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  • thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences  (9)
  • English  (9)
  • 1
    Publication Date: 2024-04-05
    Description: Neuromorphic engineering has just reached its 25th year as a discipline. In the first two decades neuromorphic engineers focused on building models of sensors, such as silicon cochleas and retinas, and building blocks such as silicon neurons and synapses. These designs have honed our skills in implementing sensors and neural networks in VLSI using analog and mixed mode circuits. Over the last decade the address event representation has been used to interface devices and computers from different designers and even different groups. This facility has been essential for our ability to combine sensors, neural networks, and actuators into neuromorphic systems. More recently, several big projects have emerged to build very large scale neuromorphic systems. The Telluride Neuromorphic Engineering Workshop (since 1994) and the CapoCaccia Cognitive Neuromorphic Engineering Workshop (since 2009) have been instrumental not only in creating a strongly connected research community, but also in introducing different groups to each other’s hardware. Many neuromorphic systems are first created at one of these workshops. With this special research topic, we showcase the state-of-the-art in neuromorphic systems.
    Keywords: RC321-571 ; Q1-390 ; neuromorphic engineering ; Learning ; Floating gate ; Neural Network ; spike-based ; event-based ; simulation ; dynamic vision sensor ; network ; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences
    Language: English
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  • 2
    Publication Date: 2024-04-05
    Description: Historically, cognitive sciences have considered selective attention and working memory as largely separated cognitive functions. That is, selective attention as a concept is typically reserved for the processes that allow for the prioritization of specific sensory input, while working memory entails more central structures for maintaining (and operating on) temporary mental representations. However, over the last decades various observations have been reported that question such sharp distinction. Most importantly, information stored in working memory has been shown to modulate selective attention processing – and vice versa. At the theoretical level, these observations are paralleled by an increasingly dominant focus on working memory as (involving) the attended part of long-term memory, with some positions considering that working memory is equivalent to selective attention turned to long-term memory representations – or internal selective attention. This questions the existence of working memory as a dedicated cognitive function and raises the need for integrative accounts of working memory and attention. The next step will be to explore the precise implications of attentional accounts of WM for the understanding of specific aspects and characteristics of WM, such as serial order processing, its modality-specificity, its capacity limitations, its relation with executive functions, as well as the nature of attentional mechanisms involved. This research topic in Frontiers in Human Neuroscience aims at bringing together the latest insights and findings about the interplay between working memory and selective attention.
    Keywords: RC321-571 ; Q1-390 ; spatial attention ; working memory ; executive attention ; Internal attention ; short-term memory ; selective attention ; serial order ; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences
    Language: English
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  • 3
    Publication Date: 2024-04-05
    Description: The huge volume of multi-modal neuroimaging data across different neuroscience communities has posed a daunting challenge to traditional methods of data sharing, data archiving, data processing and data analysis. Neuroinformatics plays a crucial role in creating advanced methodologies and tools for the handling of varied and heterogeneous datasets in order to better understand the structure and function of the brain. These tools and methodologies not only enhance data collection, analysis, integration, interpretation, modeling, and dissemination of data, but also promote data sharing and collaboration. This Neuroinformatics Research Topic aims to summarize the state-of-art of the current achievements and explores the directions for the future generation of neuroinformatics infrastructure. The publications present solutions for data archiving, data processing and workflow, data mining, and system integration methodologies. Some of the systems presented are large in scale, geographically distributed, and already have a well-established user community. Some discuss opportunities and methodologies that facilitate large-scale parallel data processing tasks under a heterogeneous computational environment. We wish to stimulate on-going discussions at the level of the neuroinformatics infrastructure including the common challenges, new technologies of maximum benefit, key features of next generation infrastructure, etc. We have asked leading research groups from different research areas of neuroscience/neuroimaging to provide their thoughts on the development of a state of the art and highly-efficient neuroinformatics infrastructure. Such discussions will inspire and help guide the development of a state of the art, highly-efficient neuroinformatics infrastructure.
    Keywords: RC321-571 ; Q1-390 ; Neuroimaging ; database ; neuroinformatics ; workflow ; infrastructure ; high-throughput ; data processing ; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences
    Language: English
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  • 4
    Publication Date: 2024-04-05
    Description: Modern neural networks gave rise to major breakthroughs in several research areas. In neuroscience, we are witnessing a reappraisal of neural network theory and its relevance for understanding information processing in biological systems. The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing. We consider the biological plausibility of neural networks, performance improvements, spiking neural networks and the use of neural networks for understanding brain function.
