122 research outputs found

    Conversations on Empathy

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    In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy — be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" – others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Somatosensory ECoG-based brain-machine interface with electrical stimulation on medial forebrain bundle

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    Brain-machine interface (BMI) provides an alternative route for controlling an external device with one's intention. For individuals with motor-related disability, the BMI technologies can be used to replace or restore motor functions. Therefore, BMIs for movement restoration generally decode the neural activity from the motor-related brain regions. In this study, however, we designed a BMI system that uses sensory-related neural signals for BMI combined with electrical stimulation for reward. Four-channel electrocorticographic (ECoG) signals were recorded from the whisker-related somatosensory cortex of rats and converted to extract the BMI signals to control the one-dimensional movement of a dot on the screen. At the same time, we used operant conditioning with electrical stimulation on medial forebrain bundle (MFB), which provides a virtual reward to motivate the rat to move the dot towards the desired center region. The BMI task training was performed for 7 days with ECoG recording and MFB stimulation. Animals successfully learned to move the dot location to the desired position using S1BF neural activity. This study successfully demonstrated that it is feasible to utilize the neural signals from the whisker somatosensory cortex for BMI system. In addition, the MFB electrical stimulation is effective for rats to learn the behavioral task for BMI.ope

    1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

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    A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

    Modélisation de la consolidation de la mémoire dépendante de l'état d'activité du cerveau

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    Our brains enable us to perform complex actions and respond quickly to the external world, thanks to transitions between different brain states that reflect the activity of interconnected neuronal populations. An intriguing example is the ever-present switch of brain activity that occurs while transitioning between periods of active and quiet waking. It involves transitions from small-amplitude, high-frequency brain oscillations to large-amplitude, low-frequency oscillations, accompanied by neuronal activity switches from tonic firing to bursting. The switch between these firing modes is regulated by neuromodulators and the inherent properties of neurons. Simultaneously, our brains have the ability to learn and form memories through persistent changes in the strength of the connections between neurons. This process is known as synaptic plasticity, where neurons strengthen or weaken connections based on their respective firing activity. While it is commonly believed that putting in more effort and time leads to better performance when memorizing new information, this thesis explores the hypothesis that taking occasional breaks and allowing the brain to rest during quiet waking periods may actually be beneficial. Using a computational approach, the thesis investigates the relationship between the transitions in brain states from active to quiet waking described by the neuronal switches from tonic firing to bursting, and synaptic plasticity on memory consolidation. To investigate this research question, we constructed neurons and circuits with the ability to switch between tonic firing and bursting using a conductance-based approach. In our first contribution, we focused on identifying the key neuronal property that enables robust switches, even in the presence of neuron and circuit heterogeneity. Through computational experiments and phase plane analysis, we demonstrated the significance of a distinct timescale separation between sodium and T-type calcium channel activation by comparing various models from the existing literature. Synaptic plasticity is studied to understand learning and memory consolidation. The second contribution involves a taxonomy of synaptic plasticity rules, investigating their compatibility with switches in neuronal activity, small neuronal variabilities, and neuromodulators. The third contribution reveals the evolution of synaptic weights during the transition from tonic firing in active waking to bursting in quiet waking. Combining bursting neurons with traditional synaptic plasticity rules using soft-bounds leads to a homeostatic reset, where synaptic weights converge to a fixed point regardless of the weights acquired during tonic firing. Strong weights depress, while weak weights potentiate until reaching a set point. This homeostatic mechanism is robust to neuron and circuit heterogeneity and the choice of synaptic plasticity rules. The reset is further exploited by neuromodulator-induced changes in synaptic rules, potentially supporting the Synaptic-Tagging and Capture hypothesis, where strong weights are tagged and converge to a high reset value during bursting. While burst-induced reset may cause forgetting of previous learning, it also restores synaptic weights and facilitates the formation of new memories. To exploit this homeostatic property, an innovative burst-dependent structural plasticity rule is developed to encode previous learning through long-lasting morphological changes. The proposed mechanism explains late-stage of Long-Term Potentiation, complementing traditional synaptic plasticity rules governing early-stage of Long-Term Potentiation. Switches to bursting enable neurons to consolidate synapses by creating new proteins and promoting synapse growth, while simultaneously restoring efficacy of postsynaptic receptors for new learning. The novel plasticity rule is validated by comparing it with traditional synaptic rules in various memory tasks. The results demonstrate that switches from tonic firing to bursting and the novel structural plasticity enhance learning and memory consolidation. In conclusion, this thesis utilizes computational models of biophysical neurons to provide evidence that the switches from tonic firing to bursting, reflecting the shift from active to quiet waking, play a crucial role in enhancing memory consolidation through structural plasticity. In essence, this thesis offers computational support for the significance of taking breaks and allowing our brains to rest in order to solidify our memories. These findings serve as motivation for collaborative experiments between computational and experimental neuroscience, fostering a deeper understanding of the biological mechanisms underlying brain-state-dependent memory consolidation. Furthermore, these insights have the potential to inspire advancements in machine learning algorithms by incorporating principles of neuronal activity switches

