Cardiac cell modelling: Observations from the heart of the cardiac physiome project

In this manuscript we review the state of cardiac cell modelling in the context of international initiatives such as the IUPS Physiome and Virtual Physiological Human Projects, which aim to integrate computational models across scales and physics. In particular we focus on the relationship between experimental data and model parameterisation across a range of model types and cellular physiological systems. Finally, in the context of parameter identification and model reuse within the Cardiac Physiome, we suggest some future priority areas for this field

Evolution of central pattern generators for the control of a five-link bipedal walking mechanism

Central pattern generators (CPGs), with a basis is neurophysiological studies, are a type of neural network for the generation of rhythmic motion. While CPGs are being increasingly used in robot control, most applications are hand-tuned for a specific task and it is acknowledged in the field that generic methods and design principles for creating individual networks for a given task are lacking. This study presents an approach where the connectivity and oscillatory parameters of a CPG network are determined by an evolutionary algorithm with fitness evaluations in a realistic simulation with accurate physics. We apply this technique to a five-link planar walking mechanism to demonstrate its feasibility and performance. In addition, to see whether results from simulation can be acceptably transferred to real robot hardware, the best evolved CPG network is also tested on a real mechanism. Our results also confirm that the biologically inspired CPG model is well suited for legged locomotion, since a diverse manifestation of networks have been observed to succeed in fitness simulations during evolution.Comment: 11 pages, 9 figures; substantial revision of content, organization, and quantitative result

LeggedWalking on Inclined Surfaces

The main contribution of this MS Thesis is centered around taking steps towards successful multi-modal demonstrations using Northeastern's legged-aerial robot, Husky Carbon. This work discusses the challenges involved in achieving multi-modal locomotion such as trotting-hovering and thruster-assisted incline walking and reports progress made towards overcoming these challenges. Animals like birds use a combination of legged and aerial mobility, as seen in Chukars' wing-assisted incline running (WAIR), to achieve multi-modal locomotion. Chukars use forces generated by their flapping wings to manipulate ground contact forces and traverse steep slopes and overhangs. Husky's design takes inspiration from birds such as Chukars. This MS thesis presentation outlines the mechanical and electrical details of Husky's legged and aerial units. The thesis presents simulated incline walking using a high-fidelity model of the Husky Carbon over steep slopes of up to 45 degrees.Comment: Masters thesi

Dynamic Walking: Toward Agile and Efficient Bipedal Robots

Dynamic walking on bipedal robots has evolved from an idea in science fiction to a practical reality. This is due to continued progress in three key areas: a mathematical understanding of locomotion, the computational ability to encode this mathematics through optimization, and the hardware capable of realizing this understanding in practice. In this context, this review article outlines the end-to-end process of methods which have proven effective in the literature for achieving dynamic walking on bipedal robots. We begin by introducing mathematical models of locomotion, from reduced order models that capture essential walking behaviors to hybrid dynamical systems that encode the full order continuous dynamics along with discrete footstrike dynamics. These models form the basis for gait generation via (nonlinear) optimization problems. Finally, models and their generated gaits merge in the context of real-time control, wherein walking behaviors are translated to hardware. The concepts presented are illustrated throughout in simulation, and experimental instantiation on multiple walking platforms are highlighted to demonstrate the ability to realize dynamic walking on bipedal robots that is agile and efficient

Modèle et simulateur à grande échelle d'une rétine biologique, avec contrôle de gain

