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The amount of data being produced by neuroscientists is increasing rapidly, driven by advances in neuroimaging and recording techniques spanning multiple scales of resolution. The availability of such data poses significant challenges for their processing and interpretation. To gain a deeper understanding of the surrounding issues, the Editors of this e-Book reached out to an interdisciplinary community, and formed the Cortical Networks Working Group. The genesis of this e-Book thus began with this Working Group through support from the National Institute for Mathematical and Biological Synthesis in the USA. The Group consisted of scientists from neuroscience, physics, psychology and computer science, and meetings were held in person (a detailed list of the group members is presented in the Editorial that follows). At the time we started, in 2010, the term “big data” was hardly in existence, though the volume of data we were handling would certainly have qualified. Furthermore, there was significant interest in harnessing the power of supercomputers to perform large scale neuronal simulations, and in creating specialized hardware to mimic neural function. We realized that the various disciplines represented in our Group could and should work together to accelerate progress in Neuroscience. We searched for common threads that could define the foundation for an integrated approach to solve important problems in the field. We adopted a network-centric perspective to address these challenges, as the data are derived from structures that are themselves network-like. We proposed three inter-twined threads, consisting of measurement of neural activity, analysis of network structures deduced from this activity, and modeling of network function, leading to theoretical insights. This approach formed the foundation of our initial call for papers. When we issued the call for papers, we were not sure how many papers would fall into each of these threads. We were pleased that we found significant interest in each thread, and the number of submissions exceeded our expectations. This is an indication that the field of neuroscience is ripe for the type of integration and interchange that we had anticipated. We first published a special topics issue after we received a sufficient number of submissions. This is now being converted to an e-book to strengthen the coherence of its contributions. One of the strong themes emerging in this e-book is that network-based measures capture better the dynamics of brain processes, and provide features with greater discriminative power than point-based measures. Another theme is the importance of network oscillations and synchrony. Current research is shedding light on the principles that govern the establishment and maintenance of network oscillation states. These principles could explain why there is impaired synchronization between different brain areas in schizophrenics and Parkinson’s patients. Such research could ultimately provide the foundation for an understanding of other psychiatric and neurodegenerative conditions. The chapters in this book cover these three main threads related to cortical networks. Some authors have combined two or more threads within a single chapter. We expect the availability of related work appearing in a single e-book to help our readers see the connection between different research efforts, and spur further insights and research.

neural synchrony --- cortical networks --- Graph measures --- neural dynamics --- emergent properties

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The amount of data being produced by neuroscientists is increasing rapidly, driven by advances in neuroimaging and recording techniques spanning multiple scales of resolution. The availability of such data poses significant challenges for their processing and interpretation. To gain a deeper understanding of the surrounding issues, the Editors of this e-Book reached out to an interdisciplinary community, and formed the Cortical Networks Working Group. The genesis of this e-Book thus began with this Working Group through support from the National Institute for Mathematical and Biological Synthesis in the USA. The Group consisted of scientists from neuroscience, physics, psychology and computer science, and meetings were held in person (a detailed list of the group members is presented in the Editorial that follows). At the time we started, in 2010, the term “big data” was hardly in existence, though the volume of data we were handling would certainly have qualified. Furthermore, there was significant interest in harnessing the power of supercomputers to perform large scale neuronal simulations, and in creating specialized hardware to mimic neural function. We realized that the various disciplines represented in our Group could and should work together to accelerate progress in Neuroscience. We searched for common threads that could define the foundation for an integrated approach to solve important problems in the field. We adopted a network-centric perspective to address these challenges, as the data are derived from structures that are themselves network-like. We proposed three inter-twined threads, consisting of measurement of neural activity, analysis of network structures deduced from this activity, and modeling of network function, leading to theoretical insights. This approach formed the foundation of our initial call for papers. When we issued the call for papers, we were not sure how many papers would fall into each of these threads. We were pleased that we found significant interest in each thread, and the number of submissions exceeded our expectations. This is an indication that the field of neuroscience is ripe for the type of integration and interchange that we had anticipated. We first published a special topics issue after we received a sufficient number of submissions. This is now being converted to an e-book to strengthen the coherence of its contributions. One of the strong themes emerging in this e-book is that network-based measures capture better the dynamics of brain processes, and provide features with greater discriminative power than point-based measures. Another theme is the importance of network oscillations and synchrony. Current research is shedding light on the principles that govern the establishment and maintenance of network oscillation states. These principles could explain why there is impaired synchronization between different brain areas in schizophrenics and Parkinson’s patients. Such research could ultimately provide the foundation for an understanding of other psychiatric and neurodegenerative conditions. The chapters in this book cover these three main threads related to cortical networks. Some authors have combined two or more threads within a single chapter. We expect the availability of related work appearing in a single e-book to help our readers see the connection between different research efforts, and spur further insights and research.

