Models for the study of neuronal activity during cognitive tasks

Assessment of brain connectivity among different brain areas during cognitive or motor tasks is a crucial problem in neuroscience today. Aim of this research study is to use neural mass models to assess the effect of various connectivity patterns in cortical EEG power spectral density (PSD), and investigate the possibility to derive connectivity circuits from EEG data. To this end, two different models have been built. In the first model an individual region of interest (ROI) has been built as the parallel arrangement of three populations, each one exhibiting a unimodal spectrum, at low, medium or high frequency. Connectivity among ROIs includes three parameters, which specify the strength of connection in the different frequency bands. Subsequent studies demonstrated that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). For this reason in the second model an individual ROI is simulated only with a single population. Both models have been validated by comparing the simulated power spectral density with that computed in some cortical regions during cognitive and motor tasks. Another research study is focused on multisensory integration of tactile and visual stimuli in the representation of the near space around the body (peripersonal space). This work describes an original neural network to simulate representation of the peripersonal space around the hands, in basal conditions and after training with a tool used to reach the far space. The model is composed of three areas for each hand, two unimodal areas (visual and tactile) connected to a third bimodal area (visual-tactile), which is activated only when a stimulus falls within the peripersonal space. Results show that the peripersonal space, which includes just a small visual space around the hand in normal conditions, becomes elongated in the direction of the tool after training, thanks to a reinforcement of synapses.

Seeing and feeling the body

Recognizing one’s body as separate from the external world plays a crucial role in detecting external events, and thus in planning adequate reactions to them. In addition, recognizing one’s body as distinct from others’ bodies allows remapping the experiences of others onto one’s sensory system, providing improved social understanding. In line with these assumptions, two well-known multisensory mechanisms demonstrated modulations of somatosensation when viewing both one’s own and someone else’s body: the Visual Enhancement of Touch (VET) and the Visual Remapping of Touch (VRT) effects. Vision of the body, in the former, and vision of the body being touched, in the latter, enhance tactile processing. The present dissertation investigated the multisensory nature of these mechanisms and their neural bases. Further experiments compared these effects for viewing one’s own body or viewing another person’s body. These experiments showed important differences in multisensory processing for one’s own body, and for other bodies, and also highlighted interactions between VET and VRT effects. The present experimental evidence demonstrated that a multisensory representation of one’s body – underlie by a high order fronto-parietal network - sends rapid modulatory feedback to primary somatosensory cortex, thus functionally enhancing tactile processing. These effects were highly spatially-specific, and depended on current body position. In contrast, vision of another person’s body can drive mental representations able to modulate tactile perception without any spatial constraint. Finally, these modulatory effects seem sometimes to interact with high order information, such as emotional content of a face. This allows one’s somatosensory system to adequately modulate perception of external events on the body surface, as a function of its interaction with the emotional state expressed by another individual.

Le désert et la littérature de voyage européenne du XIXe siècle

L'Europe commence à découvrir le désert à la fin du XVIIIe siècle. Ce milieu présente des aspects multiples aux premiers explorateurs, qui le décrivent comme un lieu vivant et varié. La géopolitique constitue le premier approche d'analyse : le désert peut être rendu plus viable si mieux administré, donc libre de la domination ottomane. Au cours du siècle, le tourisme se développe, aussi en raison de la colonisation ; l'expérience du désert devient commune, et les voyageurs partent en quête d'une expérience spirituelle aussi bien que d'une aventure. Le désert devient un écran de projection pour la rêverie et les souvenirs des Européens (la Bible et les topos traditionnels demeurent actifs dans l'imaginaire occidental) ; seules certaines caractéristiques de ce milieu sont dès lors appréciées par les voyageurs. Ensuite, avec l'âge du Réalisme, devient objet d'appréciation de la part des peintres. Grâce à la médiation de l'art, la littérature commence à regarder au désert d'un point de vue esthétique, et elle lui reconnaît un charme de la nudité et du dépouillement. La mort et le minéral caractérisent ce milieu vers la fin du siècle ; le désert se vide, et devient le "désert de sable" que nous tous connaissons aujourd'hui.

