Letteratura scientifica selezionata sul tema "Biophysical dynamics"

The aim of this thesis is to take a broad conceptual overview of the global energy system and investigate what the aims of sustainability might entail for such a system. The work presented uses a biophysical economic approach in that the dynamics of the global economy are investigated using the tool box of the physical sciences, including the laws of thermodynamics and the methods of energy analysis. Modern society currently uses approximately 500 exajoules (EJ = 10^18 J) of total primary energy supply (TPES) each year. This energy consumption has been increasing at roughly 2% per year for the past two hundred years. TPES is currently dominated by three non-renewable energy sources: coal, oil and gas which, together with energy from nuclear fission of uranium, make up around 85% of the energy market. Consumption of finite resources at a continuously growing rate is not sustainable in the long-term. A trend in policy direction is to seek a transition to renewable sources of energy. This thesis seeks to explore two questions: are the technical potentials of renewable energy sources enough to supply the current and/or projected demand for energy and what would be the effect on the physical resource economy of a transition to an energy supply system run entirely on renewable energy sources? The Global Energy Model using a Biophysical Approach (GEMBA) methodology developed here is compared and contrasted with other approaches that are used to study the global energy-economy system, including the standard neoclassical economic approach used in such models as MESSAGE and MARKAL. A number of meta-analyses have been conducted in support of the GEMBA model. These include: meta-analysis of historic energy production from all energy sources; meta-analysis of global energy resources for all energy sources; meta-analysis of energy-return-on-investment (EROI) for all energy sources. The GEMBA methodology uses a systems dynamic modelling approach utilising stocks and flows, feedback loops and time delays to capture the behaviour of the global energy-economy system. The system is decomposed into elements with simple behaviour that is known through energy analysis. The interaction of these elements is captured mathematically and run numerically via the systems dynamics software package, VenSim. Calibration of the model has been achieved using historic energy production data from 1800 to 2005. The core of the GEMBA methodology constitutes the description of a dynamic EROI function over the whole production cycle of an energy resource from initial development, through maturation to decline in production, in the case of non-renewable resources, or to the technical potential in the case of renewable resources. Using the GEMBA methodology, the global energy-economy system is identified as a self-regulating system. The self-regulating behaviour acts to constrain the amount of total primary energy supply that the system can produce under a renewable-only regime. A number of analyses are conducted to test the sensitivity of the system to such changes as: an increase of the technical potential of renewable resources; technological breakthroughs which would significantly increase the EROI of renewable resources; a decrease in the capital intensity of renewable resources and; an increase in the energy intensity of the economy, A statistical analysis reflecting the wide range of values of both the estimates of EROI and technical potentials of renewable energy sources has also been undertaken using a Monte Carlo approach. The results from the modelling suggest that not all levels of energy demand projected by the WEA can be supplied by an energy system running solely on renewable energy. The Monte Carlo analyses suggest that reduction in total energy yield over current (2010) levels might occur with a 20-30% possibility. The middle and high growth scenarios from the WEA are greater than 95% of all scenarios modelled, hence seem unlikely to be sustained by an energy system running solely on renewable energy. This finding has implications for the future direction of both engineering and technology research as well as for energy policy. These implications are discussed.