    Keywords: RC321-571 ; Q1-390 ; brain imaging ; artificial neural networks ; deep learning ; neural information processing ; backpropagation ; spiking neural networks ; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences
    Language: English
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  • 5
    Publication Date: 2024-04-05
    Description: What can we learn from spontaneously occurring brain and other physiological signals about an individual’s cognitive and affective state and how can we make use of this information? One line of research that is actively involved with this question is Passive Brain-Computer-Interfaces (BCI). To date most BCIs are aimed at assisting patients for whom brain signals could form an alternative output channel as opposed to more common human output channels, like speech and moving the hands. However, brain signals (possibly in combination with other physiological signals) also form an output channel above and beyond the more usual ones: they can potentially provide continuous, online information about an individual’s cognitive and affective state without the need of conscious or effortful communication. The provided information could be used in a number of ways. Examples include monitoring cognitive workload through EEG and skin conductance for adaptive automation or using ERPs in response to errors to correct for a behavioral response. While Passive BCIs make use of online (neuro)physiological responses and close the interaction cycle between a user and a computer system, (neuro)physiological responses can also be used in an offline fashion. Examples of this include detecting amygdala responses for neuromarketing, and measuring EEG and pupil dilation as indicators of mental effort for optimizing information systems. The described field of applied (neuro)physiology can strongly benefit from high quality scientific studies that control for confounding factors and use proper comparison conditions. Another area of relevance is ethics, ranging from dubious product claims, acceptance of the technology by the general public, privacy of users, to possible effects that these kinds of applications may have on society as a whole. In this Research Topic we aimed to publish studies of the highest scientific quality that are directed towards applications that utilize spontaneously, effortlessly generated neurophysiological signals (brain and/or other physiological signals) reflecting cognitive or affective state. We especially welcomed studies that describe specific real world applications demonstrating a significant benefit compared to standard applications. We also invited original, new kinds of (proposed) applications in this area as well as comprehensive review articles that point out what is and what is not possible (according to scientific standards) in this field. Finally, we welcomed manuscripts on the ethical issues that are involved. Connected to the Research Topic was a workshop (held on June 6, during the Fifth International Brain-Computer Interface Meeting, June 3-7, 2013, Asilomar, California) that brought together a diverse group of people who were working in this field. We discussed the state of the art and formulated major challenges, as reflected in the first paper of the Research Topic.
    Keywords: RC321-571 ; Q1-390 ; Brain-computer interface ; cognitive state ; EEG ; affective state ; physiological computing ; mental state ; applied neuroscience ; Psychophysiology ; neuroergonomics ; Neurophysiology ; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences
    Language: English
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  • 6
    Publication Date: 2024-04-05
    Description: Supercomputing facilities are becoming increasingly available for simulating activity dynamics in large-scale neuronal networks. On today's most advanced supercomputers, networks with up to a billion of neurons can be readily simulated. However, building biologically realistic, full-scale brain models requires more than just a huge number of neurons. In addition to network size, the detailed local and global anatomy of neuronal connections is of crucial importance. Moreover, anatomical connectivity is not fixed, but can rewire throughout life (structural plasticity)—an aspect that is missing in most current network models, in which plasticity is confined to changes in synaptic strength (synaptic plasticity). The papers in this Ebook, which may broadly be divided into three themes, aim to bring together high-performance computing with recent experimental and computational research in neuroanatomy. In the first theme (fiber connectivity), new methods are described for measuring and data-basing microscopic and macroscopic connectivity. In the second theme (structural plasticity), novel models are introduced that incorporate morphological plasticity and rewiring of anatomical connections. In the third theme (large-scale simulations), simulations of large-scale neuronal networks are presented with an emphasis on anatomical detail and plasticity mechanisms. Together, the articles in this Ebook make the reader aware of the methods and models by which large-scale brain networks running on supercomputers can be extended to include anatomical detail and plasticity.Supercomputing facilities are becoming increasingly available for simulating activity dynamics in large-scale neuronal networks. On today's most advanced supercomputers, networks with up to a billion of neurons can be readily simulated. However, building biologically realistic, full-scale brain models requires more than just a huge number of neurons. In addition to network size, the detailed local and global anatomy of neuronal connections is of crucial importance. Moreover, anatomical connectivity is not fixed, but can rewire throughout life (structural plasticity)—an aspect that is missing in most current network models, in which plasticity is confined to changes in synaptic strength (synaptic plasticity). The papers in this Ebook, which may broadly be divided into three themes, aim to bring together high-performance computing with recent experimental and computational research in neuroanatomy. In the first theme (fiber connectivity), new methods are described for measuring and data-basing microscopic and macroscopic connectivity. In the second theme (structural plasticity), novel models are introduced that incorporate morphological plasticity and rewiring of anatomical connections. In the third theme (large-scale simulations), simulations of large-scale neuronal networks are presented with an emphasis on anatomical detail and plasticity mechanisms. Together, the articles in this Ebook make the reader aware of the methods and models by which large-scale brain networks running on supercomputers can be extended to include anatomical detail and plasticity.
