Dynamic Information Processing Research Articles

Neural spatio-temporal patterns of information processing related to cognitive conflict and correct or false recognitions

Using a visual short-term memory task and employing a new methodological approach, we analyzed neural responses from the perspective of the conflict level and correctness/erroneous over a longer time window. Sixty-five participants performed the short-term memory task in the fMRI scanner. We explore neural spatio-temporal patterns of information processing in the context of correct or erroneous response and high or low level of cognitive conflict using classical fMRI analysis, surface-based cortical data, temporal analysis of interpolated mean activations, and machine learning classifiers. Our results provide evidence that information processing dynamics during the retrieval process vary depending on the correct or false recognition—for stimuli inducing a high level of cognitive conflict and erroneous response, information processing is prolonged. The observed phenomenon may be interpreted as the manifestation of the brain’s preparation for future goal-directed action.

Linking the Brain with Behavior: The Neural Dynamics of Success and Failure in Goal-directed Behavior.

The human brain is extremely flexible and capable of rapidly selecting relevant information in accordance with task goals. Regions of frontoparietal cortex flexibly represent relevant task information such as task rules and stimulus features when participants perform tasks successfully, but less is known about how information processing breaks down when participants make mistakes. This is important for understanding whether and when information coding recorded with neuroimaging is directly meaningful for behavior. Here, we used magnetoencephalography to assess the temporal dynamics of information processing and linked neural responses with goal-directed behavior by analyzing how they changed on behavioral error. Participants performed a difficult stimulus-response task using two stimulus-response mapping rules. We used time-resolved multivariate pattern analysis to characterize the progression of information coding from perceptual information about the stimulus, cue and rule coding, and finally, motor response. Response-aligned analyses revealed a ramping up of perceptual information before a correct response, suggestive of internal evidence accumulation. Strikingly, when participants made a stimulus-related error, and not when they made other types of errors, patterns of activity initially reflected the stimulus presented, but later reversed, and accumulated toward a representation of the "incorrect" stimulus. This suggests that the patterns recorded at later time points reflect an internally generated stimulus representation that was used to make the (incorrect) decision. These results illustrate the orderly and overlapping temporal dynamics of information coding in perceptual decision-making and show a clear link between neural patterns in the late stages of processing and behavior.

Ontology of functional synergetics in virtual cognitive-semiotic design of information processes and systems

Objectives . In view of the currently expanding practice of applying methods for configuring ontologies of designing information processes and systems, information technology (IT) specialists need to disclose the definition of such a new concept as functional synergetics. This concept is actualized by the course of development of virtual information technologies, forming the system foundation for methods of design ontologies and indicating attributes for the correct formation of design ontologies of relevant dynamic information processes and systems. In the present work, this is done by means of analytical review. Methods . The analytical review is based on the authors’ vision of deepening the concept of synergetics as applied to a variety of modern information processes and systems. This context concessionally conjugates the combined use and consideration of such approaches as the method of ontologies (the major component of the resulting concession of methods), methods of cognitive semiotics, autopoiesis, and other manifestations of synergetics with related methods and techniques of emergent evaluation of the role and effectiveness of any occurring systemic changes. It is this combined dynamics of information processes and systems that allows the authors to put forward a developing treatment of the methods of synergetics as functional. Results . The concept of functional synergetics was clarified and deepened based on the method of ontologies. In the theory of information processes and systems, it is manifested in updating ontologies accompanying scientific and engineering projects that use synergetics as an initial methodological basis. It turns out that the functional features of synergetics in the context of assessments and functional ordering of modern IT devices give it new opportunities in highlighting the significant indicators of system changes: properties, attributes, and manifestations of functionalsynergistic nature. It is these three concepts, revealed by synergetics as a functional eyepiece, that distinguish functional synergetics from the generally accepted definition of synergetics as such. Matching form to content, autopoiesis, development, and transformations occurring with informational processes and systems with their emergent consequences are the essence and feature of functional synergetics. Tracing what is happening in dynamics, in the inseparability of assessments and regulations of the set of properties, attributes, and manifestations of the analyzed processes to an even greater extent clarifies the essence and role of the concept introduced by the authors in the general classical theory of information processes and systems. In the related analysis, virtual reality, augmented reality, mixed reality, expanded reality, composite reality, coupled reality, geoinformation systems, multidimensional computer graphics, fractal graphics, holographic graphics, computer teletype games, X-reality, etc., which have essential dynamic characteristics and properties, are included here as objects of research and design. Conclusions . Improvement of the theory and practice of creating and using information processes and systems from the position of recognizingthe accelerating speed and dynamics of how their properties and indicators are modified leads the synergistic methodology of assessments and control mechanisms to the emergence of a clarifying concept of functional synergy. This is a complex and dynamic concept, which includes its interpretation of the positions of classical synergetics and related paradigms of cognitive semiotics, etc. The method of ontologies, which is gaining more and more popularity, is the main means and tool of such unification. When combined with cognitive semiotics, functional synergetics becomes a powerful science-intensive tool for further development of the theory and practice of modern multimedia intensified information systems and their information fields described by both imperative and convivial paradigms.

