A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

The interindividual variability of multimodal brain connectivity maintains spatial heterogeneity and relates to tissue microstructure

Humans differ from each other in a wide range of biometrics, but to what extent brain connectivity varies between individuals remains largely unknown. By combining diffusion-weighted imaging (DWI) and magnetoencephalography (MEG), this study characterizes the inter-subject variability (ISV) of multimodal brain connectivity. Structural connectivity is characterized by higher ISV in association cortices including the core multiple-demand network and lower ISV in the sensorimotor cortex. MEG ISV exhibits frequency-dependent signatures, and the extent of MEG ISV is consistent with that of structural connectivity ISV in selective macroscopic cortical clusters. Across the cortex, the ISVs of structural connectivity and beta-band MEG functional connectivity are negatively associated with cortical myelin content indexed by the quantitative T1 relaxation rate measured by high-resolution 7 T MRI. Furthermore, MEG ISV from alpha to gamma bands relates to the hindrance and restriction of the white-matter tissue estimated by DWI microstructural models. Our findings depict the inter-relationship between the ISV of brain connectivity from multiple modalities, and highlight the role of tissue microstructure underpinning the ISV

The Musical Structure of Time in the Brain: Repetition, Rhythm, and Harmony in fMRI During Rest and Passive Movie Viewing

© Copyright © 2020 Lloyd. Space generally overshadows time in the construction of theories in cognitive neuroscience. In this paper, we pivot from the spatial axes to the temporal, analyzing fMRI image series to reveal structures in time rather than space. To determine affinities among global brain patterns at different times, core concepts in network analysis (derived from graph theory) were applied temporally, as relations among brain images at every time point during an fMRI scanning epoch. To explore the temporal structures observed through this adaptation of network analysis, data from 180 subjects in the Human Connectome Project were examined, during two experimental conditions: passive movie viewing and rest. The temporal brain, like the spatial brain, exhibits a modular structure, where “modules” are intermittent (distributed in time). These temporal entities are here referred to as themes. Short sequences of themes – motifs – were studied in sequences from 4 to 11 s in length. Many motifs repeated at constant intervals, and are therefore rhythmic; rhythms, converted to frequencies, were often harmonic. We speculate that the structure and interaction of these global oscillations underwrites the capacity to experience and navigate a world which is both recognizably stable and noticeably changing at every moment – a temporal world. In its temporal structure, this brain-constituted world resembles music

Modelling and quantifying brain connectivity and dynamics with applications in aging and ADHD

Human brain is a complex organ and made up of integrative networks encompassing a large number of regions. These regions communicate with each other to share information involved in complex cognitive processes. Functional connectivity (FC) represents the level of synchronization between different brain regions/networks. Studying functional interactions of the brain creates a platform for understanding functional architecture of the brain as an integrative network and has implications for understanding human cognition. Furthermore, there is evidence that FC patterns are sensitive to different diseases. In addition, age is a significant determinant of intra-/inter-individual variability in the FC patterns. Therefore, key aims for the studies included in this thesis were to apply and develop novel resting-state FC methodologies, with applications in healthy aging and ADHD. Indeed, measures of the brain’s FC may serve as a useful tool to diagnose and predict the course of disease, and useful in developing individualized therapies. Age- or disease-related alterations in the FC could reflect a multitude of factors, including changes in structural connectivity. However, we still have limited knowledge of the emergence of brain dynamics from the underlying anatomy. The interplay between the brain’s structure and dynamics underlies all brain functions. Therefore, in the last study we focused on the systematic modeling of the brain network dynamics. Large-scale computational models are uniquely suited to address difficult questions related to the role of brain’s structural network in shaping functional interactions. In addition, computational modeling of the brain enables us to test different hypotheses without any experimental complication while it provides us with a platform for improving our understanding of different brain mechanisms. A new macroscopic computational model of the brain oscillations for resting-state fMRI was introduced in this thesis, which outperforms previous model in the same class. Then, the effects of malfunctions in different brain regions were simulated and subsequently predicted perturbation patterns were recruited for local vulnerability mapping as well as quantification of hazard rates induced after perturbing any brain regio

Awakening: Predicting external stimulation to force transitions between different brain states

