223 research outputs found

    Feedback information transfer in the human brain reflects bistable perception in the absence of report

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    In the search for the neural basis of conscious experience, perception and the cognitive processes associated with reporting perception are typically confounded as neural activity is recorded while participants explicitly report what they experience. Here, we present a novel way to disentangle perception from report using eye movement analysis techniques based on convolutional neural networks and neurodynamical analyses based on information theory. We use a bistable visual stimulus that instantiates two well-known properties of conscious perception: integration and differentiation. At any given moment, observers either perceive the stimulus as one integrated unitary object or as two differentiated objects that are clearly distinct from each other. Using electroencephalography, we show that measures of integration and differentiation based on information theory closely follow participants’ perceptual experience of those contents when switches were reported. We observed increased information integration between anterior to posterior electrodes (front to back) prior to a switch to the integrated percept, and higher information differentiation of anterior signals leading up to reporting the differentiated percept. Crucially, information integration was closely linked to perception and even observed in a no-report condition when perceptual transitions were inferred from eye movements alone. In contrast, the link between neural differentiation and perception was observed solely in the active report condition. Our results, therefore, suggest that perception and the processes associated with report require distinct amounts of anterior–posterior network communication and anterior information differentiation. While front-to-back directed information is associated with changes in the content of perception when viewing bistable visual stimuli, regardless of report, frontal information differentiation was absent in the no-report condition and therefore is not directly linked to perception per se

    Parameter identification in networks of dynamical systems

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    Mathematical models of real systems allow to simulate their behavior in conditions that are not easily or affordably reproducible in real life. Defining accurate models, however, is far from trivial and there is no one-size-fits-all solution. This thesis focuses on parameter identification in models of networks of dynamical systems, considering three case studies that fall under this umbrella: two of them are related to neural networks and one to power grids. The first case study is concerned with central pattern generators, i.e. small neural networks involved in animal locomotion. In this case, a design strategy for optimal tuning of biologically-plausible model parameters is developed, resulting in network models able to reproduce key characteristics of animal locomotion. The second case study is in the context of brain networks. In this case, a method to derive the weights of the connections between brain areas is proposed, utilizing both imaging data and nonlinear dynamics principles. The third and last case study deals with a method for the estimation of the inertia constant, a key parameter in determining the frequency stability in power grids. In this case, the method is customized to different challenging scenarios involving renewable energy sources, resulting in accurate estimations of this parameter

    Feedback information transfer in the human brain reflects bistable perception in the absence of report

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    In the search for the neural basis of conscious experience, perception and the cognitive processes associated with reporting perception are typically confounded as neural activity is recorded while participants explicitly report what they experience. Here, we present a novel way to disentangle perception from report using eye movement analysis techniques based on convolutional neural networks and neurodynamical analyses based on information theory. We use a bistable visual stimulus that instantiates two well-known properties of conscious perception: integration and differentiation. At any given moment, observers either perceive the stimulus as one integrated unitary object or as two differentiated objects that are clearly distinct from each other. Using electroencephalography, we show that measures of integration and differentiation based on information theory closely follow participants' perceptual experience of those contents when switches were reported. We observed increased information integration between anterior to posterior electrodes (front to back) prior to a switch to the integrated percept, and higher information differentiation of anterior signals leading up to reporting the differentiated percept. Crucially, information integration was closely linked to perception and even observed in a no-report condition when perceptual transitions were inferred from eye movements alone. In contrast, the link between neural differentiation and perception was observed solely in the active report condition. Our results, therefore, suggest that perception and the processes associated with report require distinct amounts of anterior-posterior network communication and anterior information differentiation. While front-to-back directed information is associated with changes in the content of perception when viewing bistable visual stimuli, regardless of report, frontal information differentiation was absent in the no-report condition and therefore is not directly linked to perception per se.</p

    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

    Making it count : novel behavioural tasks to quantify symptoms of dementia with Lewy bodies