Le néocortex possède un état activé dans lequel l'activité corticalemanifeste un comportement complexe. Au niveau cellulaire, l'activitéest caractérisée par de fortes fluctuations sous-liminaires dupotential membranaire et une décharge irrégulière à bassefréquence. Au niveau du réseau, l'activité est marquée par un faibleniveau de synchronie et une dynamique chaotique. Néanmoins, c'est dansce régime que l'information est traitée de manière fiable par lesréseaux neuronaux. Ce régime est donc crucial pour le traitement del'information par le cortex. Dans cette thèse, nous contribuons à sacompréhension en examinant comment les propriétés biophysiques auniveau cellulaire combinées avec les propriétés d'architecture desréseaux façonnent cette dynamique asynchrone.Cette thèse repose sur les modèles de dynamique de réseaux appelésmodèles de champ moyen, un formalisme théorique qui décrit ladynamique de population grâce à une approche auto-consistante. Aucoeur de ce formalisme se trouve la fonction de transfertneuronale : la fonction entrée-sortie d'un neurone. La première partiede cette thèse s'attache à dériver des fonctions de transfertbiologiquement réalistes en incorporant des caractérisationsexpérimentales.Dans un premier temps, nous avons examiné in vitro comment lesneurones néocorticaux pyramidaux de la couche V du cortex visuelrépondent à des fluctuations du potentiel membranaire. Nous avonsobservé que les neurones individuels ne diffèrent pas seulement entermes d'excitabilité, mais qu'ils diffèrent aussi par leurssensibilités aux paramètres des fluctuations. Dans un deuxième temps,nous avons étudié de manière théorique comment l'intégrationdendritique dans des structures arborescentes façonne les fluctuationsau soma. Nous avons observé que, en fonction des propriétés del'activité présynaptique, différentes comodulations des paramètres desfluctuations pouvaient être obtenues. En combinant cette observationavec nos mesures expérimentales, nous avons observé que cela induisaitdes couplages différents entre activité synaptique et déchargeneuronale pour chaque neurone. Nous proposons donc que, puisque cemécanisme offre un moyen d'activer spécifiquement certains neurones enfonction des propriétés de l'entrée, l'hétérogénéité biophysiquepourrait contribuer à l'encodage de propriétés des stimuli dans lestraitements de l'information sensorielle.La deuxième partie de cette thèse examine comment les propriétésd'architecture des réseaux neuronaux se combinent avec les propriétésbiophysiques et affectent les réponses sensorielles via des effets dedynamiques de populations.Nous avons tout d'abord examiné de manière théorique comment un hautniveau d'activité spontanée impactait les réponses post-synaptiquesdans le cortex. Nous avons observé que la compétition entre lerecrutement dans le réseau cortical activé et les effets deconductances associés prédisaient une relation non-triviale entrel'intensité des stimuli et l'amplitude des réponses. Cette prédictionfut observée dans des enregistrements de réponses post-synaptiquesdans le cortex auditif du rat in vivo en réponse à des stimulicorticaux, thalamiques et auditifs.Pour finir, en tirant avantage des approches de champ moyen, nousavons construit un modèle grande échelle du réseau des couches II-IIIincluant le réseau des fibres horizontales. Nous avons examiné lespropriétés intégratives spatio-temporelles du modèle et nous les avonscomparées avec des mesures par imagerie optique de l'activitécérébrale chez le singe éveillé. En particulier, nous avonsreconstruit une expérience typique du traitement sensoriel: lemouvement apparent. Le modèle prédit un fort signal suppressif dont leprofil spatio-temporel correspond quantitativement à celui observé invivo<br>The neocortex of awake animals displays an activated state in whichcortical activity manifests highly complex, seemingly noisybehavior. At the level of single neurons the activity is characterizedby strong subthreshold fluctuations and irregular firing at lowrate. At the network level, the activity is weakly synchronized andexhibits a chaotic dynamics. Yet, it is within this regime thatinformation is processed reliably through neural networks. This regimeis thus crucial to neural computation. In this thesis, we contributeto its understanding by investigating how the biophysical propertiesat the cellular level combined with the properties of the networkarchitecture shapes this asynchronous dynamics.This thesis builds up on the so-called mean-field models of networkdynamics, a theoretical formalism that describes population dynamicsvia a self-consistency approach. At the core of this formalism lie theneuronal transfer function: the input-output description of individualneurons. The first part of this thesis focuses on derivingbiologically-realistic neuronal transfer functions. We firstformulate a two step procedure to incorporate biological details (suchas an extended dendritic structure and the effect of various ionicchannels) into