A fundamental problem in systems neuroscience is how to force a transition from one brain state to another by external driven stimulation in, for example, wakefulness, sleep, coma, or neuropsychiatric diseases. This requires a quantitative and robust definition of a brain state, which has so far proven elusive. Here, we provide such a definition, which, together with whole-brain modeling, permits the systematic study in silico of how simulated brain stimulation can force transitions between different brain states in humans. Specifically, we use a unique neuroimaging dataset of human sleep to systematically investigate where to stimulate the brain to force an awakening of the human sleeping brain and vice versa. We show where this is possible using a definition of a brain state as an ensemble of "metastable substates," each with a probabilistic stability and occurrence frequency fitted by a generative whole-brain model, fine-tuned on the basis of the effective connectivity. Given the biophysical limitations of direct electrical stimulation (DES) of microcircuits, this opens exciting possibilities for discovering stimulation targets and selecting connectivity patterns that can ensure propagation of DES-induced neural excitation, potentially making it possible to create awakenings from complex cases of brain injury.Spanish Research Project PSI2016-75688-P (Agencia Estatal de Investigación/Fondo Europeo de Desarrollo Regional, European Union); by the European Union’s Horizon 2020 Re-search and Innovation Programme under Grant Agreements 720270 (Hu-man Brain Project [HBP] SGA1) and 785907 (HBP SGA2); and by the CatalanAgency for Management of University and Research Grants Programme 2017 SGR 1545. J. Cabral is supported by Portuguese Foundation for Sci-ence and Technology CEECIND/03325/2017, Portugal. M.L.K. is supportedby the European Research Council Consolidator Grant: CAREGIVING (615539) and Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117)

The What and Why of Binding: The Modeler's Perspective

In attempts to formulate a computational understanding of brain function, one of the fundamental concerns is the data structure by which the brain represents information. For many decades, a conceptual framework has dominated the thinking of both brain modelers and neurobiologists. That framework is referred to here as "classical neural networks." It is well supported by experimental data, although it may be incomplete. A characterization of this framework will be offered in the next section. Difficulties in modeling important functional aspects of the brain on the basis of classical neural networks alone have led to the recognition that another, general mechanism must be invoked to explain brain function. That mechanism I call "binding." Binding by neural signal synchrony had been mentioned several times in the liter ature (Lege´ndy, 1970; Milner, 1974) before it was fully formulated as a general phenomenon (von der Malsburg, 1981). Although experimental evidence for neural syn chrony was soon found, the idea was largely ignored for many years. Only recently has it become a topic of animated discussion. In what follows, I will summarize the nature and the roots of the idea of binding, especially of temporal binding, and will discuss some of the objec tions raised against it

Situational influences on rhythmicity in speech, music, and their interaction.

Brain processes underlying the production and perception of rhythm indicate considerable flexibility in how physical signals are interpreted. This paper explores how that flexibility might play out in rhythmicity in speech and music. There is much in common across the two domains, but there are also significant differences. Interpretations are explored that reconcile some of the differences, particularly with respect to how functional properties modify the rhythmicity of speech, within limits imposed by its structural constraints. Functional and structural differences mean that music is typically more rhythmic than speech, and that speech will be more rhythmic when the emotions are more strongly engaged, or intended to be engaged. The influence of rhythmicity on attention is acknowledged, and it is suggested that local increases in rhythmicity occur at times when attention is required to coordinate joint action, whether in talking or music-making. Evidence is presented which suggests that while these short phases of heightened rhythmical behaviour are crucial to the success of transitions in communicative interaction, their modality is immaterial: they all function to enhance precise temporal prediction and hence tightly coordinated joint action

