Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience

Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.

Molecular Modeling of Membrane Embedded Proteins

Thomas Lemmin

Over the past years, molecular modeling and simulation techniques have had a major impact on experimental life sciences. They are capable of providing accurate insight into microscopic mechanisms, which are usually difficult to investigate experimentally. Moreover, the integration of experimental data with molecular modeling appears to be a promising strategy to better understand complex biological processes. In this thesis, molecular dynamics simulation has been used in combination with experimental data to investigate two transmembrane proteins: (i) the bacterial chemoreceptor PhoQ and (ii) the Amyloid Precursor Protein (APP). (i) Bacterial two-component system PhoQ and bacterial membranes. Two-component systems (TCSs) are signaling complexes essential for bacterial survival and virulence. PhoQ is the histidine kinase chemoreceptor of the PhoQ-PhoP tandem machine that detects the concentration of cationic species at the inner membrane of Gram-negative bacteria. A full understanding of the PhoQ signal transduction mechanism is currently hindered by the lack of a complete atomistic structure. In this thesis project, the first structural model of the transmembrane (TM) portion of PhoQ from Escherichia coli was assembled, by using molecular simulations integrated with cross-linking disulfide scanning data. Its structural and dynamic features induce a concerted displacement of the TM helices at the periplasmic side, which modulates a rotation at the cytoplasmic end. This supports the idea that signal transduction is promoted through a combination of scissoring and rotational movements of the TM helices. Knowledge of this complex mechanism is essential in order to understand how the chemical stimuli sensed by the periplasmic sensor domain trigger, via the relay of the HAMP domain, the histidine auto-phosphorylation and kinase/phosphatase activity at the cytoplasmic end. The PhoQ sensor domain lies in close proximity to the membrane. Interaction with anionic lipids, such as phosphophatidylglycerols (PG) and cardiolipins (CL), are thought to play a key role in PhoQ activity. Present in bacterial and mitochondrial membranes, cardiolipins have a unique dimeric structure, which carries up to two charges, i.e. one per phosphate group, and under physiological conditions, can be unprotonated or singly protonated. Exhaustive models and characterization of cardiolipins are to-date scarce; therefore an ab initio parameterization of cardiolipin species for molecular simulation consistent with commonly used force fields is proposed here. Molecular dynamics (MD) simulations based on these models indicate a protonation-dependent lipid packing. A noteworthy interaction with solvating mono- and divalent cations is also observed. The proposed models will contribute to the biophysical and biochemical characterizations of bacterial and mitochondrial membranes and membrane-embedded proteins. (ii) Structural and dynamic properties of the Amyloid Precursor Protein. The Amyloid Precursor Protein (APP) is a type I membrane glycoprotein present at the neuronal synapsis. The proteolytic cleavage of its C-terminal segment produces amyloid-β (Aβ) peptides of different lengths, the deposition of which is an early indicator of Alzheimer"s disease (AD). Recently, the backbone structure of the APP transmembrane (TM) domain in detergent micelles was determined by nuclear magnetic resonance (NMR, independently by two different experimental groups). The TM conformations of these two structures are however markedly different. One is characterized by a highly kinked α-helix, whereas the other is mainly straight. Molecular dynamics simulations showed that the APP TM region is highly flexible and its secondary structure is influenced by the surrounding lipid environment. The size of the embedding detergent micelles strongly affects the conformation of the APP α-helix, with solvation being the main driving force for the development of a helical curvature. Once embedded in a membrane bilayer, APP systematically prefers a straight helical conformation. This is also confirmed when analyzing in silico the atomistic APP population observed in double electron-electron resonance (DEER) spectroscopy. In summary, the APP transmembrane domain is highly flexible due to the presence of glycine residues and can readily respond to the lipid environment, a property that might be critical for proteolytic processing by γ-secretase enzymes. The presented thesis work clearly shows how molecular simulations and their interplay with available experimental input can help advance the understanding of the mechanism of complex biological systems and processes on a molecular scale. These results, in particular, go well beyond the current understanding of the functioning of two transmembrane proteins relevant for human health. Furthermore, the computational approaches and procedures developed in these projects will hopefully promote novel integrated strategies for investigating biological systems.

