Signal denoising through topographic modularity of neural circuits

Information from the sensory periphery is conveyed to the cortex via structured projection pathways that spatially segregate stimulus features, providing a robust and efficient encoding strategy. Beyond sensory encoding, this prominent anatomical feature extends throughout the neocortex. However, the extent to which it influences cortical processing is unclear. In this study, we combine cortical circuit modeling with network theory to demonstrate that the sharpness of topographic projections acts as a bifurcation parameter, controlling the macroscopic dynamics and representational precision across a modular network. By shifting the balance of excitation and inhibition, topographic modularity gradually increases task performance and improves the signal-to-noise ratio across the system. We show that this is a robust and generic structural feature that enables a broad range of behaviorally-relevant operating regimes, and provide an in-depth theoretical analysis unravelling the dynamical principles underlying the mechanism.

Ship Type Recognition Based on Ship Navigating Trajectory and Convolutional Neural Network

With the aim to solve the problem of missing or tampering of ship type information in AIS information, in this paper, a novel ship type recognition scheme based on ship navigating trajectory and convolutional neural network (CNN) is proposed. Firstly, according to speed and acceleration of the ship, three ship navigating situations, i.e., static, normal navigation and maneuvering, are integrated into the process of trajectory images generation in the form of pixels. Then, three kinds of modular network structures with different depths are trained and optimized to determine the appropriate convolutional neural network structure. In the validation phase of the model, a large amount of verified data with a time span of one month was used, covering a variety of water conditions including open water, ports, rivers and lakes. Following this approach, a kind of CNN scheme which can be directly used to identify ship types in a wide range of waters is proposed. This scheme can be used to judge the ship type when the static information is completely missing and to test the data when the ship type information is partially missing.

Modularity and Dynamics on Complex Networks

Complex networks are typically not homogeneous, as they tend to display an array of structures at different scales. A feature that has attracted a lot of research is their modular organisation, i.e., networks may often be considered as being composed of certain building blocks, or modules. In this Element, the authors discuss a number of ways in which this idea of modularity can be conceptualised, focusing specifically on the interplay between modular network structure and dynamics taking place on a network. They discuss, in particular, how modular structure and symmetries may impact on network dynamics and, vice versa, how observations of such dynamics may be used to infer the modular structure. They also revisit several other notions of modularity that have been proposed for complex networks and show how these can be related to and interpreted from the point of view of dynamical processes on networks.

Cortical Cartography: Mapping Arealization Using Single-Cell Omics Technology

The cerebral cortex derives its cognitive power from a modular network of specialized areas processing a multitude of information. The assembly and organization of these regions is vital for human behavior and perception, as evidenced by the prevalence of area-specific phenotypes that manifest in neurodevelopmental and psychiatric disorders. Generations of scientists have examined the architecture of the human cortex, but efforts to capture the gene networks which drive arealization have been hampered by the lack of tractable models of human neurodevelopment. Advancements in “omics” technologies, imaging, and computational power have enabled exciting breakthroughs into the molecular and structural characteristics of cortical areas, including transcriptomic, epigenomic, metabolomic, and proteomic profiles of mammalian models. Here we review the single-omics atlases that have shaped our current understanding of cortical areas, and their potential to fuel a new era of multi-omic single-cell endeavors to interrogate both the developing and adult human cortex.

Bazaar in the 'Burbs: Infilling fine grain of activity in the course grain context of Paraparaumu

<p>The regional townships of New Zealand are losing young people. The township of Paraparaumu, located along the Kapiti Coast, is no exception. As a sprawling, low-density suburban settlement with its town center being Coastlands Shopping Center - the local mall - there are few job opportunities available. As a result, many early career adults choose to settle elsewhere. Tasked with creating more opportunities, the Kapiti Coast District Council plans to build a new commercial district. To make space for it, this will be done by paving over a large expanse of wetland adjacent to the mall. The premise of this thesis is that generating opportunities do not have to be large scale. In more dense urban areas where space is limited, many productive activities occur within the fine grain of a city. Wetlands are also recognized as a critical natural infrastructure and a valuable social amenity. Thus, instead of building large commercial facilities that have to occupy the wetland, the design in this thesis proposes a facility made up of a finer grain and infills the glut of car park spaces in front of Coastlands Mall. The parking spaces displaced will be relocated into a parking tower adjacent to the site. The building type of the Bazaar was looked at in this thesis as a model, for it is fine-grained and also ingrained with its urban context. The spatial network of the Bazaar democratizes access. The spatial network of the Bazaar democratizes access, which is a direct contrast to the singular and hierarchical nature of the mall. The design adopts these ideas and expresses them through a network of modules on a tartan grid plan transforming the design into a rhythmic series of spaces that express compression and expansion, allowing it to be an interlinked network of interior and exterior spaces. The grid is a powerful tool for organizing expanses of space though it is only useful in an architectural sense when accompanied by a fine-grained variation. Though the repetitive grid is suitable in plan, as a 3d form it quickly dissolves into monotony when repeated across a field. Similarly, the site itself is inherently charged with spatial hierarchy. Thus, localized adjustments of the roof and exterior details were made to break the monotony and rest the spatial hierarchy. This thesis explores how fine grain activity can be integrated into a large-grained context through the use of an additive, modular network set on a grid. Though the research findings produced on expression of this in the design outcome, the idea of a dense, fine-grained modular network is applicable in any context that has large inactive open space to be filled.</p>

