Local Information Storage Research Articles

Research on Reliability Prediction Methods for Dynamic Networks Based On Deep Neural Networks

In reality many networks, such as information storage networks, can be modelled as a multi-state stochastic flow network. The reliability of an information storage network is the probability that the network is able to deliver the required amount of information data to the specified server, and its an important metric to assess the performance of an information storage network. Most existing multi-state network reliability assessment algorithms are calculated using very small capacity vectors. However, calculating the reliability of such a network requires reuse of algorithms and is inefficient when the network size, demand, etc., changes rapidly, resulting in managers not making timely decisions accordingly. In order to obtain reliability metrics quickly, this paper proposes a dynamic network reliability prediction model based on deep neural networks (DNNs), which allows the corresponding reliability to be obtained quickly even when the network is changing rapidly. Finally, a local information storage network is used as an example for validation to illustrate the feasibility of the prediction model.

Local and global measures of information storage for the assessment of heartbeat-evoked cortical responses

Objective:Brain–heart interactions involve bidirectional effects produced by bottom-up input at each heartbeat, and top-down neural regulatory responses of the brain. While the cortical processing of the heartbeat is usually investigated through the analysis of the Heartbeat Evoked Potential, in this study we propose an alternative approach based on the variability in the predictability of the brain dynamics induced by the heartbeat. Methods:In a group of eighteen subjects in whom simultaneous recording of the electroencephalogram (EEG) and electrocardiogram was performed in a resting-state, we analyzed the temporal profile of the local Information Storage (IS) to detect changes in the regularity of EEG signals in time windows associated with different phases of the cardiac cycle at rest. Results:The average values of the local IS were significantly higher in the parieto-occipital areas of the scalp, suggesting an activation of the Default Mode Network, regardless of the cardiac cycle phase. In contrast, the variability of the local IS showed marked differences across the cardiac cycle phases. Conclusion:Our results suggest that cardiac activity influences the predictive information of EEG dynamics differently in the various phases of the cardiac cycle. Significance:The variability of local IS measures can represent a useful index to identify spatio-temporal dynamics within the neurocardiac system, which generally remain overlooked by the more widely employed global measures.

Astrocytic microdomains from mouse cortex gain molecular control over long-term information storage and memory retention

Memory consolidation requires astrocytic microdomains for protein recycling; but whether this lays a mechanistic foundation for long-term information storage remains enigmatic. Here we demonstrate that persistent synaptic strengthening invited astrocytic microdomains to convert initially internalized (pro)-brain-derived neurotrophic factor (proBDNF) into active prodomain (BDNFpro) and mature BDNF (mBDNF) for synaptic re-use. While mBDNF activates TrkB, we uncovered a previously unsuspected function for the cleaved BDNFpro, which increases TrkB/SorCS2 receptor complex at post-synaptic sites. Astrocytic BDNFpro release reinforced TrkB phosphorylation to sustain long-term synaptic potentiation and to retain memory in the novel object recognition behavioral test. Thus, the switch from one inactive state to a multi-functional one of the proBDNF provides post-synaptic changes that survive the initial activation. This molecular asset confines local information storage in astrocytic microdomains to selectively support memory circuits.

Circadian Modulation of Neurons and Astrocytes Controls Synaptic Plasticity in Hippocampal Area CA1

SUMMARYMost animal species operate according to a 24-h period set by the suprachiasmatic nucleus (SCN) of the hypothalamus. The rhythmic activity of the SCN modulates hippocampal-dependent memory, but the molecular and cellular mechanisms that account for this effect remain largely unknown. Here, we identify cell-type-specific structural and functional changes that occur with circadian rhythmicity in neurons and astrocytes in hippocampal area CA1. Pyramidal neurons change the surface expression of NMDA receptors. Astrocytes change their proximity to synapses. Together, these phenomena alter glutamate clearance, receptor activation, and integration of temporally clustered excitatory synaptic inputs, ultimately shaping hippocampal-dependent learning in vivo. We identify corticosterone as a key contributor to changes in synaptic strength. These findings highlight important mechanisms through which neurons and astrocytes modify the molecular composition and structure of the synaptic environment, contribute to the local storage of information in the hippocampus, and alter the temporal dynamics of cognitive processing.