Neural oscillatory signatures of auditory and audiovisual illusions

Questions of the relationship between human perception and brain activity can be approached from different perspectives: in the first, the brain is mainly regarded as a recipient and processor of sensory data. The corresponding research objective is to establish mappings of neural activity patterns and external stimuli. Alternatively, the brain can be regarded as a self-organized dynamical system, whose constantly changing state affects how incoming sensory signals are processed and perceived. The research reported in this thesis can chiefly be located in the second framework, and investigates the relationship between oscillatory brain activity and the perception of ambiguous stimuli. Oscillations are here considered as a mechanism for the formation of transient neural assemblies, which allows efficient information transfer. While the relevance of activity in distinct frequency bands for auditory and audiovisual perception is well established, different functional architectures of sensory integration can be derived from the literature. This dissertation therefore aims to further clarify the role of oscillatory activity in the integration of sensory signals towards unified perceptual objects, using illusion paradigms as tools of study. In study 1, we investigate the role of low frequency power modulations and phase alignment in auditory object formation. We provide evidence that auditory restoration is associated with a power reduction, while the registration of an additional object is reflected by an increase in phase locking. In study 2, we analyze oscillatory power as a predictor of auditory influence on visual perception in the sound-induced flash illusion. We find that increased beta-/ gamma-band power over occipitotemporal electrodes shortly before stimulus onset predicts the illusion, suggesting a facilitation of processing in polymodal circuits. In study 3, we address the question of whether visual influence on auditory perception in the ventriloquist illusion is reflected in primary sensory or higher-order areas. We establish an association between reduced theta-band power in mediofrontal areas and the occurrence of illusion, which indicates a top-down influence on sensory decision-making. These findings broaden our understanding of the functional relevance of neural oscillations by showing that different processing modes, which are reflected in specific spatiotemporal activity patterns, operate in different instances of sensory integration.Fragen nach dem Zusammenhang zwischen menschlicher Wahrnehmung und Hirnaktivität können aus verschiedenen Perspektiven adressiert werden: in der einen wird das Gehirn hauptsächlich als Empfänger und Verarbeiter von sensorischen Daten angesehen. Das entsprechende Forschungsziel wäre eine Zuordnung von neuronalen Aktivitätsmustern zu externen Reizen. Dieser Sichtweise gegenüber steht ein Ansatz, der das Gehirn als selbstorganisiertes dynamisches System begreift, dessen sich ständig verändernder Zustand die Verarbeitung und Wahrnehmung von sensorischen Signalen beeinflusst. Die Arbeiten, die in dieser Dissertation zusammengefasst sind, können vor allem in der zweitgenannten Forschungsrichtung verortet werden, und untersuchen den Zusammenhang zwischen oszillatorischer Hirnaktivität und der Wahrnehmung von mehrdeutigen Stimuli. Oszillationen werden hier als ein Mechanismus für die Formation von transienten neuronalen Zusammenschlüssen angesehen, der effizienten Informationstransfer ermöglicht. Obwohl die Relevanz von Aktivität in verschiedenen Frequenzbändern für auditorische und audiovisuelle Wahrnehmung gut belegt ist, können verschiedene funktionelle Architekturen der sensorischen Integration aus der Literatur abgeleitet werden. Das Ziel dieser Dissertation ist deshalb eine Präzisierung der Rolle oszillatorischer Aktivität bei der Integration von sensorischen Signalen zu einheitlichen Wahrnehmungsobjekten mittels der Nutzung von Illusionsparadigmen. In der ersten Studie untersuchen wir die Rolle von Leistung und Phasenanpassung in niedrigen Frequenzbändern bei der Formation von auditorischen Objekten. Wir zeigen, dass die Wiederherstellung von Tönen mit einer Reduktion der Leistung zusammenhängt, während die Registrierung eines zusätzlichen Objekts durch einen erhöhten Phasenangleich widergespiegelt wird. In der zweiten Studie analysieren wir oszillatorische Leistung als Prädiktor von auditorischem Einfluss auf visuelle Wahrnehmung in der sound-induced flash illusion. Wir stellen fest, dass erhöhte Beta-/Gamma-Band Leistung über occipitotemporalen Elektroden kurz vor der Reizdarbietung das Auftreten der Illusion vorhersagt, was auf eine Begünstigung der Verarbeitung in polymodalen Arealen hinweist. In der dritten Studie widmen wir uns der Frage, ob ein visueller Einfluss auf auditorische Wahrnehmung in der ventriloquist illusion sich in primären sensorischen oder übergeordneten Arealen widerspiegelt. Wir weisen einen Zusammenhang von reduzierter Theta-Band Leistung in mediofrontalen Arealen und dem Auftreten der Illusion nach, was einen top-down Einfluss auf sensorische Entscheidungsprozesse anzeigt. Diese Befunde erweitern unser Verständnis der funktionellen Bedeutung neuronaler Oszillationen, indem sie aufzeigen, dass verschiedene Verarbeitungsmodi, die sich in spezifischen räumlich-zeitlichen Aktivitätsmustern spiegeln, in verschiedenen Phänomenen von sensorischer Integration wirksam sind