Permutation-based stochastic ordering using pairwise comparisons

The topic of this work concerns nonparametric permutation-based methods aiming to find a ranking (stochastic ordering) of a given set of groups (populations), gathering together information from multiple variables under more than one experimental designs. The problem of ranking populations arises in several fields of science from the need of comparing G>2 given groups or treatments when the main goal is to find an order while taking into account several aspects. As it can be imagined, this problem is not only of theoretical interest but it also has a recognised relevance in several fields, such as industrial experiments or behavioural sciences, and this is reflected by the vast literature on the topic, although sometimes the problem is associated with different keywords such as: "stochastic ordering", "ranking", "construction of composite indices" etc., or even "ranking probabilities" outside of the strictly-speaking statistical literature. The properties of the proposed method are empirically evaluated by means of an extensive simulation study, where several aspects of interest are let to vary within a reasonable practical range. These aspects comprise: sample size, number of variables, number of groups, and distribution of noise/error. The flexibility of the approach lies mainly in the several available choices for the test-statistic and in the different types of experimental design that can be analysed. This render the method able to be tailored to the specific problem and the to nature of the data at hand. To perform the analyses an R package called SOUP (Stochastic Ordering Using Permutations) has been written and it is available on CRAN.

Visual-somatosensory interactions in mental representations of the body and the face

The body is represented in the brain at levels that incorporate multisensory information. This thesis focused on interactions between vision and cutaneous sensations (i.e., touch and pain). Experiment 1 revealed that there are partially dissociable pathways for visual enhancement of touch (VET) depending upon whether one sees one’s own body or the body of another person. This indicates that VET, a seeming low-level effect on spatial tactile acuity, is actually sensitive to body identity. Experiments 2-4 explored the effect of viewing one’s own body on pain perception. They demonstrated that viewing the body biases pain intensity judgments irrespective of actual stimulus intensity, and, more importantly, reduces the discriminative capacities of the nociceptive pathway encoding noxious stimulus intensity. The latter effect only occurs if the pain-inducing event itself is not visible, suggesting that viewing the body alone and viewing a stimulus event on the body have distinct effects on cutaneous sensations. Experiment 5 replicated an enhancement of visual remapping of touch (VRT) when viewing fearful human faces being touched, and further demonstrated that VRT does not occur for observed touch on non-human faces, even fearful ones. This suggests that the facial expressions of non-human animals may not be simulated within the somatosensory system of the human observer in the same way that the facial expressions of other humans are. Finally, Experiment 6 examined the enfacement illusion, in which synchronous visuo-tactile inputs cause another’s face to be assimilated into the mental self-face representation. The strength of enfacement was not affected by the other’s facial expression, supporting an asymmetric relationship between processing of facial identity and facial expressions. Together, these studies indicate that multisensory representations of the body in the brain link low-level perceptual processes with the perception of emotional cues and body/face identity, and interact in complex ways depending upon contextual factors.

Integration of Multiple Simultaneous Stimuli within and between Somatosensory sub-Modalities

Psychological characterisation of the somatosensory system often focusses on minimal units of perception, such as detection, localisation, and magnitude estimation of single events. Research on how multiple simultaneous stimuli are aggregated to create integrated, synthetic experiences is rarer. This thesis aims to shed a light on the mechanisms underlying the integration of multiple simultaneous stimuli, within and between different sub-modalities of the somatosensory system. First, we investigated the ability of healthy individuals to perceive the total intensity of composite somatosensory patterns. We found that the overall intensity of tactile, cold, or warm patterns was systematically overestimated when the multiple simultaneous stimuli had different intensities. Perception of somatosensory totals was biased towards the most salient element in the pattern. Furthermore, we demonstrated that peak-biased aggregation is a genuine perceptual phenomenon which does not rely on the discrimination of the parts, but is rather based on the salience of each stimulus. Next, we studied a classical thermal illusion to assess participants’ ability to localise thermal stimuli delivered on the fingers either in isolation, or in uniform and non-uniform patterns. We found that despite a surprisingly high accuracy in reporting the location of a single stimulus, when participants were presented with non-uniform patterns, their ability to identify the thermal state of a specific finger was completely abolished. Lastly, we investigated the perceptual and neural correlates of thermo-nociceptive interaction during the presentation of multiple thermal stimuli. We found that inhibition of pain by warmth was independent from both the position and the number of thermal stimuli administered. Our results suggest that nonlinear integration of multiple stimuli, within and between somatosensory sub-modalities, may be an efficient way by which the somatosensory system synthesises the complexity of reality, providing an extended and coherent perception of the world, in spite of its deep bandwidth limitations.

