identify three discrete spatio-temporal clusters, or subtypes, of trials with different patterns of activation that are further associated with differences in decision-making processes. These results demonstrate that differences in the patterns of neural activity during working memory tasks represent fluctuations in the engagement of distinct brain networks and cognitive processes, suggesting that the brain can choose from multiple mechanisms to perform a given task.

Eggshell Cutting Task Strategy for Robot that Handles Drill Based on Multiple Information Feedback of Force-sensing, Auditory-sensing, and Hand-position

Autonomous robots are expected to perform minute tasks that require skill for humans. In order for robots to achieve autonomous tasks, multiple sensory information processing and feedback strategies are indispensable. This study focuses on eggshell cutting task strategy that supports the exposure of eggshell membranes by a robot that handles micro drill. We propose the task strategy that robot performs discrete and continuous cutting on eggshell and the failure detection when the eggshell membrane is damaged. Finally, we show the experimental results of the autonomous eggshell cutting tasks by robot and the human-assisted eggshell peeling and eggshell membrane exposure. Besides, we consider when to stop the cutting task so as not to break the eggshell membrane.

Correlation Analysis between Early Reading Amount and Expressive Ability of Young Children Aided by Multiple Information Processing Techniques under the Heuristic Pattern

The cultivation of reading ability runs through a child’s life. Children are in a critical period of early reading ability formation. Only based on the actual development characteristics of children and adopting effective training methods, can early childhood educators better meet the needs of children’s long-term development. Based on the data of 22 classes in 7 public kindergartens in X City in Mainland of China, this study first compared the effects of two different teaching modes and then analyzed the relationship between early reading amount and expressive ability of children. Independent sample T test, OLS regression, and Lasso regression were used to demonstrate the correlation between early reading amount and expressive ability, and the possible mechanisms of family background are also explored. The results show the following: firstly, heuristic teaching mode has positive teaching effect. The effect quantities of heuristic teaching mode on transmission ability, communication ability, transfer ability, and generalization ability are 0.21, 0.25, 0.31, 0.30, and 0.32, respectively, which belong to medium intensity, indicating that heuristic teaching mode has a good intervention effect on improving students’ expression ability. Secondly, there is a high correlation between early reading and children’s expressive ability. Thirdly, there are two aspects of the influence of early reading amount on children’s expressive ability: explicit influence and implicit influence. Fourthly, the association between early reading and different presentation skills is not the same. To sum up, we should actively encourage the use of heuristic teaching mode, realize the heterogeneity of early reading amount, and promote the improvement of children’s expressive ability through both explicit and implicit paths.

Activation of oxytocin receptors in mouse GABAergic amacrine cells modulates retinal dopaminergic signaling

BackgroundOxytocin, secreted by oxytocin neurons in the hypothalamus, is an endogenous neuropeptide involved in modulating multiple sensory information processing pathways, and its roles in the brain have been associated with prosocial, maternal, and feeding-related behaviors. Visual information is necessary for initiating these behaviors, with the retina consisting of the first stage in the visual system mediating external stimulus perception. Oxytocin has been detected in the mammalian retina; however, the expression and possible function of oxytocin receptors (OxtR) in the retina remain unknown. Here, we explore the role of oxytocin in regulating visual information processing in the retina.ResultsWe observed that OxtR mRNA and protein are expressed in the mouse retina. With Oxtr-Cre transgenic mice, immunostaining, and fluorescence in situ hybridization, we found that OxtRs are mainly expressed in GABAergic amacrine cells (ACs) in both the inner nuclear layer (INL) and ganglion cell layer (GCL). Further immunoreactivity studies showed that GABAergic OxtR+ neurons are mainly cholinergic and dopaminergic neurons in the INL and are cholinergic and corticotrophin-releasing hormone neurons in the GCL. Surprisingly, a high level of Oxtr mRNAs was detected in retinal dopaminergic neurons, and exogenous oxytocin application activated dopaminergic neurons to elevate the retinal dopamine level. Relying on in vivo electroretinographic recording, we found that activating retinal OxtRs reduced the activity of bipolar cells via OxtRs and dopamine receptors.ConclusionsThese data indicate the functional expression of OxtRs in retinal GABAergic ACs, especially dopaminergic ACs, and expand the interactions between oxytocinergic and dopaminergic systems. This study suggests that visual perception, from the first stage of information processing in the retina, is modulated by hypothalamic oxytocin signaling.