this transfer function based on experimentalcharacterizations.First, we investigated in vitro how layer V pyramidal neocorticalneurons respond to membrane potential fluctuations on a cell-by-cellbasis. We found that, not only individual neurons strongly differ interms of their excitability, but also, and unexpectedly, in theirsensitivities to fluctuations. In addition, using theoreticalmodeling, we attempted to reproduce these results. The model predictsthat heterogeneous levels of biophysical properties such as sodiuminactivation, sharpness of sodium activation and spike frequencyadaptation account for the observed diversity of firing rateresponses.Then, we studied theoretically how dendritic integration in branchedstructures shape the membrane potential fluctuations at the soma. Wefound that, depending on the type of presynaptic activity, variouscomodulations of the membrane potential fluctuations could beachieved. We showed that, when combining this observation with theheterogeneous firing responses found experimentally, individual neuronsdifferentially responded to the different types of presynapticactivities. We thus propose that, because this mechanism offers a wayto produce specific activation as a function of the input properties,biophysical heterogeneity might contribute to the encoding of the stimulusproperties during sensory processing in neural networks.The second part of this thesis investigates how circuit properties,such as recurrent connectivity and lateral connectivity, combine withbiophysical properties to impact sensory responses through effectsmediated by population dynamics.We first investigated what was the effect of a high level of ongoingdynamics (the Up-state compared to the Down-state) on the scaling ofpost-synaptic responses. We found that the competition between therecruitment within the active recurrent network (in favor of highresponses in the Up-state) and the increased conductance level due tobackground activity (in favor of reduced responses in the Up-state)predicted a non trivial stimulus-response relationship as a functionof the intensity of the stimulation. This prediction was shown toaccurately capture measurements of post-synaptic membrane potentialresponses in response to cortical, thalamic or auditory stimulation inrat auditory cortex in vivo.Finally, by taking advantage of the mean-field approach, weconstructed a tractable large-scale model of the layer II-III networkincluding the horizontal fiber network. We investigate thespatio-temporal properties of this large-scale model and we compareits predictions with voltage sensitive dye imaging in awake fixatingmonkey

For many species such as the blue crab, Callinectes sapidus, successful estuarine recruitment to juvenile nursery habitats is dependent on the biophysical processes experienced during dispersal of the early life stages. The goal of this study was to determine how blue crab primary (postlarval) and secondary (early juvenile) dispersal occurs within a predominately wind-driven estuary, Pamlico Sound, North Carolina, USA. We (1) characterized circulation patterns in Pamlico Sound during the fall blue crab recruitment months over two consecutive years using current meters (2) sampled during multiple 24 h periods to relate spatiotemporal water column distributions of postlarval and early juveniles blue crabs with circulation patterns, and used a hydrodynamic model to recreate dispersal trajectories from eastern (inlet) to western sound nursery habitats and (3) examined the environmental (wind, diel cycle, tidal phase) and biological (ontogenetic, density-dependent) factors that contribute to early juvenile blue crab secondary dispersal from near-inlet nursery habitats. During our study, surface currents responded synchronously to wind-forcing by generally flowing in the same direction as the wind. Particle-tracking simulations suggested that dispersal from Oregon and Hatteras Inlets to across-sound nursery habitats resulted from the combined use of tidal and wind-driven currents. Simulation results and observed crab distributions further indicated that Oregon Inlet was the primary supplier of postlarval blue crabs (dispersing in surface waters at night) throughout Pamlico Sound, as postlarvae ingressing through Hatteras Inlet were not retained within our study area. Furthermore, Oregon Inlet supplied early juvenile blue crabs (dispersing in bottom waters at night) to northwestern sound habitats, while crabs from Hatteras Inlet dispersed to mid- and eastern-sound regions. Results from our study in near-inlet settlement habitats confirmed the importance of tides to mediating dispersal partway into Pamlico Sound, as early juvenile blue crabs responded to increasing conspecific density in settlement habitats by using flood-tide transport near the inlets to rapidly leave these habitats. Based on our findings, we make recommendations regarding the prioritization of nursery habitats for conservation and fisheries management.