Dynamical Mechanism of Sampling-Based Probabilistic Inference Under Probabilistic Population Codes.

Animals make efficient probabilistic inferences based on uncertain and noisy information from the outside environment. It is known that probabilistic population codes, which have been proposed as a neural basis for encoding probability distributions, allow general neural networks (NNs) to perform near-optimal point estimation. However, the mechanism of sampling-based probabilistic inference has not been clarified. In this study, we trained two types of artificial NNs, feedforward NN (FFNN) and recurrent NN (RNN), to perform sampling-based probabilistic inference. Then we analyzed and compared their mechanisms of sampling. We found that sampling in RNN was performed by a mechanism that efficiently uses the properties of dynamical systems, unlike FFNN. In addition, we found that sampling in RNNs acted as an inductive bias, enabling a more accurate estimation than in maximum a posteriori estimation. These results provide important arguments for discussing the relationship between dynamical systems and information processing in NNs.

Multistability of fractional-order memristor-coupled heterogeneous neurons and its hardware realization

There is heterogeneity among different neurons, and the activities of neurons are greatly different, so the coupling between heterogeneous neurons can show richer dynamic phenomena, which is of great significance in understanding the neural function of the human brain. Unfortunately, in many studies of memristive coupled neurons, researchers have considered two adjacent identical neurons, but ignored the heterogeneous neurons. In this paper, two models are chosen, i.e. a Hindmarsh-Rose neuron model and a Hopfield neuron model, which are very different from each other. The proposed fractional-order linear memristor and fractional-order hyperbolic memristor simulated neural synapses are introduced into the two heterogeneous neuron models, considering not only the coupling between the two neurons, but also the coupling between single neurons. The self-coupling of neurons, a five-dimensional fractional memristive coupled heterogeneous neuron model, is established. In the numerical simulation of the new neuron model, the phase diagrams, bifurcation diagrams, Lyapunov exponent diagrams, and attraction basins are used to demonstrate the changes in coupling strength and other parameters in the memristive coupled heterogeneous neuron model, the new neuron model performance coexistence of different attractors. On the other hand, by changing the initial state of the system while keeping the relevant parameters of the system unchanged, the multistable phenomenon of the coupled heterogeneous neuron model can be observed. Using the phase diagram, the coexistence of different periods, and the phenomenon of period and chaos can be clearly observed. The coexistence of different attractor states can also be observed in the attractor basin. This has many potential implications for studying dynamic memory and information processing in neurons. Uncovering different types of multistable states from a dynamical perspective can provide an insight into the role of multistable states in brain information processing and cognitive function. Finally, the neuron model is implemented based on the micro control unit of the advanced RISC machine, and the phase diagram is observed under some parameters of the coupled neuron model on an oscilloscope. The experimental results show the validity of the theoretical analysis.

Dynamic Cooper Pair Splitter.

