A Dynamic Neural Field Model of Mesoscopic Cortical Activity Captured with Voltage-Sensitive Dye Imaging

A neural field model is presented that captures the essential non-linear characteristics of activity dynamics across several millimeters of visual cortex in response to local flashed and moving stimuli. We account for physiological data obtained by voltage-sensitive dye (VSD) imaging which reports mesoscopic population activity at high spatio-temporal resolution. Stimulation included a single flashed square, a single flashed bar, the line-motion paradigm – for which psychophysical studies showed that flashing a square briefly before a bar produces sensation of illusory motion within the bar – and moving squares controls. We consider a two-layer neural field (NF) model describing an excitatory and an inhibitory layer of neurons as a coupled system of non-linear integro-differential equations. Under the assumption that the aggregated activity of both layers is reflected by VSD imaging, our phenomenological model quantitatively accounts for the observed spatio-temporal activity patterns. Moreover, the model generalizes to novel similar stimuli as it matches activity evoked by moving squares of different speeds. Our results indicate that feedback from higher brain areas is not required to produce motion patterns in the case of the illusory line-motion paradigm. Physiological interpretation of the model suggests that a considerable fraction of the VSD signal may be due to inhibitory activity, supporting the notion that balanced intra-layer cortical interactions between inhibitory and excitatory populations play a major role in shaping dynamic stimulus representations in the early visual cortex

Representations for Cognitive Vision : a Review of Appearance-Based, Spatio-Temporal, and Graph-Based Approaches

The emerging discipline of cognitive vision requires a proper representation of visual information including spatial and temporal relationships, scenes, events, semantics and context. This review article summarizes existing representational schemes in computer vision which might be useful for cognitive vision, a and discusses promising future research directions. The various approaches are categorized according to appearance-based, spatio-temporal, and graph-based representations for cognitive vision. While the representation of objects has been covered extensively in computer vision research, both from a reconstruction as well as from a recognition point of view, cognitive vision will also require new ideas how to represent scenes. We introduce new concepts for scene representations and discuss how these might be efficiently implemented in future cognitive vision systems

Revealing intra-urban spatial structure through an exploratory analysis by combining road network abstraction model and taxi trajectory data

The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management

An investigation into adaptive power reduction techniques for neural hardware

In light of the growing applicability of Artificial Neural Network (ANN) in the signal processing field [1] and the present thrust of the semiconductor industry towards lowpower SOCs for mobile devices [2], the power consumption of ANN hardware has become a very important implementation issue. Adaptability is a powerful and useful feature of neural networks. All current approaches for low-power ANN hardware techniques are ‘non-adaptive’ with respect to the power consumption of the network (i.e. power-reduction is not an objective of the adaptation/learning process). In the research work presented in this thesis, investigations on possible adaptive power reduction techniques have been carried out, which attempt to exploit the adaptability of neural networks in order to reduce the power consumption. Three separate approaches for such adaptive power reduction are proposed: adaptation of size, adaptation of network weights and adaptation of calculation precision. Initial case studies exhibit promising results with significantpower reduction

Mechanisms underlying different onset patterns of focal seizures

Focal seizures are episodes of pathological brain activity that appear to arise from a localised area of the brain. The onset patterns of focal seizure activity have been studied intensively, and they have largely been distinguished into two types - low amplitude fast oscillations (LAF), or high amplitude spikes (HAS). Here we explore whether these two patterns arise from fundamentally different mechanisms. Here, we use a previously established computational model of neocortical tissue, and validate it as an adequate model using clinical recordings of focal seizures. We then reproduce the two onset patterns in their most defining properties and investigate the possible mechanisms underlying the different focal seizure onset patterns in the model. We show that the two patterns are associated with different mechanisms at the spatial scale of a single ECoG electrode. The LAF onset is initiated by independent patches of localised activity, which slowly invade the surrounding tissue and coalesce over time. In contrast, the HAS onset is a global, systemic transition to a coexisting seizure state triggered by a local event. We find that such a global transition is enabled by an increase in the excitability of the "healthy" surrounding tissue, which by itself does not generate seizures, but can support seizure activity when incited. In our simulations, the difference in surrounding tissue excitability also offers a simple explanation of the clinically reported difference in surgical outcomes. Finally, we demonstrate in the model how changes in tissue excitability could be elucidated, in principle, using active stimulation. Taken together, our modelling results suggest that the excitability of the tissue surrounding the seizure core may play a determining role in the seizure onset pattern, as well as in the surgical outcome

