105 research outputs found

    Neuromorphic hardware for somatosensory neuroprostheses

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    In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies

    Functional connectivity and dendritic integration of feedback in visual cortex

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    A fundamental question in neuroscience is how different brain regions communicate with each other. Sensory processing engages distributed circuits across many brain areas and involves information flow in the feedforward and feedback direction. While feedforward processing is conceptually well understood, feedback processing has remained mysterious. Cortico-cortical feedback axons are enriched in layer 1, where they form synapses with the apical dendrites of pyramidal neurons. The organization and dendritic integration of information conveyed by these axons, however, are unknown. This thesis describes my efforts to link the circuit-level and dendritic-level organization of cortico-cortical feedback in the mouse visual system. First, using cellular resolution all-optical interrogation across cortical areas, I characterized the functional connectivity between the lateromedial higher visual area (LM) and primary visual cortex (V1). Feedback influence had both facilitating and suppressive effects on visually-evoked activity in V1 neurons, and was spatially organized: retinotopically aligned feedback was relatively more suppressive, while retinotopically offset feedback was relatively more facilitating. Second, to examine how feedback inputs are integrated in apical dendrites, I optogenetically stimulated presynaptic neurons in LM while using 2-photon calcium imaging to map feedback-recipient spines in the apical tufts of layer 5 neurons in V1. Activation of a single feedback-providing input was sufficient to boost calcium signals and recruit branch-specific local events in the recipient dendrite, suggesting that feedback can engage dendritic nonlinearities directly. Finally, I measured the recruitment of apical dendrites during visual stimulus processing. Surround visual stimuli, which should recruit relatively more facilitating feedback, drove local calcium events in apical tuft branches. Moreover, global dendritic event size was not purely determined by somatic activity but modulated by visual stimuli and behavioural state, in a manner consistent with the spatial organization of feedback. In summary, these results point toward a possible involvement of active dendritic processing in the integration of feedback signals. Active dendrites could thus provide a biophysical substrate for the integration of essential top-down information streams, including contextual or predictive processing

    Insect neuroethology of reinforcement learning

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    Historically, reinforcement learning is a branch of machine learning founded on observations of how animals learn. This involved collaboration between the fields of biology and artificial intelligence that was beneficial to both fields, creating smarter artificial agents and improving the understanding of how biological systems function. The evolution of reinforcement learning during the past few years was rapid but substantially diverged from providing insights into how biological systems work, opening a gap between reinforcement learning and biology. In an attempt to close this gap, this thesis studied the insect neuroethology of reinforcement learning, that is, the neural circuits that underlie reinforcement-learning-related behaviours in insects. The goal was to extract a biologically plausible plasticity function from insect-neuronal data, use this to explain biological findings and compare it to more standard reinforcement learning models. Consequently, a novel dopaminergic plasticity rule was developed to approximate the function of dopamine as the plasticity mechanism between neurons in the insect brain. This allowed a range of observed learning phenomena to happen in parallel, like memory depression, potentiation, recovery, and saturation. In addition, by using anatomical data of connections between neurons in the mushroom body neuropils of the insect brain, the neural incentive circuit of dopaminergic and output neurons was also explored. This, together with the dopaminergic plasticity rule, allowed for dynamic collaboration amongst parallel memory functions, such as acquisition, transfer, and forgetting. When tested on olfactory conditioning paradigms, the model reproduced the observed changes in the activity of the identified neurons in fruit flies. It also replicated the observed behaviour of the animals and it allowed for flexible behavioural control. Inspired by the visual navigation system of desert ants, the model was further challenged in the visual place recognition task. Although a relatively simple encoding of the olfactory information was sufficient to explain odour learning, a more sophisticated encoding of the visual input was required to increase the separability among the visual inputs and enable visual place recognition. Signal whitening and sparse combinatorial encoding were sufficient to boost the performance of the system in this task. The incentive circuit enabled the encoding of increasing familiarity along a known route, which dropped proportionally to the distance of the animal from that route. Finally, the proposed model was challenged in delayed reinforcement tasks, suggesting that it might take the role of an adaptive critic in the context of reinforcement learning