EPFL2013

Motor-Primed Visual Attention for Humanoid Robots

Aude Billard, Luka Lukic, José Santos-Victor

We present a novel, biologically inspired, approach to an efficient allocation of visual resources for humanoid robots in a form of a motor-primed visual attentional landscape. The attentional landscape is a more general, dynamic and a more complex concept of an arrangement of spatial attention than the popular "attentional spotlight" or "zoom-lens" models of attention. Motor-priming of attention is a mechanism for prioritizing visual processing to motor-relevant parts of the visual field, in contrast to other, motor-irrelevant, parts. In particular, we present two techniques for constructing a visual "attentional landscape". The first, more general, technique, is to devote visual attention to the reachable space of a robot (peripersonal space-primed attention). The second, more specialized, technique is to allocate visual attention with respect to motor plans of the robot (motor plans-primed attention). Hence, in our model, visual attention is not exclusively defined in terms of visual saliency in color, texture or intensity cues, it is rather modulated by motor information. This computational model is inspired by recent findings in visual neuroscience and psychology. In addition to two approaches to constructing the attentional landscape, we present two methods for using the attentional landscape for driving visual processing. We show that motor-priming of visual attention can be used to very efficiently distribute limited computational resources devoted to the visual processing. The proposed model is validated in a series of experiments conducted with the iCub robot, both using the simulator and the real robot.

Ieee-Inst Electrical Electronics Engineers Inc2015

The Learnability of the Grammar of Jazz: Bayesian Inference of Hierarchical Structures in Harmony

Daniel Harasim

Musical grammar describes a set of principles that are used to understand and interpret the structure of a piece according to a musical style. The main topic of this study is grammar induction for harmony --- the process of learning structural principles from the observation of chord sequences. The question how grammars are learnable by induction from sequential data is an instance of the more general question how abstract knowledge is inducible from the observation of data --- a central question of cognitive science. Under the assumption that human learning approximately follows the principles of rational reasoning, Bayesian models of cognition can be used to simulate learning processes. This study investigates what prior knowledge makes it possible to learn musical grammar inductively from Jazz chord sequences using Bayesian models and computational simulations. The theoretical part of the thesis presents how questions about learnability can be studied in a unified framework involving music analysis, cognitive modeling, Bayesian statistics, and computational simulations. A new grammar formalism, called Probabilistic Abstract Context-Free Grammar (PACFG), is proposed that allows for flexible probability models which facilitate the grammar-induction experiments of this study. PACFG can jointly model multiple musical dimensions such as harmony and rhythm, and can use coordinate ascent variational inference for grammar learning. The empirical part of the thesis reports supervised and unsupervised grammar-learning experiments. To train and evaluate grammar models, a ground-truth dataset of hierarchical analyses of complete Jazz standards, called the Jazz Harmony Treebank (JHT), was created. The supervised grammar-learning experiments, in which grammars for Jazz harmony are learned from the JHT analyses, show that jointly modeling harmony and rhythm significantly improves the grammar models' prediction of the ground truth. The performance and robustness of the grammars are further improved by a transpositionally invariant parameterization of rule probabilities. Following the supervised grammar learning, unsupervised grammar learning was performed by inducing harmony grammars merely from Jazz chord sequences, without the observation of the JHT trees. The results show that the best induced grammar performs similarly well as the best supervised grammar. In particular, the goal-directedness of functional harmony does not need to be assumed a priori, but can be learned without usage of music-specific prior knowledge. The findings of this thesis show that general prior knowledge enables an ideal learner to acquire abstract musical principles by statistical learning. In conclusion, it is plausible that much aspects of musical grammar have been learned by Jazz musicians and listeners, instead of being innate predispositions or explicitly taught concepts. This thesis is moreover embedded into the context of empirical music research and digital humanities. Current studies either describe complex musical structures qualitatively or investigate simpler aspects quantitatively. The computational models developed in this thesis demonstrate that deep insights into music and statistical analyses are not mutually exclusive. They enable a new kind of data-driven music theory and musicology, for instance through comparative analyses of musical grammar for different styles such as Jazz, Rock, and Western classical music.