Design of Artificial Intelligence Epidemic Big Data Prevention and Control Early Warning System

The artificial intelligence epidemic big data prevention and early warning system of the project includes client, cloud database, information acquisition module and information processing module. The client communicates with the cloud database, the cloud database connects with multiple information collection modules, and the cloud database communicates with multiple information processing modules. The client is used for backstage personnel to watch the information, As well as the feedback and display of background information, the cloud database is used to store the information entered by the information collection module, and the information processing module is used to compare and process the information entered by the information collection module. This project involves the field of epidemic prevention and early warning technology. The artificial intelligence epidemic big data prevention and control early warning system solves the problem that the existing epidemic big data early warning system is inconvenient for real-time detection of health monitoring, personnel positioning and trajectory tracking.

Urban traffic flow prediction techniques: A review

In recent decades, the development of transport infrastructure has had a great development, although traffic problems continue to spread due to increase due to the increase in the population in urban areas that require the use of these means of transport. This has led to increased problems related to congestion control, which has a direct impact on citizens: air pollution, fuel consumption, violation of traffic rules, noise pollution, accidents and loss of time. In Latin America, the disorderly growth of cities increases distances and routes, likewise, there is an accelerated increase in the number of cars and motorcycles, which increases the problem. In this sense, intelligent transport systems are an alternative to improve the traffic environment, they incorporate the internet of things and intelligent algorithms, for the collection of data from multiple sources and information processing, respectively, in order to improve the efficiency of the transport flow. However, the processing and modeling of traffic data is challenging due to the complexity of road networks, the space–time dependencies between them, and heterogeneous traffic patterns. In this review study, (i) the smart techniques used for the analysis of mobility data in the prediction of traffic flow in urban areas are grouped, likewise, (ii) the results of implementing said techniques are shown, in addition, (iii) The procedures performed are described and analyzed to understand the benefits and limitations of these smart techniques. Given the above, (iv) the data sets used in the literature and available for use are shown, in addition, (v) the quantifiable results of precision of the various techniques were compared, highlighting advantages and limitations, which allows us to (vi) identify the related challenges and, from there, (vii) propose a general taxonomy in which the knowledge acquired in this traffic flow review converges from a computational approach.

Reduction in Cognitive Processing Speed Surrounding Multiple Sclerosis Relapse.

ObjectiveThe purpose of this study was to explore the longitudinal relationship between multiple sclerosis (MS) relapses and information processing efficiency among persons with relapsing–remitting MS.MethodsWe conducted a Swedish nationwide cohort study of persons with incident relapsing–remitting MS (2001–2019). Relapse information and symbol digit modalities test (SDMT) scores were obtained from the Swedish MS Registry. Follow‐up was categorized into 2 periods based on relapse status: “relapse” (90 days pre‐relapse to 730 days post‐relapse, subdivided into 10 periods) and “remission.” Linear mixed models compared SDMT scores during the relapse periods to SDMT scores recorded during remission (reference) with results reported as β‐coefficients and 95% confidence intervals (CIs), adjusted for age, sex, SDMT type (written vs oral), time‐varying, disease‐modifying therapy exposure and sequence of SDMT.ResultsOver a mean (SD) follow‐up of 10.7 (4.3) years, 31,529 distinct SDMTs were recorded among 3,877 persons with MS. There was a significant decline in information processing efficiency that lasted from 30 days pre‐relapse up to 550 days post‐relapse, with the largest decline occurring 0 to 30 days post‐relapse (β‐coefficient: −4.00 (95% CI = −4.61 to −3.39), relative to the period of remission.InterpretationWe found evidence of cognitive change up to 1 month prior to relapse onset. The reduction in SDMT lasted 1.5 years and was clinically significant up to 3 months post‐relapse. These results suggest that the effects of a relapse on cognition are longer than previously thought and highlight the importance of reducing relapse rates as a potential means of preserving cognitive function. ANN NEUROL 2022;91:417–423

Towards the Idea of Molecular Brains.

How can single cells without nervous systems perform complex behaviours such as habituation, associative learning and decision making, which are considered the hallmark of animals with a brain? Are there molecular systems that underlie cognitive properties equivalent to those of the brain? This review follows the development of the idea of molecular brains from Darwin’s “root brain hypothesis”, through bacterial chemotaxis, to the recent discovery of neuron-like r-protein networks in the ribosome. By combining a structural biology view with a Bayesian brain approach, this review explores the evolutionary labyrinth of information processing systems across scales. Ribosomal protein networks open a window into what were probably the earliest signalling systems to emerge before the radiation of the three kingdoms. While ribosomal networks are characterised by long-lasting interactions between their protein nodes, cell signalling networks are essentially based on transient interactions. As a corollary, while signals propagated in persistent networks may be ephemeral, networks whose interactions are transient constrain signals diffusing into the cytoplasm to be durable in time, such as post-translational modifications of proteins or second messenger synthesis. The duration and nature of the signals, in turn, implies different mechanisms for the integration of multiple signals and decision making. Evolution then reinvented networks with persistent interactions with the development of nervous systems in metazoans. Ribosomal protein networks and simple nervous systems display architectural and functional analogies whose comparison could suggest scale invariance in information processing. At the molecular level, the significant complexification of eukaryotic ribosomal protein networks is associated with a burst in the acquisition of new conserved aromatic amino acids. Knowing that aromatic residues play a critical role in allosteric receptors and channels, this observation suggests a general role of π systems and their interactions with charged amino acids in multiple signal integration and information processing. We think that these findings may provide the molecular basis for designing future computers with organic processors.

Recent advances in nano-opto-electro-mechanical systems

Abstract Nano-opto-electro-mechanical systems (NOEMS), considered as new platforms to study electronic and mechanical freedoms in the field of nanophotonics, have gained rapid progress in recent years. NOEMS offer exciting opportunities to manipulate information carriers using optical, electrical, and mechanical degrees of freedom, where the flow of light, dynamics of electrons, and mechanical vibration modes can be explored in both classical and quantum domains. By exploiting NOEMS concepts and technologies, high speed and low-power consumption switches, high-efficiency microwave-optical conversion devices, and multiple quantum information processing functions can be implemented through on-chip integration. This review will introduce the principles of NOEMS, summarize the recent developments, and important achievements, and give a prospect for the further applications and developments in this field.

Valley pseudospin in monolayer MoSi2N4 and MoSi2As4

For a long time, two-dimensional (2D) hexagonal MoS2 was proposed as a promising material for valleytronic system. However, the limited size of growth and low carrier motilities in MoS2 restrict its further application. Very recently, a new kind of hexagonal 2D MXene, MoSi2N4, was successfully synthesized with large size, excellent ambient stability, and considerable hole mobility. In this paper, based on the first-principles calculations, we predict that the valley-contrast properties can be realized in monolayer MoSi2N4 and its derivative MoSi2As4. Beyond the traditional two-level valleys, the valleys in monolayer MoSi2As4 are multiple-folded, implying a new valley dimension. Such multiple-folded valleys can be described by a three-band low-power Hamiltonian. This study presents the theoretical advance and the potential applications of monolayer MoSi2N4 and MoSi2As4 in valleytronic devices, especially multiple information processing.

Roles of Fear of Happiness and Fragility of Happiness in Nigerian Military Personnel Workload

In the light of conflicts and Boko Haram insurgency facing Nigeria as a county and the involvement of the military in the fight against terrorists, the study of workload and happiness need to be researched more to gain greater acceptance and become one of the global indices on which nations’ military are measured and ranked. Our study anchors on multiple resource and information processing theory to assess the roles of fear and fragility of happiness in workload among Nigerian military personnel. Two hundred and twenty (220) Nigerian soldiers, drawn from Nigerian Army and Nigerian Air Force participated in this study. Three instruments , namely, Workload Scale, Fear of Happiness Scale and Fragility of Happiness Scale, were used to collect data from the participants. Results of hierarchical multiple regression analysis indicated that fear of happiness was not significantly associated with work load. Fragility of happiness positively predicted work load, indicating that military personnel who had greater fragility of happiness reported greater workload. It was concluded that workplace interventions and programs to curtail fragility of happiness would be helpful in reducing the perceptions of excess workload among Nigerian military personnel.

Segregated Co-activation Patterns in the Emergence of Decision Confidence During Visual Perception.

Visual metacognition—the introspection and evaluation of one’s own visual perceptual processes—is measured through both decision confidence and “metacognitive efficiency.” Metacognitive efficiency refers to an individual’s ability to accurately judge incorrect and correct decisions through confidence ratings given their task performance. Previous imaging studies in humans and nonhuman primates reported widely distributed brain regions being involved in decision confidence and metacognition. However, the neural correlates of metacognition are remarkably inconsistent across studies concerning spatial outline. Therefore, this study investigates the neural correlates of visual metacognition by examining co-activation across regions that scale with visual decision confidence. We hypothesized that interacting processes of perceptual and metacognitive performance contribute to the arising decision confidence in distributed, but segregable co-activating brain regions. To test this hypothesis, we performed task-fMRI in healthy humans during a visual backward masking task with four-scale, post-decision confidence ratings. We measured blood oxygenation covariation patterns, which served as a physiological proxy for co-activation across brain regions. Decision confidence ratings and an individual’s metacognitive efficiency served as behavioral measures for metacognition. We found three distinct co-activation clusters involved in decision confidence: the first included right-centered fronto-temporal-parietal regions, the second included left temporal and parietal regions, and the left basal forebrain (BF), and the third included cerebellar regions. The right fronto-temporal-parietal cluster including the supplementary eye field and the right basal forebrain showed stronger co-activation in subjects with higher metacognitive efficiency. Our results provide novel evidence for co-activation of widely distributed fronto-parieto-temporal regions involved in visual confidence. The supplementary eye field was the only region that activated for both decision confidence and metacognitive efficiency, suggesting the supplementary eye field plays a key role in visual metacognition. Our results link findings in electrophysiology studies and human fMRI studies and provide evidence that confidence estimates arise from the integration of multiple information processing pathways.

An Adaptive Infrared Image Segmentation Method Based on Fusion SPCNN

Inspired by multiple information processing mechanisms of the human nervous system, a fusion simplified pulse coupled neural network (FSPCNN) model for infrared (IR) image segmentation is proposed in this paper. In the method based on FSPCNN, the time decay factor is set adaptively based on Stevens’ power law, and the synaptic weight is generated adaptively based on lateral inhibition (LI), without manual intervention. Meanwhile, according to fast linking mechanism, the similarity between adjacent iteration results is used to implement the automatic selection of optimal segmentation result and control iteration. Experimental results indicate that the proposed method has favorable robustness and segmentation performance.

An adaptive infrared image segmentation method based on fusion SPCNN

Inspired by multiple information processing mechanisms of the human nervous system, a fusion simplified pulse coupled neural network (FSPCNN) model for infrared (IR) image segmentation is proposed in this paper. In the method based on FSPCNN, the time decay factor is set adaptively based on Stevens’ power law, and the synaptic weight is generated adaptively based on Lateral Inhibition (LI), without manual intervention. Meanwhile, according to Fast linking mechanism, the similarity between adjacent iteration results is used to implement the automatic selection of optimal segmentation result and control iteration. Experimental results indicate that the proposed method can satisfactorily segment targets from complex backgrounds, and it has favorable robustness and segmentation performance.

An analysis on the current researches in encryption algorithms based on neural networks

To perform an in-depth analysis on the current researches concerning the application of neural networks in the encryption algorithms, this paper used some research methods such as literature review and algorithm analysis to review the proposal and application of neural network technologies as well as the proposal and research and development of encryption algorithms through. The importance of neural networks in the encryption algorithms is highlighted. The current researches on encryption algorithms based on neural networks are reviewed from three dimensions, namely, algorithm framework, algorithm model and algorithm application. Through the research, it shows that the neural network technology has become the preferred choice for developing the encryption algorithms. This paper also proposed the direction of improvement through fusion application of multiple information processing technologies. This work lays the basis for improving the encryption algorithms based on neural network.

A Novel Microbiosensor Microarray for Continuous Ex Vivo Monitoring of Gamma-Aminobutyric Acid in Real-Time

Gamma-aminobutyric acid(GABA) is the major inhibitory neurotransmitter that is essential for normal brain function. It is involved in multiple neuronal activities, including plasticity, information processing and network synchronization.Abnormal GABA levels result to severe brain disorders and therefore GABA has been the target of a wide range of drug therapeutics. GABA being non-electroactive is challenging to detect in real-time. Here, we report the design and implementation of a GABA microarray probe that operates on a newly conceived principle. It consists of two microbiosensors, one for Glutamate (Glu) and one for GABA detection, modified with glutamate oxidase and GABASE enzymes, respectively. GABA and Glu can be detected continuously in real-time in-vitro and ex-vivo and without the addition of any externally applied reagents. This method yielded higher sensitivity and it was also successfully tested in an adult rat brain slice preparation. These initial results demonstrate that the developed GABA probe has the potential to become powerful neuro-scientific tool that could be employed in the future for in vivo studies. Moreover, we will discuss the recent findings from the optimization of the probe by modifying enzyme concentration and process automation with micro-spotting technique and the changes in the GLU-GABA concentrations in different parts of the brain (thalamus, hippocampus, cortex) for epilepsy disease model. Figure 1