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    Dementia with Lewy bodies (DLB) is a neurodegenerative disease and a common cause of dementia in the elderly. The primary pathology of DLB is the mis-folding of the α-synuclein protein, classifying DLB as a synucleinopathy. However, concomitant pathologies are commonly found in post-mortem examination of DLB patients that may complicate diagnosis. Furthermore, DLB is a relatively new disease, first discovered in 1976, while the first official diagnostic criteria released in 1996. Consequently, the diagnostic criteria for DLB have evolved as more is learnt about the clinical and neuropathological profile. Synucleinopathies are also known to be heterogeneous, with no single symptom or biomarker present in all DLB cases. Instead, combinations of common symptoms lead to a diagnosis of probable DLB. Two of the most prominent and debilitating symptoms of DLB are visual hallucinations and cognitive fluctuations. Visual hallucinations (VH) in DLB patients are typically vivid, well-formed percepts and are a major cause of patient and caregiver stress as well as a risk factor for the patient being placed into professional care. Cognitive fluctuations (CF) involve a cycling change in attention and alertness and may occur on a daily or monthly basis, while drops in awareness may last seconds or hours. Currently, the only tools to measure cognitive fluctuations or visual hallucinations are scales or questionnaires that rely on responses from the patient or informant. Furthermore, severity of the symptom is then ranked on an arbitrary ranking system. While this method has advantages in a clinical setting, the subjective nature of the scales combined with the ranking of scores results in a loss of sensitivity. In a research setting, especially imaging or clinical trials, objective measures that are sensitive to changes in symptom severity are highly valued. This allows researchers to assess the relationship between behavioural and fMRI data and clinicians to observe subtle changes in severity. Furthermore, the measures need to be easy to conduct as patients are often severely impaired. The aim of this thesis is to test cognitive function using three paradigms that are novel to DLB patients: Sustained Attention Response Task (SART), the Mental Rotation (MR) task and the Bistable Percept Paradigm (BPP). Overall, this thesis provided the groundwork needed before these three tasks can be utilised in a clinical or research setting. Moreover, as each task was accessible to DLB patients and provided a measure associated with VH or CF, they may prove useful for future neuroimaging/neuropsychological studies

    Adaptation In the Sensory Cortex Drives Bistable Switching During Auditory Stream Segregation

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    Current theories of perception emphasize the role of neural adaptation, inhibitory competition, and noise as key components that lead to switches in perception. Supporting evidence comes from neurophysiological findings of specific neural signatures in modality-specific and supramodal brain areas that appear to be critical to switches in perception. We used functional magnetic resonance imaging to study brain activity around the time of switches in perception while participants listened to a bistable auditory stream segregation stimulus, which can be heard as one integrated stream of tones or two segregated streams of tones. The auditory thalamus showed more activity around the time of a switch from segregated to integrated compared to time periods of stable perception of integrated; in contrast, the rostral anterior cingulate cortex and the inferior parietal lobule showed more activity around the time of a switch from integrated to segregated compared to time periods of stable perception of segregated streams, consistent with prior findings of asymmetries in brain activity depending on the switch direction. In sound-responsive areas in the auditory cortex, neural activity increased in strength preceding switches in perception and declined in strength over time following switches in perception. Such dynamics in the auditory cortex are consistent with the role of adaptation proposed by computational models of visual and auditory bistable switching, whereby the strength of neural activity decreases following a switch in perception, which eventually destabilizes the current percept enough to lead to a switch to an alternative percept