    Neural foundations of cooperative social interactions

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    The embodied-embedded-enactive-extended (4E) approach to study cognition suggests that interaction with the world is a crucial component of our cognitive processes. Most of our time, we interact with other people. Therefore, studying cognition without interaction is incomplete. Until recently, social neuroscience has only focused on studying isolated human and animal brains, leaving interaction unexplored. To fill this gap, we studied interacting participants, focusing on both intra- and inter-brain (hyperscanning) neural activity. In the first study, we invited dyads to perform a visual task in both a cooperative and a competitive context while we measured EEG. We found that mid-frontal activity around 200-300 ms after receiving monetary rewards was sensitive to social context and differed between cooperative and competitive situations. In the second study, we asked participants to coordinate their movements with each other and with a robotic partner. We found significantly stronger EEG amplitudes at frontocentral electrodes when people interacted with a robotic partner. Lastly, we performed a comprehensive literature review and the first meta-analysis in the emerging field of hyperscanning that validated it as a method to study social interaction. Taken together, our results showed that adding a second participant (human or AI/robotic) fostered our understanding of human cognition. We learned that the activity at frontocentral electrodes is sensitive to social context and type of partner (human or robotic). In both studies, the participants’ interaction was required to show these novel neural processes involved in action monitoring. Similarly, studying inter-brain neural activity allows for the exploration of new aspects of cognition. Many cognitive functions involved in successful social interactions are accompanied by neural synchrony between brains, suggesting the extended form of our cognition

    Discovering causal relations and equations from data

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    Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws, and principles that are invariant, robust, and causal has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventions on the system under study. With the advent of big data and data-driven methods, the fields of causal and equation discovery have developed and accelerated progress in computer science, physics, statistics, philosophy, and many applied fields. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for data-driven causal and equation discovery, point out connections, and showcase comprehensive case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is revolutionised with the efficient exploitation of observational data and simulations, modern machine learning algorithms and the combination with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems

    Generative Model based Training of Deep Neural Networks for Event Detection in Microscopy Data

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    Several imaging techniques employed in the life sciences heavily rely on machine learning methods to make sense of the data that they produce. These include calcium imaging and multi-electrode recordings of neural activity, single molecule localization microscopy, spatially-resolved transcriptomics and particle tracking, among others. All of them only produce indirect readouts of the spatiotemporal events they aim to record. The objective when analysing data from these methods is the identification of patterns that indicate the location of the sought-after events, e.g. spikes in neural recordings or fluorescent particles in microscopy data. Existing approaches for this task invert a forward model, i.e. a mathematical description of the process that generates the observed patterns for a given set of underlying events, using established methods like MCMC or variational inference. Perhaps surprisingly, for a long time deep learning saw little use in this domain, even though it became the dominant approach in the field of pattern recognition over the previous decade. The principal reason is that in the absence of labeled data needed for supervised optimization it remains unclear how neural networks can be trained to solve these tasks. To unlock the potential of deep learning, this thesis proposes different methods for training neural networks using forward models and without relying on labeled data. The thesis rests on two publications: In the first publication we introduce an algorithm for spike extraction from calcium imaging time traces. Building on the variational autoencoder framework, we simultaneously train a neural network that performs spike inference and optimize the parameters of the forward model. This approach combines several advantages that were previously incongruous: it is fast at test-time, can be applied to different non-linear forward models and produces samples from the posterior distribution over spike trains. The second publication deals with the localization of fluorescent particles in single molecule localization microscopy. We show that an accurate forward model can be used to generate simulations that act as a surrogate for labeled training data. Careful design of the output representation and loss function result in a method with outstanding precision across experimental designs and imaging conditions. Overall this thesis highlights how neural networks can be applied for precise, fast and flexible model inversion on this class of problems and how this opens up new avenues to achieve performance beyond what was previously possible