Rising groundwater levels in the Neapolitan area and its impacts on civil engineering structures, agricultural soils and archaeological sites

The rise of groundwater levels (GWLr) is a worldwide phenomenon with several consequences for urban and rural environment, cultural heritage and human health. In this thesis the phenomenon and its effects are analysed in two sectors of the Metropolitan City of Naples (southern Italy). These areas are the central sector of the eastern plain of Naples and the Cumae archaeological site in the western coastal sector of Phlegraean Fields. The triggering mechanism of GWLr is attributed to anthropogenic and natural causes, as the groundwater rebound (GR) process and the relative sea level rise due to volcano-tectonic subsidence of coastal areas. In the eastern plain of Naples, the interruption of pumping for public and private purposes occurred in 1990, leading to a progressive increase of piezometric levels with values up to 16.54 m. Since the end of 2000s, episodes of groundwater flooding (GF) have been registered on underground structures and agricultural soils. The historical piezometric levels and a comprehensive conceptual model of the aquifer have been reconstructed, as well as a first inventory of GF episodes and the hydrogeological controlling factors of GF occurrence have been detected. The economic consequences of GF have been analysed for an experimental building of study area, in which a sharp increment of expenditures has been registered. These costs include technical and legal support, construction and maintenance of GF mitigation measures and electricity consumption. Others GWLr-induced phenomena have been recognised, as ground vertical deformation and variations of the groundwater contamination. A relationship between GWLr and ground uplift emerges from the coupled analysis of piezometric and interferometric data, referred to the 1989-2013 period. The ground deformation occurs in response to the recovery of pore-pressure in the aquifer system, reaching an uplift magnitude up to 40-50 mm. In the 1989-2017 period, the piezometric levels and the concentrations of some natural contaminants in groundwater (Fe, Mn, fluorides) show opposite trends, conversely the same rising trend has been observed with nitrates. These different responses to piezometric rise are related to the lack of mobilization of deep fluids due to the interruption of pumping and to the reduction of the surficial contaminants' time travel caused by a shorter thickness of the vadose zone. In the western sector of Phlegraean Fields, the naturally triggered GWLr has caused GF in the Cumae archaeological site for the last decade, threatening safeguard and conservation of the archaeological heritage. From an integrated hydrogeological, hydrochemical and isotopic survey, a considerable contamination of groundwater resulted, due to the presence of rising highly mineralized fluids, mobilized during pumping periods, and others anthropogenic sources of contamination. Lastly, a novel methodology for groundwater flooding susceptibility (GFS) assessment has been developed by using machine learning techniques and tested in the eastern plain of Naples. Points of GF occurrence have been connected to environmental predisposing factors through Spatial Distribution Models' algorithms to estimate the most prone areas' distribution. Ensemble Models have been carried out to reduce the uncertainty associated with each algorithm and increase its reliability. Mapping of GFS has been realized by dividing occurrence probability values into five classes of susceptibility. Results show an optimal correspondence between GF points' location and the highest classes (93% of GF points falls into high and very high classes). The results of this research provide new knowledge on the GWLr phenomenon that has impacted a large territory of the Metropolitan City of Naples. The methodological approach used can be exported in others hydrogeological contexts to characterize GWLr and its impacts. In addition, the implemented GFS methodology represents a new tool to assist local government authorities, planners and water decision-makers in addressing the problems deriving from GF, and a first step for the evaluation of GF risk as required by Italian and European legislation