Cooper pair splitters are promising candidates for generating spin-entangled electrons. However, the splitting of Cooper pairs is a random and noisy process, which hinders further synchronized operations on the entangled electrons. To circumvent this problem, we here propose and analyze a dynamic Cooper pair splitter that produces a noiseless and regular flow of spin-entangled electrons. The Cooper pair splitter is based on a superconductor coupled to quantum dots, whose energy levels are tuned in and out of resonance to control the splitting process. We identify the optimal operating conditions for which exactly one Cooper pair is split per period of the external drive and the flow of entangled electrons becomes noiseless. To characterize the regularity of the Cooper pair splitter in the time domain, we analyze the g^{(2)} function of the output currents and the distribution of waiting times between split Cooper pairs. Our proposal is feasible using current technology, and it paves the way for dynamic quantum information processing with spin-entangled electrons.

Internet of things-assisted advanced dynamic information processing system for physical education system.

In recent years the Internet of Things (IoT) has become a popular technological culture in the physical education system. Though several technologies have grown in the physical education system domain, IoT plays a significant role due to its optimized health information processing framework for students during workouts. In this paper, an advanced dynamic information processing system (ADIPS) has been proposed with IoT assistance to explore the traditional design architecture for physical activity tracking. To track and evaluate human physical activity in day-to-day living, a new paradigm has been integrated with wearable IoT devices for effective information processing during physical workouts. Continuous observation and review of the condition and operations of various students by ADIPS helps to evaluate the sensed information to analyze the health condition of the students. The result of ADIPS has been implemented based on the performance factor correlation with the traditional system.

How Does Information Overload Affect Consumers' Online Decision Process? An Event-Related Potentials Study.

One of the advantages of e-retailers is their capability to provide a large amount of information to consumers. However, when the amount of information exceeds consumers’ information processing capacities, it will lead to worse decision quality and experience, causing the information overload effect. In this study, the event-related potentials (ERPs) were applied to examine the hidden neural mechanism of the impact of information overload on consumers’ decision processes. Behavioral data showed that people would spend more time making decisions when faced with information overload. Neurophysiologically, consumers would invest less attentional resources in the high amount of information (HAI) condition than those in the low amount of information (LAI) condition and lead to less positive P2 amplitudes. The HAI condition would increase decision difficulty than would the LAI condition and result in smaller P3 amplitudes. In addition, an increased late positive component (LPC) was observed for the HAI condition in contrast to the LAI condition, indicating that consumers were more inclined to have decision process regret when consumers were overloaded. We further investigated the dynamic information processing when consumers got over information overload by mining the brain’s time-varying networks. The results revealed that during the decision process and the neural response stage, the central area controlled other brain regions’ activities for the HAI condition, suggesting that people may still consider and compare other important information after the decision process when faced with information overload. In general, this study may provide neural evidence of how information overload affects consumers’ decision processes and ultimately damages decision quality.

Digital library studies: The subject and definition

The author demonstrates that the problems of library process automation have been put forward in 1970s. He refers to the book by Gerard Salton (1927–1995) (well-known author and designer of SMART – System for the Mechanical Analysis and Retrieval of Text): «Dynamic information and library processing» (1973). In his monograph, Salton reviewed the libraries and predicted their future as electronic institutions; he also proposed the model of efficient library collection circulation and discussed the issues of collection quality, file organization and storage, technology of computer-aided classification, etc. He concluded the monograph with the paragraph on the collection growth and document removal. In fact, it was one of the first and significant works in electronic collection studies. Russian e-librarianship dates back to 2001 when the first edition of textbook by Andrey I. Zemskov and Ya. L. Shrayberg was published. The priority of theoretical conceptualization of electronic processes was given to IT professionals. They were enthusiastic about new technologies but rather depreciative about the time-honored provisions of general and applied library science. They have been thinking that they are to design the library of special kind and, correspondingly, that the library science of special kind bears no relation to traditional library studies. Empirically they have adopted the approach of their predecessors of the dawn of civilization. They have been interpreting the subject of electronic library studies etymologically – as the book depository (i.e. the system of two elements: e-documents and facilities). The e-library, however, has got the same structure as any other library, and the structure of library science is also the same; it comprises the rovisions related to the whole e-library and each of its four elements of its primary contour, i.e. library collection, user/reader contingent, facilities and resources, and library staff. Each of the provisions is specified in the article.