Bazaar in the 'Burbs: Infilling fine grain of activity in the course grain context of Paraparaumu

<p>The regional townships of New Zealand are losing young people. The township of Paraparaumu, located along the Kapiti Coast, is no exception. As a sprawling, low-density suburban settlement with its town center being Coastlands Shopping Center - the local mall - there are few job opportunities available. As a result, many early career adults choose to settle elsewhere. Tasked with creating more opportunities, the Kapiti Coast District Council plans to build a new commercial district. To make space for it, this will be done by paving over a large expanse of wetland adjacent to the mall. The premise of this thesis is that generating opportunities do not have to be large scale. In more dense urban areas where space is limited, many productive activities occur within the fine grain of a city. Wetlands are also recognized as a critical natural infrastructure and a valuable social amenity. Thus, instead of building large commercial facilities that have to occupy the wetland, the design in this thesis proposes a facility made up of a finer grain and infills the glut of car park spaces in front of Coastlands Mall. The parking spaces displaced will be relocated into a parking tower adjacent to the site. The building type of the Bazaar was looked at in this thesis as a model, for it is fine-grained and also ingrained with its urban context. The spatial network of the Bazaar democratizes access. The spatial network of the Bazaar democratizes access, which is a direct contrast to the singular and hierarchical nature of the mall. The design adopts these ideas and expresses them through a network of modules on a tartan grid plan transforming the design into a rhythmic series of spaces that express compression and expansion, allowing it to be an interlinked network of interior and exterior spaces. The grid is a powerful tool for organizing expanses of space though it is only useful in an architectural sense when accompanied by a fine-grained variation. Though the repetitive grid is suitable in plan, as a 3d form it quickly dissolves into monotony when repeated across a field. Similarly, the site itself is inherently charged with spatial hierarchy. Thus, localized adjustments of the roof and exterior details were made to break the monotony and rest the spatial hierarchy. This thesis explores how fine grain activity can be integrated into a large-grained context through the use of an additive, modular network set on a grid. Though the research findings produced on expression of this in the design outcome, the idea of a dense, fine-grained modular network is applicable in any context that has large inactive open space to be filled.</p>

Resonant Learning in Scale-free Networks

Over the last decades, analyses of the connectivity of large biological and artificial networks have identified a common scale-free topology, where few of the network elements, called hubs, control many other network elements. In monitoring the dynamics of networks hubs, recent experiments have revealed that they can show behaviors oscillating between ON and OFF states of activation. Prompted by these observations, we ask whether the existence of oscillatory hubs states could contribute to the emergence of specific network dynamical behaviors. Here, we use Boolean threshold networks with scale-free architecture as representative models to demonstrate how periodic activation of the network hub can provide a network-level advantage in learning specific new dynamical behaviors. First, we find that hub oscillations with distinct periods can induce robust and distinct attractors whose lengths depend upon the hub oscillation period. Second, we determine that a given network can exhibit series of different attractors when we sequentially change the period of hub pulses. Using rounds of evolution and selection, these different attractors could independently learn distinct target functions. We term this network-based learning strategy resonant learning, as the emergence of new learned dynamical behaviors depends on the choice of the period of the hub oscillations. Finally, we find that resonant learning leads to convergence towards target behaviors over an order of magnitude faster than standard learning procedures. While it is already known that modular network architecture contributes to learning separate tasks, our results reveal an alternative design principle based on forced oscillations of the network hub.