Robotic Sensing and Manipulation of Deformable Linear Objects with Learning-based methods

Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solved. However, Deformable-Objects (DOs) still represent a huge limitation for robots. The main difficulty in DOs manipulation is dealing with the shape and dynamics uncertainties, which prevents the use of model-based approaches (since they are excessively computationally complex) and makes sensory data difficult to interpret. This thesis reports the research activities aimed to address some applications in robotic manipulation and sensing of Deformable-Linear-Objects (DLOs), with particular focus to electric wires. In all the works, a significant effort was made in the study of an effective strategy for analyzing sensory signals with various machine learning algorithms. In the former part of the document, the main focus concerns the wire terminals, i.e. detection, grasping, and insertion. First, a pipeline that integrates vision and tactile sensing is developed, then further improvements are proposed for each module. A novel procedure is proposed to gather and label massive amounts of training images for object detection with minimal human intervention. Together with this strategy, we extend a generic object detector based on Convolutional-Neural-Networks for orientation prediction. The insertion task is also extended by developing a closed-loop control capable to guide the insertion of a longer and curved segment of wire through a hole, where the contact forces are estimated by means of a Recurrent-Neural-Network. In the latter part of the thesis, the interest shifts to the DLO shape. Robotic reshaping of a DLO is addressed by means of a sequence of pick-and-place primitives, while a decision making process driven by visual data learns the optimal grasping locations exploiting Deep Q-learning and finds the best releasing point. The success of the solution leverages on a reliable interpretation of the DLO shape. For this reason, further developments are made on the visual segmentation.

Nanostructured piezoelectric materials for the design and development of self-sensing composite materials and energy harvesting devices

The work activities reported in this PhD thesis regard the functionalization of composite materials and the realization of energy harvesting devices by using nanostructured piezoelectric materials, which can be integrated in the composite without affecting its mechanical properties. The self-sensing composite materials were fabricated by interleaving between the plies of the laminate the piezoelectric elements. The problem of negatively impacting on the mechanical properties of the hosting structure was addressed by shaping the piezoelectric materials in appropriate ways. In the case of polymeric piezoelectric materials, the electrospinning technique allowed to produce highly-porous nanofibrous membranes which can be immerged in the hosting matrix without inducing delamination risk. The flexibility of the polymers was exploited also for the production of flexible tactile sensors. The sensing performances of the specimens were evaluated also in terms of lifetime with fatigue tests. In the case of ceramic piezo-materials, the production and the interleaving of nanometric piezoelectric powder limitedly affected the impact resistance of the laminate, which showed enhanced sensing properties. In addition to this, a model was proposed to predict the piezoelectric response of the self-sensing composite materials as function of the amount of the piezo-phase within the laminate and to adapt its sensing functionalities also for quasi-static loads. Indeed, one final application of the work was to integrate the piezoelectric nanofibers in the sole of a prosthetic foot in order to detect the walking cycle, which has a period in the order of 1 second. In the end, the energy harvesting capabilities of the piezoelectric materials were investigated, with the aim to design wearable devices able to collect energy from the environment and from the body movements. The research activities focused both on the power transfer capability to an external load and the charging of an energy storage unit, like, e.g., a supercapacitor.

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains.