Spiking neural network connectivity and its potential for temporal sensory processing and variable binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well-understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modeling of neural circuits found in the brain.

Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modelling of neural circuits found in the brain. In recent years, much of the focus in neuron modelling has moved to the study of the connectivity of spiking neural networks. Spiking neural networks provide a vehicle to understand from a computational perspective, aspects of the brain’s neural circuitry. This understanding can then be used to tackle some of the historically intractable issues with artificial neurons, such as scalability and lack of variable binding. Current knowledge of feed-forward, lateral, and recurrent connectivity of spiking neurons, and the interplay between excitatory and inhibitory neurons is beginning to shed light on these issues, by improved understanding of the temporal processing capabilities and synchronous behaviour of biological neurons. This research topic aims to amalgamate current research aimed at tackling these phenomena.

A multi-agent-based evolution model of innovation networks in dynamic environments

An innovation network can be considered as a complex adaptive system with evolution affected by dynamic environments. This paper establishes a multi-agent-based evolution model of innovation networks under dynamic settings through computational and logical modeling, and a multi-agent system paradigm. This evolution model is composed of several sub-models of agents' knowledge production by independent innovations in dynamic situations, knowledge learning by cooperative innovations covering agents' heterogeneities, decision-making for innovation selections, and knowledge update considering decay factors. On the basis of above-mentioned sub-models, an evolution rule for multi-agent based innovation network system is given. The proposed evolution model can be utilized to simulate and analyze different scenarios of innovation networks in various dynamic environments and support decision-making for innovation network optimization.

The river solofrana bicycle network: a project of green mobility

The article presents the “LungoSolofrana” project, carried out during the course “Urban and Mobility” in the academic year 2009/2010, held during the bachelor in Environmental Engineering at the University of Naples “Federico II”. The work has also been chosen as a finalist at the “UrbanPromo 2010” contest, the urban and territorial marketing event sponsored by the National Institute of Urban Planning and Urbit which was held in Venice in 2010. The project consists in a green mobility proposal, developed with an approach based on the integration of the environmental redevelopment of a portion of river Solofrana, located in the Salerno Province, and of the renewal of seven local stations of the railway line Mercato San Severino – Nocera Inferiore, including the realization of a cycle-path network for the natural environment fruition. Furthermore the work drew attention to the local and regional administration. The main intent of the project is to integrate sustainable mobility themes with the environment recovery in a territory affected by high environmental troubles. The area includes the municipalities of Nocera Inferiore, Nocera Superiore, Mercato San Severino, Castel San Giorgio and Roccapiemonte, situated in Salerno’s province, with a total population about 114.000 (font Demo ISTAT 2010). The area extension is about 84,30 sqkm and it is crossed by river Solofrana that is the central point of the project idea. The intervention strategy is defined in two kinds of actions: internal and external rail station interventions. The external rail station interventions regard the construction of pedestrian-cycle paths with the scope of increasing the spaces dedicated to cyclists and to pedestrians along the river Solofrana sides and to connect the urban areas with the railway station. In this way, it’s also possible to achieve an urban requalification of the interested area. On the other side, the interventions inside the station , according to Transit Oriented Development principles, aim at redeveloping common spaces with the insertion of new activities and at realizing new automatic cycle parks covered by photovoltaic panels. The project proposal consists of the urban regeneration of small railway stations along the route-Nocera-Codola Mercato San Severino in the province of Salerno, through interventions aimed at improving pedestrian accessibility. The project involves in particular the construction of pedestrian paths protected access to the station and connecting with neighboring towns and installation of innovative bike parking stations in elevation, covering surfaces coated with solar panels and spaces information. The project is aimed to propose a new model of sustainable transport for small and medium shifts as an alternative to private transportation

Dynamic Network Mechanisms of Relational Integration

A prominent hypothesis states that specialized neural modules within the human lateral frontopolar cortices (LFPCs) support “relational integration” (RI), the solving of complex problems using inter-related rules. However, it has been proposed that LFPC activity during RI could reflect the recruitment of additional “domain-general” resources when processing more difficult problems in general as opposed to RI specifi- cally. Moreover, theoretical research with computational models has demonstrated that RI may be supported by dynamic processes that occur throughout distributed networks of brain regions as opposed to within a discrete computational module. Here, we present fMRI findings from a novel deductive reasoning paradigm that controls for general difficulty while manipulating RI demands. In accordance with the domain- general perspective, we observe an increase in frontoparietal activation during challenging problems in general as opposed to RI specifically. Nonetheless, when examining frontoparietal activity using analyses of phase synchrony and psychophysiological interactions, we observe increased network connectivity during RI alone. Moreover, dynamic causal modeling with Bayesian model selection identifies the LFPC as the effective connectivity source. Based on these results, we propose that during RI an increase in network connectivity and a decrease in network metastability allows rules that are coded throughout working memory systems to be dynamically bound. This change in connectivity state is top-down propagated via a hierarchical system of domain-general networks with the LFPC at the apex. In this manner, the functional network perspective reconciles key propositions of the globalist, modular, and computational accounts of RI within a single unified framework.

Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints

Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for dayahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle- o-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the user´s requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method.

Fuzzy Monte Carlo mathematical model for load curtailment minimization in transmission power systems

This paper presents a methodology which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states by Monte Carlo simulation. This is followed by a remedial action algorithm, based on optimal power flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. In order to illustrate the application of the proposed methodology to a practical case, the paper will include a case study for the Reliability Test System (RTS) 1996 IEEE 24 BUS.

Logic programming and fuzzy Monte Carlo for distribution network reconfiguration

This paper present a methodology to choose the distribution networks reconfiguration that presents the lower power losses. The proposed methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modeling for system component outage parameters. The proposed hybrid method using fuzzy sets and Monte Carlo simulation based on the fuzzyprobabilistic models allows catching both randomness and fuzziness of component outage parameters. A logic programming algorithm is applied, once obtained the system states by Monte Carlo Simulation, to get all possible reconfigurations for each system state. To evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation an AC load flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 115 buses distribution network.

Modeling of the lung impedance using a fractional order ladder network with constant phase elements

The self similar branching arrangement of the airways makes the respiratory system an ideal candidate for the application of fractional calculus theory. The fractal geometry is typically characterized by a recurrent structure. This study investigates the identification of a model for the respiratory tree by means of its electrical equivalent based on intrinsic morphology. Measurements were obtained from seven volunteers, in terms of their respiratory impedance by means of its complex representation for frequencies below 5 Hz. A parametric modeling is then applied to the complex valued data points. Since at low-frequency range the inertance is negligible, each airway branch is modeled by using gamma cell resistance and capacitance, the latter having a fractional-order constant phase element (CPE), which is identified from measurements. In addition, the complex impedance is also approximated by means of a model consisting of a lumped series resistance and a lumped fractional-order capacitance. The results reveal that both models characterize the data well, whereas the averaged CPE values are supraunitary and subunitary for the ladder network and the lumped model, respectively.

Building a person home behavior model based on data from smart house sensors 2015

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Cost Allocation Model for Distribution Networks Considering High Penetration of Distributed Energy Resources

The high penetration of distributed energy resources (DER) in distribution networks and the competitiveenvironment of electricity markets impose the use of new approaches in several domains. The networkcost allocation, traditionally used in transmission networks, should be adapted and used in the distribu-tion networks considering the specifications of the connected resources. The main goal is to develop afairer methodology trying to distribute the distribution network use costs to all players which are usingthe network in each period. In this paper, a model considering different type of costs (fixed, losses, andcongestion costs) is proposed comprising the use of a large set of DER, namely distributed generation(DG), demand response (DR) of direct load control type, energy storage systems (ESS), and electric vehi-cles with capability of discharging energy to the network, which is known as vehicle-to-grid (V2G). Theproposed model includes three distinct phases of operation. The first phase of the model consists in aneconomic dispatch based on an AC optimal power flow (AC-OPF); in the second phase Kirschen’s andBialek’s tracing algorithms are used and compared to evaluate the impact of each resource in the net-work. Finally, the MW-mile method is used in the third phase of the proposed model. A distributionnetwork of 33 buses with large penetration of DER is used to illustrate the application of the proposedmodel.

Should Quinta dos Açores expand its retailer network in lisbon?

The primary purpose of this research is to examine the feasibility of expanding Quinta dos Açores retailer network in Lisbon starting from 2015 onwards. A time series model was developed to estimate the company’s future production and sales. A Discounted Cash Flow analysis was also conducted to determine the profitability of this expansion opportunity. Our findings reveal that Quinta dos Açores will face negative results in the first two years of the expansion strategy, but the overall opportunity presents a net positive result of almost three million euros.