    Neural correlates of visual awareness

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    openL'elaborato si propone di esporre le attuali evidenze riguardanti il modo in cui i contenuti soggettivi di consapevolezza visiva sono codificati a livello neurale. Sebbene i meccanismi neurali della percezione visiva siano ampiamente conosciuti, rimane ancora da chiarire come l'informazione visiva entri a far parte dei contenuti della coscienza. Per identificare i correlati neurali della coscienza (CNC), che rappresentano la minima attività neurale per una specifica esperienza conscia, vengono messe in relazione misure comportamentali di consapevolezza, limitatamente a stimoli presentati in un contesto sperimentale, con i sottostanti meccanismi neurali. Attraverso paradigmi sperimentali come la rivalità binoculare e tecniche di mascheramento visivo è possibile provare ad identificare i CNC contenuto-specifici utilizzando misure neurofisiologiche e tecniche di neuroimaging. Tali tecniche forniscono infatti utili informazioni circa le basi neuroanatomiche e funzionali dell'esperienza sotto esame. Sebbene i meccanismi che sottendono l’attenzione siano spesso associati all'esperienza cosciente, evidenze sperimentali suggeriscono una separazione tra i due processi. Le ricerche sui correlati neurali della consapevolezza visiva indicano come l’attività di una singola area cerebrale non possa essere necessaria e sufficiente a spiegare le qualità dei contenuti coscienti. Sembrerebbe invece essere necessaria una rappresentazione della scena visiva distribuita nella corteccia visiva primaria (V1) e nelle aree visive ventrali con attivazione di regioni temporo-parietali. Misure elettrofisiologiche come la visual awareness negativity (VAN) sono state correlate alla consapevolezza visiva mentre altri indicatori sembrerebbero essere maggiormente legati a processi attentivi. Diversi modelli teorici offrono spiegazioni empiriche sull’emergenza della coscienza dall’attività cerebrale. Nel caso della consapevolezza visiva, alcuni modelli teorici rilevanti sono la teoria dello spazio di lavoro neurale globale, la quale sottolinea la necessità di condivisione dell'informazione tra ampie aree cerebrali e la teoria dell'elaborazione ricorrente che si concentra invece sul feedback proveniente a V1 dalle aree extrastriate. Inoltre, il modello dell’”elaborazione predittiva” descrive la percezione cosciente come il risultato di un processo attivo in cui il cervello crea costantemente previsioni sull’ambiente circostante. Allo stato attuale, la ricerca sui correlati neurali della consapevolezza visiva evidenzia dunque come un network di regioni cerebrali posteriori sia fondamentale per avere esperienze visive coscienti. Inoltre, i segnali di feedback sembrano svolgere un ruolo cruciale, evidenziando le complesse interazioni tra dinamiche neurali e percezione cosciente.The paper aims to present the current evidence regarding how subjective contents of visual awareness are encoded at the neural level. While the neural mechanisms of visual perception are well understood, it remains unclear how visual information becomes part of consciousness. To identify the neural correlates of consciousness (NCC), representing the minimum neural activity for a specific conscious experience, behavioral measures of awareness are related to underlying neural mechanisms, limited to stimuli presented in an experimental context. Through experimental paradigms such as binocular rivalry and visual masking techniques, it is possible to attempt to identify content-specific NCC using neurophysiological measures and neuroimaging techniques. These techniques indeed provide valuable information about the neuroanatomical and functional basis of the examined experience. Although mechanisms underlying attention are often associated with conscious experience, experimental evidence suggests a separation between the two processes. Research on the neural correlates of visual awareness indicates that the activity of a single brain area may not be necessary and sufficient to explain the qualities of conscious contents. Instead, a distributed representation of the visual scene in the primary visual cortex (V1) and ventral visual areas with activation of temporo-parietal regions seems to be necessary. Electrophysiological measures such as Visual Awareness Negativity (VAN) have been correlated with visual awareness, while other indicators appear to be more related to attentional processes. Various theoretical models offer empirical explanations of the emergence of consciousness from brain activity. In the case of visual awareness, some relevant theoretical models include the global neural workspace theory, which emphasizes the need for information sharing among extensive brain areas, and the recurrent processing theory, which focuses on feedback from extrastriate areas to V1. Additionally, the predictive processing model describes conscious perception as the result of an active process in which the brain constantly generates predictions about the surrounding environment. Currently, research on the neural correlates of visual awareness highlights the importance of a network of posterior brain regions for conscious visual experiences. Furthermore, feedback signals appear to play a crucial role, highlighting the complex interactions between neural dynamics and conscious perception

    25th Annual Computational Neuroscience Meeting: CNS-2016

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    Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

    Uncertainty In serial dependence

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    Learning and Decision Making in Social Contexts: Neural and Computational Models

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    Social interaction is one of humanity's defining features. Through it, we develop ideas, express emotions, and form relationships. In this thesis, we explore the topic of social cognition by building biologically-plausible computational models of learning and decision making. Our goal is to develop mechanistic explanations for how the brain performs a variety of social tasks, to test those theories by simulating neural networks, and to validate our models by comparing to human and animal data. We begin by introducing social cognition from functional and anatomical perspectives, then present the Neural Engineering Framework, which we use throughout the thesis to specify functional brain models. Over the course of four chapters, we investigate many aspects of social cognition using these models. We begin by studying fear conditioning using an anatomically accurate model of the amygdala. We validate this model by comparing the response properties of our simulated neurons with real amygdala neurons, showing that simulated behavior is consistent with animal data, and exploring how simulated fear generalization relates to normal and anxious humans. Next, we show that biologically-detailed networks may realize cognitive operations that are essential for social cognition. We validate this approach by constructing a working memory network from multi-compartment cells and conductance-based synapses, then show that its mnemonic performance is comparable to animals performing a delayed match-to-sample task. In the next chapter, we study decision making and the tradeoffs between speed and accuracy: our network gathers information from the environment and tracks the value of choice alternatives, making a decision once certain criteria are met. We apply this model to a two-choice decision task, fit model parameters to recreate the behavior of individual humans, and reproduce the speed-accuracy tradeoff evident in the human population. Finally, we combine our networks for learning, working memory, and decision making into a cognitive agent that uses reinforcement learning to play a simple social game. We compare this model with two other cognitive architectures and with human data from an experiment we ran, and show that our three cognitive agents recreate important patterns in the human data, especially those related to social value orientation and cooperative behavior. Our concluding chapter summarizes our contributions to the field of social cognition and proposes directions for further research. The main contribution of this thesis is the demonstration that a diverse set of social cognitive abilities may be explained, simulated, and validated using a functionally-descriptive, biologically-plausible theoretical framework. Our models lay a foundation for studying increasingly-sophisticated forms of social cognition in future work
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