Organizing Network Architecture Research Articles

Direct Cortical Recordings Suggest Temporal Order of Task-Evoked Responses in Human Dorsal Attention and Default Networks.

The past decade has seen a large number of neuroimaging studies focused on the anticorrelated functional relationship between the default mode network (DMN) and the dorsal attention network (DAN). Due principally to the low temporal resolution of functional neuroimaging modalities, the fast-neuronal dynamics across these networks remain poorly understood. Here we report novel human intracranial electrophysiology data from six neurosurgical patients (four males) with simultaneous coverage of well characterized nodes of the DMN and DAN. Subjects performed an arithmetic processing task, shown previously to evoke reliable deactivations (below baseline) in the DMN, and activations in the DAN. In this cohort, we show that DMN deactivations lag DAN activations by approximately 200 ms. Our findings suggest a clear temporal order of processing across the two networks during the current task and place the DMN further than the DAN in a plausible information-processing hierarchy.SIGNIFICANCE STATEMENT The human brain contains an intrinsic and strictly organized network architecture. Our understanding of the interplay across association networks has relied primarily on the slow fluctuations of the hemodynamic response, and as such it has lacked essential evidence regarding the temporal dynamics of activity across these networks. The current study presents evidence from high spatiotemporal methods showing that well studied areas of the default mode network display delayed task-induced activity relative to divergent responses in dorsal attention network nodes. This finding provides direct and critical evidence regarding the temporal chronology of neuronal events across opposing brain networks.

SDN assisted self organizing network architecture for multi-RAT networks and mobility prediction

Existing and future wireless networks are expected to have multiple Radio Access Technologies (RAT), primarily due to legacy reasons and introduction of newer technologies. However, new technologies, e.g., Self Organizing Networks (SON), tend to focus only on future generations networks, ignoring the existing benefits provided by legacy systems. The motivation behind this work is that, with minimal changes newer functionalities can be extended to legacy systems as well. This paper proposes an Software Defined Networking assisted SON architecture for multi-RAT cellular networks. Using this architecture, a multi-RAT Automatic Neighbour Relation functionality of SON is constructed, and the same can be used to predict mobility of User Equipments (UEs) to a cell. This mobility aspect is modeled using random graphs, and a Mixed Integer Non-Linear Programming problem is formulated, to maximize probability of mobility of UEs to a cell. It is observed that the problem is non-convex and also NP-Hard. Subsequently, a Particle Swarm Optimization (PSO) approach is developed to solve this problem. Results show that PSO output matches with that of exhaustive search for finding the maximum value of UE mobility probability to a cell, thus validating the solution approach. Results also reveal, compatibility of QoS parameters (required by UEs) among source and target cells, increases mobility probability. However, higher arrival rate of UEs towards a cell decreases the same, within target cell capacity limits. Subsequently, implications of UE mobility probability on core SON functionalities, such as load balancing and mobility optimization, are studied.

Secured Self Organizing Network Architecture in Wireless Personal Networks

Secured self organizing network is an approach to computer network architecture that seeks to address the technical issues in heterogeneous networks that may lack continuous network connectivity. In delay tolerant network packets storage exists when there is any link breakage between the nodes in the network so delay is tolerable in this type of network during the data transmission. But this delay is not tolerable in wireless network for voice packet transmission. This evokes the use of wireless networks. In a network, different wireless network topologies are interoperating with each other so the communication across the network is called overlay network. This network is vulnerable to attacks due to mobile behaviour of nodes and frequent changes in topologies of the network. The attacks are wormhole attack and blackhole attack is analysed in this paper. They are critical threats to normal operation in wireless networks which results in the degradation of the network performance. The proposed recovery algorithm for wormhole and the isolation of blackhole will increase the performance of the network. The performance metrics such as throughput, packet delivery ratio, end–end delay and routing overhead of the network are evaluated.

Axial Organization of a Brain Region That Sequences a Learned Pattern of Behavior

Neural activity within HVC (proper name), a premotor nucleus of the songbird telencephalon analogous to premotor cortical regions in mammals, controls the temporal structure of learned song in male zebra finches (Taeniopygia guttata). HVC is composed of a superficially isomorphic neuronal mosaic, implying that song is encoded in a distributed network within HVC. Here, we combined HVC microlesions (10% focal ablation) with singing-driven immediate-early gene (IEG) labeling to explore the network architecture of HVC during singing. Microlesions produce a transient disruption of HVC activity that results in a temporary (≈ 1 week) loss of vocal patterning. Results showed an asymmetrical reduction in the density of IEG-labeled cells 3-5 d after microlesions: swaths of unlabeled cells extended rostrally and/or caudally depending on the position of the HVC microlesion. Labeling returned once birds recovered their songs. Axial swaths of unlabeled cells occurred whether microlesions were located at rostral or caudal poles of HVC, indicating that the localized reduction in IEG labeling could not be attributable solely to transection of afferents that enter HVC rostrally. The asymmetrical pattern of reduced IEG labeling could be explained if synaptic connectivity within HVC is organized preferentially within the rostrocaudal axis. In vivo retrograde tracer injections and in vitro stimulation and recording experiments in horizontal slices of HVC confirmed a rostrocaudal organization of HVC neural connectivity. Our findings suggest that HVC contains an axially organized network architecture that may encode the temporal structure of song.