Circadian Modulation of Neurons and Astrocytes Controls Synaptic Plasticity in Hippocampal Area CA1

Most animal species operate according to a 24-hour period set by the suprachiasmatic nucleus (SCN) of the hypothalamus. The rhythmic activity of the SCN is known to modulate hippocampal-dependent memory processes, but the molecular and cellular mechanisms that account for these effects remain largely unknown. Here, we show that there are cell-type specific structural and functional changes that occur with circadian rhythmicity in neurons and astrocytes in hippocampal area CA1. Pyramidal neurons change the surface expression of NMDA receptors, whereas astrocytes change their proximity to synapses. Together, these phenomena alter glutamate clearance, receptor activation and integration of temporally clustered excitatory synaptic inputs. These findings identify important mechanisms through which neurons and astrocytes modify the molecular composition and structure of the synaptic environment, contribute to the local storage of information in the hippocampus and alter the temporal dynamics of learning and memory processing.

Editorial for the research topic: information-based methods for neuroimaging: analyzing structure, function and dynamics.

Editorial for the research topic: information-based methods for neuroimaging: analyzing structure, function and dynamics.

Mathematical Modeling of Locally Information Storage Capability of VANET for Highway Traffic

By cooperative communication among mobile vehicles, content based information dissemination could be implemented by local information storage. This paper focuses on analyzing how long the local information could be stored in this local region for, which is also called storage capability. Although there is an approximation model for two-way traffic, it is not scalable for different scenarios .We analyze the different scenarios of two-way highway traffic. Based on the analytical model for one-way highway traffic, we improve the model for two-way highway traffic. Finally, exhaustive simulations are presented to show the good performance of the proposed models for different scenarios.

Information storage, loop motifs, and clustered structure in complex networks

We use a standard discrete-time linear Gaussian model to analyze the information storage capability of individual nodes in complex networks, given the network structure and link weights. In particular, we investigate the role of two- and three-node motifs in contributing to local information storage. We show analytically that directed feedback and feedforward loop motifs are the dominant contributors to information storage capability, with their weighted motif counts locally positively correlated to storage capability. We also reveal the direct local relationship between clustering coefficient(s) and information storage. These results explain the dynamical importance of clustered structure and offer an explanation for the prevalence of these motifs in biological and artificial networks.

Co-evolution of a multiple autonomous robot system and its working environment via intelligent local information storage

This paper proposes a new strategy of evolution and adaptation of a population of multiple autonomous mobile robots to make multi-robot systems more flexible and robust against unpredictable environmental conditions and human operator's capricious commands, and to overcome the limitation of biologically inspired techniques which need a certain degree of redundancy and diversity. The important idea of the strategy is to utilize the advantageous parallelism of the multi-robot system and immobile but portable devices (data carriers) for information storage and management. A device named IDC (Intelligent Data Carrier) is developed with a unique mushroom-like structure to be easily handled by a forklift mechanism which can be mounted on actual mobile robots. The handling method of the IDC unit, which should be essential to realization of the proposed strategy, is examined through the experiment using an omni-directional mobile robot, and the robot succeeded in placing the unit at an appropriate location. With the demonstrated handling technique, the robots can adapt and evolve as a population by accessing locally available information which is stored and processed in the IDC unit.

The SUNBURST network: recording and analyzing solar magnetic disturbances

SUNBURST, a monitoring and communication network covering the US and Canada to alert electric utilities to the onset and magnitude of a solar magnetic disturbance (SMD), is discussed. Experience with a system involving data collection and local storage of information from multiple points at a substation, which was developed and field tested as part of a study concerned with the measurement of magnetic fields (EPRI Project RP2942-1), is briefly summarized. The design of SUNBURST is described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Information systems for environmental hygiene: experience with a U. S. company proprietary service.

Part I (by E.G.B.) describes the role of a proprietary service in providing occupational health information to affiliates of a multinational petroleum company. The work is primarily concerned with the preparation and distribution of reports documenting health-related research on prducts and processes, dissemination of internally prepared abstracts of information published in the scientific or medical press, and the collection of information specifically requested by affiliate personnel. Techniques employed in classifying and distributing proprietary and published material arc discussed. Part 2 (by R.J.S.) describes the use of this service by a regional hygienist, relating it to the general channels of communication and the local storage of information. The need for a simple system for the hygienist to store and retrieve technical and administrative information is apparent.