The new economy: Essays in network economics and two-sided markets

Following the Introduction, which surveys existing literature on the technology advances and regulation in telecommunications and on two-sided markets, we address specific issues on the industries of the New Economy, featured by the existence of network effects. We seek to explore how each one of these industries work, identify potential market failures and find new solutions at the economic regulation level promoting social welfare. In Chapter 1 we analyze a regulatory issue on access prices and investments in the telecommunications market. The existing literature on access prices and investment has pointed out that networks underinvest under a regime of mandatory access provision with a fixed access price per end-user. We propose a new access pricing rule, the indexation approach, i.e., the access price, per end-user, that network i pays to network j is function of the investment levels set by both networks. We show that the indexation can enhance economic efficiency beyond what is achieved with a fixed access price. In particular, access price indexation can simultaneously induce lower retail prices and higher investment and social welfare as compared to a fixed access pricing or a regulatory holidays regime. Furthermore, we provide sufficient conditions under which the indexation can implement the socially optimal investment or the Ramsey solution, which would be impossible to obtain under fixed access pricing. Our results contradict the notion that investment efficiency must be sacrificed for gains in pricing efficiency. In Chapter 2 we investigate the effect of regulations that limit advertising airtime on advertising quality and on social welfare. We show, first, that advertising time regulation may reduce the average quality of advertising broadcast on TV networks. Second, an advertising cap may reduce media platforms and firms' profits, while the net effect on viewers (subscribers) welfare is ambiguous because the ad quality reduction resulting from a regulatory cap o¤sets the subscribers direct gain from watching fewer ads. We find that if subscribers are sufficiently sensitive to ad quality, i.e., the ad quality reduction outweighs the direct effect of the cap, a cap may reduce social welfare. The welfare results suggest that a regulatory authority that is trying to increase welfare via regulation of the volume of advertising on TV might necessitate to also regulate advertising quality or, if regulating quality proves impractical, take the effect of advertising quality into consideration. 3 In Chapter 3 we investigate the rules that govern Electronic Payment Networks (EPNs). In EPNs the No-Surcharge Rule (NSR) requires that merchants charge at most the same amount for a payment card transaction as for cash. In this chapter, we analyze a three- party model (consumers, merchants, and a proprietary EPN) with endogenous transaction volumes and heterogenous merchants' transactional benefits of accepting cards to assess the welfare impacts of the NSR. We show that, if merchants are local monopolists and the network externalities from merchants to cardholders are sufficiently strong, with the exception of the EPN, all agents will be worse o¤ with the NSR, and therefore the NSR is socially undesirable. The positive role of the NSR in terms of improvement of retail price efficiency for cardholders is also highlighted.

Proteomic and Metabolomic Analyses of Mitochondrial Complex I-deficient Mouse Model Generated by Spontaneous B2 Short Interspersed Nuclear Element (SINE) Insertion into NADH Dehydrogenase (Ubiquinone) Fe-S Protein 4 (Ndufs4) Gene.

Eukaryotic cells generate energy in the form of ATP, through a network of mitochondrial complexes and electron carriers known as the oxidative phosphorylation system. In mammals, mitochondrial complex I (CI) is the largest component of this system, comprising 45 different subunits encoded by mitochondrial and nuclear DNA. Humans diagnosed with mutations in the gene NDUFS4, encoding a nuclear DNA-encoded subunit of CI (NADH dehydrogenase ubiquinone Fe-S protein 4), typically suffer from Leigh syndrome, a neurodegenerative disease with onset in infancy or early childhood. Mitochondria from NDUFS4 patients usually lack detectable NDUFS4 protein and show a CI stability/assembly defect. Here, we describe a recessive mouse phenotype caused by the insertion of a transposable element into Ndufs4, identified by a novel combined linkage and expression analysis. Designated Ndufs4(fky), the mutation leads to aberrant transcript splicing and absence of NDUFS4 protein in all tissues tested of homozygous mice. Physical and behavioral symptoms displayed by Ndufs4(fky/fky) mice include temporary fur loss, growth retardation, unsteady gait, and abnormal body posture when suspended by the tail. Analysis of CI in Ndufs4(fky/fky) mice using blue native PAGE revealed the presence of a faster migrating crippled complex. This crippled CI was shown to lack subunits of the "N assembly module", which contains the NADH binding site, but contained two assembly factors not present in intact CI. Metabolomic analysis of the blood by tandem mass spectrometry showed increased hydroxyacylcarnitine species, implying that the CI defect leads to an imbalanced NADH/NAD(+) ratio that inhibits mitochondrial fatty acid β-oxidation.

Riffing on a Theme: Faculty Experiences With Service-Learning in a Food Security Research Network in Ontario

While service-learning is often said to be beneficial for all those involved—students, community members, higher education institutions, and faculty members—there are relatively few studies of the attraction to, and effect of, service-learning on faculty members. Existing studies have tended to use a survey design, and to be based in the United States. There is a lack of information on faculty experiences with service-learning in Ontario or Canada. This qualitative case study of faculty experiences with service-learning was framed through an Appreciative Inquiry social constructionist approach. The data were drawn from interviews with 18 faculty members who belong to a Food Security Research Network (FSRN) at a university in northern Ontario, reports submitted by the network, and personal observation of a selection of network-related events. This dissertation study revealed how involvement with service-learning created opportunities for faculty learning and growth. The focus on food security and a commitment to the sustainability of local food production was found to be an ongoing attraction to service-learning and a means to engage in and integrate research and teaching on matters of personal and professional importance to these faculty members. The dissertation concludes with a discussion of the FSRN’s model and the perceived value of a themed, transdisciplinary approach to service-learning. This study highlights promising practices for involving faculty in service-learning and, in keeping with an Appreciative Inquiry approach, depicts a view of faculty work at its best.