    Event-Driven Technologies for Reactive Motion Planning: Neuromorphic Stereo Vision and Robot Path Planning and Their Application on Parallel Hardware

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    Die Robotik wird immer mehr zu einem Schlüsselfaktor des technischen Aufschwungs. Trotz beeindruckender Fortschritte in den letzten Jahrzehnten, übertreffen Gehirne von Säugetieren in den Bereichen Sehen und Bewegungsplanung noch immer selbst die leistungsfähigsten Maschinen. Industrieroboter sind sehr schnell und präzise, aber ihre Planungsalgorithmen sind in hochdynamischen Umgebungen, wie sie für die Mensch-Roboter-Kollaboration (MRK) erforderlich sind, nicht leistungsfähig genug. Ohne schnelle und adaptive Bewegungsplanung kann sichere MRK nicht garantiert werden. Neuromorphe Technologien, einschließlich visueller Sensoren und Hardware-Chips, arbeiten asynchron und verarbeiten so raum-zeitliche Informationen sehr effizient. Insbesondere ereignisbasierte visuelle Sensoren sind konventionellen, synchronen Kameras bei vielen Anwendungen bereits überlegen. Daher haben ereignisbasierte Methoden ein großes Potenzial, schnellere und energieeffizientere Algorithmen zur Bewegungssteuerung in der MRK zu ermöglichen. In dieser Arbeit wird ein Ansatz zur flexiblen reaktiven Bewegungssteuerung eines Roboterarms vorgestellt. Dabei wird die Exterozeption durch ereignisbasiertes Stereosehen erreicht und die Pfadplanung ist in einer neuronalen Repräsentation des Konfigurationsraums implementiert. Die Multiview-3D-Rekonstruktion wird durch eine qualitative Analyse in Simulation evaluiert und auf ein Stereo-System ereignisbasierter Kameras übertragen. Zur Evaluierung der reaktiven kollisionsfreien Online-Planung wird ein Demonstrator mit einem industriellen Roboter genutzt. Dieser wird auch für eine vergleichende Studie zu sample-basierten Planern verwendet. Ergänzt wird dies durch einen Benchmark von parallelen Hardwarelösungen wozu als Testszenario Bahnplanung in der Robotik gewählt wurde. Die Ergebnisse zeigen, dass die vorgeschlagenen neuronalen Lösungen einen effektiven Weg zur Realisierung einer Robotersteuerung für dynamische Szenarien darstellen. Diese Arbeit schafft eine Grundlage für neuronale Lösungen bei adaptiven Fertigungsprozesse, auch in Zusammenarbeit mit dem Menschen, ohne Einbußen bei Geschwindigkeit und Sicherheit. Damit ebnet sie den Weg für die Integration von dem Gehirn nachempfundener Hardware und Algorithmen in die Industrierobotik und MRK

    The evolution of language: Proceedings of the Joint Conference on Language Evolution (JCoLE)

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    Plasticity and neuromodulation of the extended recurrent visual network

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    The extended visual network, which includes occipital, temporal and parietal posterior cortices, is a system characterized by an intrinsic connectivity consisting of bidirectional projections. This network is composed of feedforward and feedback projections, some hierarchically arranged and others bypassing intermediate areas, allowing direct communication across early and late stages of processing. Notably, the early visual cortex (EVC) receives considerably more feedback and lateral inputs than feedforward thalamic afferents, placing it at the receiving end of a complex cortical processing cascade, rather than just being the entrance stage of cortical processing of retinal input. The critical role of back-projections to visual cortices has been related to perceptual awareness, amplification of neural activity in lower order areas and improvement of stimulus processing. Recently, significant results have shown behavioural evidence suggesting the importance of reentrant projections in the human visual system, and demonstrated the feasibility of inducing their reversible modulation through a transcranial magnetic stimulation (TMS) paradigm named cortico-cortical paired associative stimulation (ccPAS). Here, a novel research line for the study of recurrent connectivity and its plasticity in the perceptual domain was put forward. In the present thesis, we used ccPAS with the aim of empowering the synaptic efficacy, and thus the connectivity, between the nodes of the visuocognitive system to evaluate the impact on behaviour. We focused on driving plasticity in specific networks entailing the elaboration of relevant social features of human faces (Chapters I & II), alongside the investigation of targeted pathways of sensory decisions (Chapter III). This allowed us to characterize perceptual outcomes which endorse the prominent role of the EVC in visual awareness, fulfilled by the activity of back-projections originating from distributed functional nodes

    Sensing the world through predictions and errors

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