    Keywords: RC321-571 ; Q1-390 ; Supercomputing ; structural plasticity ; connectivity ; brain models ; simulation ; Anatomy ; high-performance computing ; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences
    Language: English
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  • 7
    Publication Date: 2024-04-05
    Description: Proteolysis by the ubiquitin-proteasome pathway (UPP) in the nervous system has been extensively studied both in the context of normal physiological function as well as abnormal pathological conditions. Although ubiquitin was used as a marker of brain pathology, the normal functions of the UPP were not studied much in the nervous system until the 1990s. The early investigations focused on synaptic plasticity which was followed by studies on the roles of protein degradation in the development of the nervous system. Research on the role of abnormal roles of the UPP follows a parallel trajectory. Since the 2000s, the field has grown to encompass many subareas of research and several model systems. Despite the progress made, many unanswered questions still remain. For example, there are many unknowns about the precise spatial and temporal control of protein degradation in the normal nervous system. With respect to the roles of proteolysis in brain pathology a major challenge is to elucidate the connection between impaired protein degradation and disease progression. In addition, in-depth studies of the roles of ubiquitin-proteasome-mediated proteolysis in neurodegenerative diseases are promising in identifying therapeutic targets. This ebook contains original research papers and insightful reviews that cover several aspects of proteolysis by the UPP and its physiological as well as pathological functions in the nervous system.
    Keywords: RC321-571 ; Q1-390 ; proteasome ; memory ; neuroinflammation ; neurodegenerative ; synaptic plasticity ; aging ; deubiquitinating ; Huntington's disease ; Alzheimer's disease ; glutamate receptors ; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences
    Language: English
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  • 8
    Publication Date: 2024-04-05
    Description: Models of adaptation and neural plasticity are often demonstrated in robotic scenarios with heavily pre-processed and regulated information streams to provide learning algorithms with appropriate, well timed, and meaningful data to match the assumptions of learning rules. On the contrary, natural scenarios are often rich of raw, asynchronous, overlapping and uncertain inputs and outputs whose relationships and meaning are progressively acquired, disambiguated, and used for further learning. Therefore, recent research efforts focus on neural embodied systems that rely less on well timed and pre-processed inputs, but rather extract autonomously relationships and features in time and space. In particular, realistic and more complete models of plasticity must account for delayed rewards, noisy and ambiguous data, emerging and novel input features during online learning. Such approaches model the progressive acquisition of knowledge into neural systems through experience in environments that may be affected by ambiguities, uncertain signals, delays, or novel features.
    Keywords: RC321-571 ; Q1-390 ; Neuro-robotics ; emobodied cognition ; neural plasticity ; Neural adaptation ; Cognitive Modeling ; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences
    Language: English
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  • 9
    Publication Date: 2024-04-05
    Description: Hemispatial neglect is the failure to report, respond to, or orient to novel or meaningful stimuli presented in the contralesional visual field. It constitutes one of the most invalidating neurological disorders that can occur after stroke. It is therefore important to treat neglect as adequate as possible and much of the research dedicated to neglect therefore focuses on rehabilitation. In this special topic, you will find 29 articles on the rehabilitation of neglect. This Research Topic has opened new perspectives, and has given us an indication of where the field is going. Although some of the current rehabilitation techniques have proven to be beneficial, there is limited agreement on the most valuable technique or the mechanisms underlying the ameliorating effects.
    Keywords: RC321-571 ; Q1-390 ; Prism adaptation ; Stroke ; Brain Stimulation ; visual scan training ; computer-based testing ; Rehabilitation ; Neuropsychology ; brain damage ; neglect ; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences
    Language: English
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