The retina is a complex neural structure. The characteristics of retinal processing are reviewed extensively in Part I of this work: It is a very ordered structure, which proceeds to band-pass spatio-temporal enhancements of the incoming light, along different parallel output pathways with distinct spatio-temporal properties. The spike trains emitted by the retina have a complex statistical structure, such that precise spike timings may play a role in the code conveyed by the retina. Several mechanisms of gain control provide a constant adaptation of the retina to luminosity and contrast. The retina model that we have defined and implemented in Part II can account for a good part of this complexity. It can model spatio-temporal band-pass behavior with adjustable filtering scales, with the inclusion of plausible mechanisms of contrast gain control and spike generation. The gain control mechanism proposed in the model provides a good fit to experimental data, and it can induce interesting effects of local renormalization in the output retinal image. Furthermore, a mathematical analysis confirms that the gain control behaves well under simple sinusoidal stimulation. Finally, the simulator /Virtual Retina/ implements the model on a large-scale, so that it can emulate up to around 100,000 cells with a processing speed of about 1/100 real time. It is ready for use in various applications, while including a number of advanced retinal functionalities which are too often overlooked.La rétine est une structure neuronale complexe, qui non seulement capte la lumière incidente au fond de l'oeil, mais procède également à des transformations importantes du signal lumineux. Dans la Partie I de ce travail, nous résumons en détail les caractéristiques fonctionnelles de la rétine des vertébrés: Il s'agit d'une structure très ordonnée, qui réalise un filtrage passe-bande du stimulus visuel, selon différents canaux parallèles d'information aux propriétés spatio-temporelles distinctes. Les trains de potentiels d'action émis par la rétine ont également une structure statistique complexe, susceptible de véhiculer une information importante. De nombreux mécanismes de contrôle de gain permettent une adaptation constante à la luminosité et au contraste. Le modèle de rétine défini et implémenté dans la Partie II de ce travail prend en compte une part importante de cette complexité. Il reproduit le comportement passe-bande, à l'aide de filtres linéaires spatio-temporels appropriés. Des mécanismes non-linéaires d'adaptation au contraste et de génération de potentiels d'action sont également inclus. Le mécanisme de contrôle du gain au contraste proposé permet une bonne reproduction des données expérimentales, et peut également véhiculer d'importants effets d'égalisation spatiale des contrastes en sortie de rétine. De plus, une analyse mathématique confirme que notre mécanisme a le comportement escompté en réponse à une stimulation sinusoïdale. Enfin, le simulateur /Virtual Retina/ implémente le modèle à grande échelle, permettant la simulation d'environ 100 000 cellules en un temps raisonnable (100 fois le temps réel)

Doctor of Philosophy

dissertationFocused ultrasound (FUS) is a promising noninvasive and radiation-free cancer therapy that selectively delivers high-intensity acoustic energy to a small target volume. This dissertation presents original research that improves the speed, safety, and efficacy of FUS therapies under magnetic resonance imaging (MRI) guidance. First, a new adaptive model-predictive controller is presented that leverages the ability of MRI to measure temperature inside the patient at near real-time speeds. The controller uses MR temperature feedback to dynamically derive and update a patient-specific thermal model, and optimizes the treatment based on the model's predictions. Treatment safety is a key element of the controller's design, and it can actively protect healthy tissue from unwanted damage. In vivo and simulation studies indicate the controller can safeguard healthy tissue and accelerate treatments by as much as 50%. Significant tradeoffs exist between treatment speed, and safety, which makes a real-time controller absolutely necessary for carrying out efficient, effective, and safe treatments while also highlighting the importance of continued research into optimal treatment planning. Next, two new methods for performing 3D MR acoustic radiation force imaging (MR-ARFI) are presented. Both techniques measure the tissue displacement induced by short bursts of focused ultrasound, and provide a safe way to visualize the ultrasound beam's location. In some scenarios, ARFI is a necessity for proper targeting since traditional MR thermometry cannot measure temperature in fat. The first technique for performing 3D ARFI introduces a novel unbalanced bipolar motion encoding gradient. The results demonstrate that this technique is safe, and that 3D displacement maps can be attained time-efficiently even in organs that contain fat, such as breast. The second technique measures 3D ARFI simultaneously with temperature monitoring. This method uses a multi-contrast gradient recalled echo sequence which makes multiple readings of the data without increasing scan time. This improves the signal to noise ratio and makes it possible to separate the effects of tissue heating vs displacement. Both of the 3D MR-ARFI techniques complement the presented controllersince proper positioning of the focal spot is critical to achieving fast and safe treatments