Enhancing Hospital Performance: the role of interfirm dynamic capabilities from the information processing view

ABSTRACT In today’s increasingly volatile world, organisations including healthcare providers need interfirm dynamic capabilities to make timely responses to supply chains changes. There has been a wealth of literature on dynamic capabilities; however, very few are at interfirm level and even fewer are conducted in the healthcare industry. Grounded in information processing view, this study contributes to the literature by examining interfirm dynamic capabilities in the context of healthcare delivery networks. It proposes the interfirm dynamic capabilities as a form of information processing capability with five dimensions: sensing, learning, coordination, collective mind and partner development. Using data collected from healthcare delivery networks, this study explores the impact of interfirm dynamic capabilities on hospitals’ performance. The findings suggest that the fit between a healthcare delivery network's interfirm dynamic capabilities and information processing requirements will improve its operational capabilities, which will lead to better performance.

ERP Characteristics of Inducing Rule Validity in Number Series Under Time Pressure.

A great deal of research has been devoted to examining the neural mechanisms of inductive reasoning. However, the influences of rule validity and time pressure on numerical inductive reasoning remain unclear. In the current study, we aimed to examine the effects of these variables on the time course of rule identification in numerical inductive reasoning. We designed a 3 (task type: valid, invalid, and anomalous) × 2 (time pressure: with time pressure and without time pressure) within-subject experiment based on electroencephalographic event-related potentials (ERP). Behaviorally, we found significant effects of rule validity and time pressure on rule identification. Neurologically, we considered the elicited N200 ERP to reflect conflict detection and found it to be modulated by rule validity but not time pressure. We considered the induced P300 ERP to be primarily related to updating working memory, affected by both rule validity and time pressure. These findings have new implications for better understanding dynamic information processing within numerical inductive reasoning.

Direct training of hardware-friendly weight binarized spiking neural network with surrogate gradient learning towards spatio-temporal event-based dynamic data recognition

The spiking neural network (SNN)-based neuromorphic hardware has been extensively studied in dynamic information processing. However, there is still a lack of training algorithms to drive or support the implementation of compact spatio-temporal neuromorphic hardware; and the existing neuromorphic hardware uses excessive on-chip memory to store parameters, which limits its neuron and synapse scale. Here, we introduce a hardware-friendly weight binarized spiking neural network (BSNN) to efficiently recognize the spatio-temporal event-based data. The neuron of the spike response model (SRM) is used in BSNN due to its rich spatio-temporal characteristics. In the training process, a surrogate gradient method is used to replace the derivative of the spike train, and the weights are binarized. Moreover, combined with the spiking characteristics of SNN (i.e., the input/output of SNN and the communications of neurons in SNN are binary spikes), it is possible to replace the hardware-expensive matrix–vector multiplication (MVM) with the hardware-friendly “Signed AND” operation during inference, which is favored for constructing compact neuromorphic hardware. The trained BSNN has competitive recognition accuracies of 99.52% and 62.1%, 97.57%, and 90.35% on the dynamic images dataset N-MNIST and DVS-CIFAR10, dynamic gestures dataset DvsGesture, and dynamic audio dataset N-TIDIGITS18, respectively, which are related to human vision or hearing. The proposed compact SNN training method paves the way for real-time dynamic information processing oriented hardware-saving and power-efficient neuromorphic hardware.

Information processing on equity prices and exchange rate for cross-listed stocks

I propose a novel structural setting to investigate the dynamics of information processing on equity prices and the exchange rate for cross-listed stocks. Using high-frequency data on Brazilian cross-listed firms, I disentangle the effects on firm value of the exchange rate from the other determinants of a firm's cash flow. In general, the results suggest that the U.S. is faster than the home market and that there is a net positive relationship between the value of the domestic currency and the firm's value. This result is linked to the likely partially segmented market characteristic of the home market. Robustness checks confirm the results.