    Layer 3 pyramidal neurons of rhesus monkeys in aging and after ischemic injury

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    Layer 3 (L3) pyramidal neurons are involved in intrinsic and extrinsic corticocortical communications that are integral to area specific cortical functions. The functional and morphological properties of these neurons are altered in the lateral prefrontal cortex (LPFC) of aged rhesus monkeys, changes which parallel the decline of working memory (WM) function. What is not yet understood is the time course of these neuronal alternations during the aging process, or the impact of neuronal changes on the function of local networks that underlie WM. By comparing the properties of L3 pyramidal neurons from the LPFC of behaviorally characterized rhesus monkeys over the adult lifespan using whole cell patch clamp recordings and neuronal reconstructions, the present dissertation demonstrates that WM impairment, neuronal hyperexcitabilty and spine loss begin in middle age. We use bump attractor models to predict how empirically observed changes affect performance on the Delayed Response Task and Delayed Recognition Span Task (spatial). The performance of both models is affected much more by neuronal hyperexcitability than by synapse loss. In a separate study, we examine pathological changes of L3 pyramidal neurons in the perilesional ventral premotor cortex following acute ischemic injury to the primary motor cortex. Neurons from lesioned monkeys exhibit hyperexcitability and changes the excitatory:inhibitory synaptic balance in favor of inhibition. As oxidative stress and inflammation are known to exacerbate both age-related and injury-induced neuronal pathology, we characterize neuronal properties in both conditions after administering therapeutic interventions which target inflammatory pathways and which have previously been shown to ameliorate behavioral deficits. Chronic dietary curcumin treatment dampens neuronal hyperexcitability in middle-aged subjects, but the neuronal changes are not correlated with WM improvements. Treatment with mesenchymal-derived extracellular vesicles lowers firing rates and restores excitatory:inhibitory synaptic balance, and importantly, these changes correlate significantly with motor function

    Assessing brain connectivity through electroencephalographic signal processing and modeling analysis

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    Brain functioning relies on the interaction of several neural populations connected through complex connectivity networks, enabling the transmission and integration of information. Recent advances in neuroimaging techniques, such as electroencephalography (EEG), have deepened our understanding of the reciprocal roles played by brain regions during cognitive processes. The underlying idea of this PhD research is that EEG-related functional connectivity (FC) changes in the brain may incorporate important neuromarkers of behavior and cognition, as well as brain disorders, even at subclinical levels. However, a complete understanding of the reliability of the wide range of existing connectivity estimation techniques is still lacking. The first part of this work addresses this limitation by employing Neural Mass Models (NMMs), which simulate EEG activity and offer a unique tool to study interconnected networks of brain regions in controlled conditions. NMMs were employed to test FC estimators like Transfer Entropy and Granger Causality in linear and nonlinear conditions. Results revealed that connectivity estimates reflect information transmission between brain regions, a quantity that can be significantly different from the connectivity strength, and that Granger causality outperforms the other estimators. A second objective of this thesis was to assess brain connectivity and network changes on EEG data reconstructed at the cortical level. Functional brain connectivity has been estimated through Granger Causality, in both temporal and spectral domains, with the following goals: a) detect task-dependent functional connectivity network changes, focusing on internal-external attention competition and fear conditioning and reversal; b) identify resting-state network alterations in a subclinical population with high autistic traits. Connectivity-based neuromarkers, compared to the canonical EEG analysis, can provide deeper insights into brain mechanisms and may drive future diagnostic methods and therapeutic interventions. However, further methodological studies are required to fully understand the accuracy and information captured by FC estimates, especially concerning nonlinear phenomena