neural synchrony --- cortical networks --- Graph measures --- neural dynamics --- emergent properties --- neural synchrony --- cortical networks --- Graph measures --- neural dynamics --- emergent properties

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Correlated activity in populations of neurons has been observed in many brain regions and plays a central role in cortical coding, attention, and network dynamics. Accurately quantifying neuronal correlations presents several difficulties. For example, despite recent advances in multicellular recording techniques, the number of neurons from which spiking activity can be simultaneously recorded remains orders magnitude smaller than the size of local networks. In addition, there is a lack of consensus on the distribution of pairwise spike cross correlations obtained in extracellular multi-unit recordings. These challenges highlight the need for theoretical and computational approaches to understand how correlations emerge and to decipher their functional role in the brain.

Neurons. --- Brain function. --- Nerve cells --- Neurocytes --- Cells --- Nervous system --- Neural coding --- neural synchrony --- spike train analysis --- neuronal networks --- noise correlation --- Neuronal correlations

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Cognitive processing is commonly conceptualized as being restricted to the cerebral cortex. Accordingly, electrophysiology, neuroimaging and lesion studies involving human and animal subjects have almost exclusively focused on defining roles for cerebral cortical areas in cognition. Roles for the thalamus in cognition have been largely ignored despite the fact that the extensive connectivity between the thalamus and cerebral cortex gives rise to a closely coupled thalamo-cortical system. However, in recent years, growing interest in the thalamus as much more than a passive sensory structure, as well as methodological advances such as high-resolution functional magnetic resonance imaging of the thalamus and improved electrode targeting to subregions of thalamic nuclei using electrical stimulation and diffusion tensor imaging, have fostered research into thalamic contributions to cognition. Evidence suggests that behavioral context modulates processing in primary sensory, or first-order, thalamic nuclei (for example, the lateral geniculate and ventral posterior nuclei), allowing attentional filtering of incoming sensory information at an early stage of brain processing. Behavioral context appears to more strongly influence higher-order thalamic nuclei (for example, the pulvinar and mediodorsal nucleus), which receive major input from the cortex rather than the sensory periphery. Such higher-order thalamic nuclei have been shown to regulate information transmission in frontal and higher-order sensory cortex according to cognitive demands. This Research Topic aims to bring together neuroscientists who study different parts of the thalamus, particularly thalamic nuclei other than the primary sensory relays, and highlight the thalamic contributions to attention, memory, reward processing, decision-making, and language. By doing so, an emphasis is also placed on neural mechanisms common to many, if not all, of these cognitive operations, such as thalamo-cortical interactions and modulatory influences from sources in the brainstem and basal ganglia. The overall view that emerges is that the thalamus is a vital node in brain networks supporting cognition.

Neuroscience. --- Neuroscience --- Human Anatomy & Physiology --- Health & Biological Sciences --- neural synchrony --- cognitive control --- intralaminar thalamus --- mediodorsal thalamus --- Memory --- Pulvinar --- thalamocortical interactions --- oscillations --- anterior thalamus --- Prefrontal Cortex --- neural synchrony --- cognitive control --- intralaminar thalamus --- mediodorsal thalamus --- Memory --- Pulvinar --- thalamocortical interactions --- oscillations --- anterior thalamus --- Prefrontal Cortex