Thoughts on the application of quantum-based artificial intelligence for military purposes

In today's information-based society, computer technology is developing rapidly, and thus the speed of operations is increasing exponentially. Fast and dynamic information processing has become one of the basic needs of countries and their organizations. Furthermore, higher levels of info-communication technologies can also be detected. In the field of computing, quantum computers are becoming more and more popular, and their current development process is particularly intense, which in turn has triggered a kind of competition between the countries interested in the subject. The estimated future usability of quantum computers for humans is currently being outlined. Regarding to the new computing capabilities, quantum informatics is closely related to artificial intelligence and military forces can gain unprecedented information superiority through the development.

Reduced Dynamic Aperiodic Spectral Slope Marks Atypical Neural Information Processing in Children With Attention Deficit Hyperactivity Disorder

Reduced Dynamic Aperiodic Spectral Slope Marks Atypical Neural Information Processing in Children With Attention Deficit Hyperactivity Disorder

RETRACTED: Research on virtual simulation of basketball technology 3D animation based on FPGA and motion capture system

A stable structure and group information for measuring the driving dynamic binding character information processing, network control and operation when ready. Created with Unity, Tensol Stream and PyTorch. This method can be done bipedal and quadruped movement in the past occupied the complex and rational activity. The study will help researchers select the appropriate exercise programs and motion capture framework to test their gadgets. The rose to select and added to the open network arena, improve audit in their investigation. Framework has instructed sub conventional and highly accurate three-dimensional movement. Article screening to determine the motion capture framework research study of distributed source in one hundred academic studies used in the report of human activity. The proposed use of support to Field Programmable Gate array (FPGA) virtual computing innovation replay basketball to move the ball from the foundation and other projects, such as the distinction between players and referees. The two-dimensional position of the ball results are calibrated to identify the real position of the ball. Reality from the position of the ball, shoot the scene from every angle camera, and further ground plane. True development continue to see the ball from the top. Tests showed that the proposed calculation obstacles can be handled quickly and less difficult ball, the ball slightly different size and shape.

A Bayesian brain model of adaptive behavior: an application to the Wisconsin Card Sorting Task.

Adaptive behavior emerges through a dynamic interaction between cognitive agents and changing environmental demands. The investigation of information processing underlying adaptive behavior relies on controlled experimental settings in which individuals are asked to accomplish demanding tasks whereby a hidden regularity or an abstract rule has to be learned dynamically. Although performance in such tasks is considered as a proxy for measuring high-level cognitive processes, the standard approach consists in summarizing observed response patterns by simple heuristic scoring measures. With this work, we propose and validate a new computational Bayesian model accounting for individual performance in the Wisconsin Card Sorting Test (WCST), a renowned clinical tool to measure set-shifting and deficient inhibitory processes on the basis of environmental feedback. We formalize the interaction between the task’s structure, the received feedback, and the agent’s behavior by building a model of the information processing mechanisms used to infer the hidden rules of the task environment. Furthermore, we embed the new model within the mathematical framework of the Bayesian Brain Theory (BBT), according to which beliefs about hidden environmental states are dynamically updated following the logic of Bayesian inference. Our computational model maps distinct cognitive processes into separable, neurobiologically plausible, information-theoretic constructs underlying observed response patterns. We assess model identification and expressiveness in accounting for meaningful human performance through extensive simulation studies. We then validate the model on real behavioral data in order to highlight the utility of the proposed model in recovering cognitive dynamics at an individual level. We highlight the potentials of our model in decomposing adaptive behavior in the WCST into several information-theoretic metrics revealing the trial-by-trial unfolding of information processing by focusing on two exemplary individuals whose behavior is examined in depth. Finally, we focus on the theoretical implications of our computational model by discussing the mapping between BBT constructs and functional neuroanatomical correlates of task performance. We further discuss the empirical benefit of recovering the assumed dynamics of information processing for both clinical and research practices, such as neurological assessment and model-based neuroscience.

