Recent advances in micro technologies have equipped researches with novel tools for interacting with biological molecules and cells. This thesis focuses on the design, fabrication and application of microfluidic platforms for stimuli-responsive drug delivery and the electromechanical characterization of single cells. Stimuli-responsive hydrogels are promising materials for controlled drug delivery due to their ability to respond to changes in local environmental conditions. In particular, nanohydrogel particles have been a topic of considerable interest due to their rapid response times compared to micro and macro-scale counterparts. Owing to their small size and thus low drug-loading capacity, these materials are unsuitable for prolonged drug delivery. To address this issue, stimuli-responsive implantable drug delivery micro devices by integrating microfabricated drug reservoirs with smart nano-hydrogel particles embedded composite membranes have been proposed. In one proposed glucose-responsive micro device, crosslinked glucose oxidase enables the oxidation of glucose into gluconic acid, producing a microenvironment with lower pH values to modulate the pH-responsive nanoparticles. In vitro glucose-responsive drug release profiles were characterized and normoglycemic glucose levels in diabetic rats with device implantation were also recorded. The biophysical properties of single cells have recently been demonstrated as an important indicator of disease diagnosis. Existing technologies are capable of characterizing single parameter either electrical or mechanical rapidly, but not both, which could only collect limited information for cell status evaluation. To address this issue, two microfluidic platforms capable of simultaneously characterizing both the electrical and mechanical properties of single cells based on electrodeformation and integrated impedance spectroscopy with micropipette aspiration have been proposed. In one proposed microfluidic device, a negative pressure was used to suck cells continuously through the aspiration channel with impedance profiles measured. By interpreting impedance profiles, transit time and impedance amplitude ratio can be quantified as cellular mechanical and electrical property indicators. Neural network based cell classification was conducted, demonstrating that two biophysical parameters could provide a higher cell classification success rate than using electrical or mechanical parameter alone.

Recent advances in micro technologies have equipped researches with novel tools for interacting with biological molecules and cells. This thesis focuses on the design, fabrication and application of microfluidic platforms for stimuli-responsive drug delivery and the electromechanical characterization of single cells. Stimuli-responsive hydrogels are promising materials for controlled drug delivery due to their ability to respond to changes in local environmental conditions. In particular, nanohydrogel particles have been a topic of considerable interest due to their rapid response times compared to micro and macro-scale counterparts. Owing to their small size and thus low drug-loading capacity, these materials are unsuitable for prolonged drug delivery. To address this issue, stimuli-responsive implantable drug delivery micro devices by integrating microfabricated drug reservoirs with smart nano-hydrogel particles embedded composite membranes have been proposed. In one proposed glucose-responsive micro device, crosslinked glucose oxidase enables the oxidation of glucose into gluconic acid, producing a microenvironment with lower pH values to modulate the pH-responsive nanoparticles. In vitro glucose-responsive drug release profiles were characterized and normoglycemic glucose levels in diabetic rats with device implantation were also recorded. The biophysical properties of single cells have recently been demonstrated as an important indicator of disease diagnosis. Existing technologies are capable of characterizing single parameter either electrical or mechanical rapidly, but not both, which could only collect limited information for cell status evaluation. To address this issue, two microfluidic platforms capable of simultaneously characterizing both the electrical and mechanical properties of single cells based on electrodeformation and integrated impedance spectroscopy with micropipette aspiration have been proposed. In one proposed microfluidic device, a negative pressure was used to suck cells continuously through the aspiration channel with impedance profiles measured. By interpreting impedance profiles, transit time and impedance amplitude ratio can be quantified as cellular mechanical and electrical property indicators. Neural network based cell classification was conducted, demonstrating that two biophysical parameters could provide a higher cell classification success rate than using electrical or mechanical parameter alone.

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... / 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...

No / This paper presents a circuit model which is used to calibrate the performance of nonlinear RF energy conversion inside a high quality factor resonant cavity with a known nonlinear loading device. The nonlinear radiofrequency energy conversion can be detected by exciting the fundamental operating frequency and observing the second harmonic resonant frequency within a doubly resonant cavity. By implementing the proposed mathematical model, the required input power can be estimated to maximise the chance of detecting the weak second harmonic signal prior to carry out the measurement.