    Egocentric Computer Vision and Machine Learning for Simulated Prosthetic Vision

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    Las prótesis visuales actuales son capaces de proporcionar percepción visual a personas con cierta ceguera. Sin pasar por la parte dañada del camino visual, la estimulación eléctrica en la retina o en el sistema nervioso provoca percepciones puntuales conocidas como “fosfenos”. Debido a limitaciones fisiológicas y tecnológicas, la información que reciben los pacientes tiene una resolución muy baja y un campo de visión y rango dinámico reducido afectando seriamente la capacidad de la persona para reconocer y navegar en entornos desconocidos. En este contexto, la inclusión de nuevas técnicas de visión por computador es un tema clave activo y abierto. En esta tesis nos centramos especialmente en el problema de desarrollar técnicas para potenciar la información visual que recibe el paciente implantado y proponemos diferentes sistemas de visión protésica simulada para la experimentación.Primero, hemos combinado la salida de dos redes neuronales convolucionales para detectar bordes informativos estructurales y siluetas de objetos. Demostramos cómo se pueden reconocer rápidamente diferentes escenas y objetos incluso en las condiciones restringidas de la visión protésica. Nuestro método es muy adecuado para la comprensión de escenas de interiores comparado con los métodos tradicionales de procesamiento de imágenes utilizados en prótesis visuales.Segundo, presentamos un nuevo sistema de realidad virtual para entornos de visión protésica simulada más realistas usando escenas panorámicas, lo que nos permite estudiar sistemáticamente el rendimiento de la búsqueda y reconocimiento de objetos. Las escenas panorámicas permiten que los sujetos se sientan inmersos en la escena al percibir la escena completa (360 grados).En la tercera contribución demostramos cómo un sistema de navegación de realidad aumentada para visión protésica ayuda al rendimiento de la navegación al reducir el tiempo y la distancia para alcanzar los objetivos, incluso reduciendo significativamente el número de colisiones de obstáculos. Mediante el uso de un algoritmo de planificación de ruta, el sistema encamina al sujeto a través de una ruta más corta y sin obstáculos. Este trabajo está actualmente bajo revisión.En la cuarta contribución, evaluamos la agudeza visual midiendo la influencia del campo de visión con respecto a la resolución espacial en prótesis visuales a través de una pantalla montada en la cabeza. Para ello, usamos la visión protésica simulada en un entorno de realidad virtual para simular la experiencia de la vida real al usar una prótesis de retina. Este trabajo está actualmente bajo revisión.Finalmente, proponemos un modelo de Spiking Neural Network (SNN) que se basa en mecanismos biológicamente plausibles y utiliza un esquema de aprendizaje no supervisado para obtener mejores algoritmos computacionales y mejorar el rendimiento de las prótesis visuales actuales. El modelo SNN propuesto puede hacer uso de la señal de muestreo descendente de la unidad de procesamiento de información de las prótesis retinianas sin pasar por el análisis de imágenes retinianas, proporcionando información útil a los ciegos. Esté trabajo está actualmente en preparación.<br /

    Two faces of perceptual awareness during the attentional blink:Gradual and discrete

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    In a series of experiments, the nature of perceptual awareness during the attentional blink was investigated. Previous work has considered the attentional blink as a discrete, all-or-none phenomenon, indicative of general access to conscious awareness. Using continuous report measures in combination with mixture modeling, the outcomes showed that perceptual awareness during the attentional blink can be a gradual phenomenon. Awareness was not exclusively discrete, but also exhibited a gradual characteristic whenever the spatial extent of attention induced by the first target spanned more than a single location. Under these circumstances, mental representations of blinked targets were impoverished, but did approach the actual identities of the targets. Conversely, when the focus of attention covered only a single location, there was no evidence for any partial knowledge of blinked targets. These two different faces of awareness during the attentional blink challenge current theories of both awareness and temporal attention, which cannot explain the existence of gradual awareness of targets during the attentional blink. To account for the current outcomes, an adaptive gating model is proposed that casts awareness on a continuum between gradual and discrete, rather than as being of either single kind
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