Reconstruction of a neural network and alteration of the operators’ strategies during the recognition of facial images

Functional magnetic-resonance imaging was used to study the dynamics of information processing concerning a face in the neural networks of the brain. It is shown that large-scale neural networks of the operators’ brains were reconstructed during the second half of the study. Thus, when an instruction conflicts with the contents of the image so that it is impossible to find the correct solution, the operators transfer to a less energy-intensive behavioral strategy—they simulate the solution of the problem. When the assigned task is carried out under specified conditions, so that it is easy to distinguish the rotation of the head, the subject quickly learns to solve the problem, develops the knack, and adopts an automatic-motion regime—another neural network is activated, called the brain’s default-mode network. It is shown that the medial prefrontal cortex plays a role in developing and altering the strategy of the operator’s activity. The reconstruction of large-scale neural networks is probably caused by an unconscious mechanism that simplifies purposeful activity. The results agree well with the well-known principle of least action.

Maintenance vs. Manipulation in Auditory Verbal Working Memory in the Elderly: New Insights Based on Temporal Dynamics of Information Processing in the Millisecond Time Range.

Working memory (WM) is a limited-capacity cognitive system that allows the storage and use of a limited amount of information for a short period of time. Two WM processes can be distinguished: maintenance (i.e., storing, monitoring, and matching information) and manipulation (i.e., reordering and updating information). A number of studies have reported an age-related decline in WM, but the mechanisms underlying this deterioration need to be investigated. Previous research, including studies conducted in our laboratory, revealed that age-related cognitive deficits are related to decreased millisecond timing, i.e., the ability to perceive and organize incoming events in time. The aim of the current study was: (1) to identify in the elderly the brain network involved in the maintenance and manipulation WM processes; and (2) to use an fMRI task to investigate the relation between the brain activity associated with these two processes and the efficiency of temporal information processing (TIP) on a millisecond level reflected by psychophysical indices. Subjects were 41 normal healthy elderly people aged from 62 to 78 years. They performed: (1) an auditory verbal n-back task for assessing WM efficiency in an MRI scanner; and (2) a psychophysical auditory temporal-order judgment (TOJ) task for assessing temporal resolution in the millisecond domain outside the scanner. The n-back task comprised three conditions (0-, 1-, and 2-back), which allowed maintenance (1- vs. 0-back comparisons) and manipulation (2- vs. 1-back comparisons) processes to be distinguished. Results revealed the involvement of a similar brain network in the elderly to that found in previous studies. However, during maintenance processes, we found relatively limited and focused activations, which were significantly extended during manipulation. A novel result of our study, never reported before, is an indication of significant moderate correlations between the efficiency of WM and TIP. These correlations were found only for manipulation but not for maintenance. Our results confirmed the hypothesis that manipulation in the elderly is a dynamic process requiring skilled millisecond timing with high temporal resolution. We conclude that millisecond timing contributes to WM manipulation in the elderly, but not to maintenance.

Entropic Dynamics in Neural Networks, the Renormalization Group and the Hamilton-Jacobi-Bellman Equation.

We study the dynamics of information processing in the continuum depth limit of deep feed-forward Neural Networks (NN) and find that it can be described in language similar to the Renormalization Group (RG). The association of concepts to patterns by a NN is analogous to the identification of the few variables that characterize the thermodynamic state obtained by the RG from microstates. To see this, we encode the information about the weights of a NN in a Maxent family of distributions. The location hyper-parameters represent the weights estimates. Bayesian learning of a new example determine new constraints on the generators of the family, yielding a new probability distribution which can be seen as an entropic dynamics of learning, yielding a learning dynamics where the hyper-parameters change along the gradient of the evidence. For a feed-forward architecture the evidence can be written recursively from the evidence up to the previous layer convoluted with an aggregation kernel. The continuum limit leads to a diffusion-like PDE analogous to Wilson’s RG but with an aggregation kernel that depends on the weights of the NN, different from those that integrate out ultraviolet degrees of freedom. This can be recast in the language of dynamical programming with an associated Hamilton–Jacobi–Bellman equation for the evidence, where the control is the set of weights of the neural network.