Search

In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales—from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration, and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues, and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research.

Neuronal activity in the primary visual cortex (V1) is driven by feedforward input from within the neurons’ receptive fields (RFs) and modulated by contextual information in regions surrounding the RF. The effect of contextual information on spiking activity occurs rapidly and is therefore challenging to dissociate from feedforward input. To address this challenge, we recorded the spiking activity of V1 neurons in monkeys viewing either natural scenes or scenes where the information in the RF was occluded, effectively removing the feedforward input. We found that V1 neurons responded rapidly and selectively to occluded scenes. V1 responses elicited by occluded stimuli could be used to decode individual scenes and could be predicted from those elicited by non-occluded images, indicating that there is an overlap between visually driven and contextual responses. We used representational similarity analysis to show that the structure of V1 representations of occluded scenes measured with electrophysiology in monkeys correlates strongly with the representations of the same scenes in humans measured with functional magnetic resonance imaging (fMRI). Our results reveal that contextual influences rapidly alter V1 spiking activity in monkeys over distances of several degrees in the visual field, carry information about individual scenes, and resemble those in human V1.

Neuronal activity in the primary visual cortex (V1) is driven by feedforward input from within the neurons' receptive fields (RFs) and modulated by contextual information in regions surrounding the RF. The effect of contextual information on spiking activity occurs rapidly and is therefore challenging to dissociate from feedforward input. To address this challenge, we recorded the spiking activity of V1 neurons in monkeys viewing either natural scenes or scenes where the information in the RF was occluded, effectively removing the feedforward input. We found that V1 neurons responded rapidly and selectively to occluded scenes. V1 responses elicited by occluded stimuli could be used to decode individual scenes and could be predicted from those elicited by non-occluded images, indicating that there is an overlap between visually driven and contextual responses. We used representational similarity analysis to show that the structure of V1 representations of occluded scenes measured with electrophysiology in monkeys correlates strongly with the representations of the same scenes in humans measured with functional magnetic resonance imaging (fMRI). Our results reveal that contextual influences rapidly alter V1 spiking activity in monkeys over distances of several degrees in the visual field, carry information about individual scenes, and resemble those in human V1.

Neuronal cortical circuitry comprises feedforward, lateral, and feedback projections, each of which terminates in distinct cortical layers [1-3]. In sensory systems, feedforward processing transmits signals from the external world into the cortex, whereas feedback pathways signal the brain's inference of the world [4-11]. However, the integration of feedforward, lateral, and feedback inputs within each cortical area impedes the investigation of feedback, and to date, no technique has isolated the feedback of visual scene information in distinct layers of healthy human cortex. We masked feedforward input to a region of V1 cortex and studied the remaining internal processing. Using high-resolution functional brain imaging (0.8 mm(3)) and multivoxel pattern information techniques, we demonstrate that during normal visual stimulation scene information peaks in mid-layers. Conversely, we found that contextual feedback information peaks in outer, superficial layers. Further, we found that shifting the position of the visual scene surrounding the mask parametrically modulates feedback in superficial layers of V1. Our results reveal the layered cortical organization of external versus internal visual processing streams during perception in healthy human subjects. We provide empirical support for theoretical feedback models such as predictive coding [10, 12] and coherent infomax [13] and reveal the potential of high-resolution fMRI to access internal processing in sub-millimeter human cortex.

Neuronal cortical circuitry comprises feedforward, lateral, and feedback projections, each of which terminates in distinct cortical layers [1-3]. In sensory systems, feedforward processing transmits signals from the external world into the cortex, whereas feedback pathways signal the brain's inference of the world [4-11]. However, the integration of feedforward, lateral, and feedback inputs within each cortical area impedes the investigation of feedback, and to date, no technique has isolated the feedback of visual scene information in distinct layers of healthy human cortex. We masked feedforward input to a region of V1 cortex and studied the remaining internal processing. Using high-resolution functional brain imaging (0.8 mm(3)) and multivoxel pattern information techniques, we demonstrate that during normal visual stimulation scene information peaks in mid-layers. Conversely, we found that contextual feedback information peaks in outer, superficial layers. Further, we found that shifting the position of the visual scene surrounding the mask parametrically modulates feedback in superficial layers of V1. Our results reveal the layered cortical organization of external versus internal visual processing streams during perception in healthy human subjects. We provide empirical support for theoretical feedback models such as predictive coding [10, 12] and coherent infomax [13] and reveal the potential of high-resolution fMRI to access internal processing in sub-millimeter human cortex.

Proficient categorization of facial expressions is crucial for normal social interaction. Neurophysiological, behavioural, event-related potential, lesion and functional neuroimaging techniques can be used to investigate the underlying brain mechanisms supporting this seemingly effortless process, and the associated arrangement of bilateral networks. These brain areas exhibit consistent and replicable activation patterns, and can be broadly defined to include visual (occipital and temporal), limbic (amygdala) and prefrontal (orbitofrontal) regions. Together, these areas support early perceptual processing, the formation of detailed representations and subsequent recognition of expressive faces. Despite the critical role of facial expressions in social communication and extensive work in this area, it is still not known how the brain decodes nonverbal signals in terms of expression-specific features. For these reasons, this thesis investigates the role of these so-called diagnostic facial features at three significant stages in expression recognition; the spatiotemporal inputs to the visual system, the dynamic integration of features in higher visual (occipitotemporal) areas, and early sensitivity to features in V1. In Chapter 1, the basic emotion categories are presented, along with the brain regions that are activated by these expressions. In line with this, the current cognitive theory of face processing reviews functional and anatomical dissociations within the distributed neural “face network”. Chapter 1 also introduces the way in which we measure and use diagnostic information to derive brain sensitivity to specific facial features, and how this is a useful tool by which to understand spatial and temporal organisation of expression recognition in the brain. In relation to this, hierarchical, bottom-up neural processing is discussed along with high-level, top-down facilitatory mechanisms. Chapter 2 describes an eye-movement study that reveals inputs to the visua

Proficient categorization of facial expressions is crucial for normal social interaction. Neurophysiological, behavioural, event-related potential, lesion and functional neuroimaging techniques can be used to investigate the underlying brain mechanisms supporting this seemingly effortless process, and the associated arrangement of bilateral networks. These brain areas exhibit consistent and replicable activation patterns, and can be broadly defined to include visual (occipital and temporal), limbic (amygdala) and prefrontal (orbitofrontal) regions. Together, these areas support early perceptual processing, the formation of detailed representations and subsequent recognition of expressive faces. Despite the critical role of facial expressions in social communication and extensive work in this area, it is still not known how the brain decodes nonverbal signals in terms of expression-specific features. For these reasons, this thesis investigates the role of these so-called diagnostic facial features at three significant stages in expression recognition; the spatiotemporal inputs to the visual system, the dynamic integration of features in higher visual (occipitotemporal) areas, and early sensitivity to features in V1. In Chapter 1, the basic emotion categories are presented, along with the brain regions that are activated by these expressions. In line with this, the current cognitive theory of face processing reviews functional and anatomical dissociations within the distributed neural “face network”. Chapter 1 also introduces the way in which we measure and use diagnostic information to derive brain sensitivity to specific facial features, and how this is a useful tool by which to understand spatial and temporal organisation of expression recognition in the brain. In relation to this, hierarchical, bottom-up neural processing is discussed along with high-level, top-down facilitatory mechanisms. Chapter 2 describes an eye-movement study that reveals inputs to the visua

Proficient categorization of facial expressions is crucial for normal social interaction. Neurophysiological, behavioural, event-related potential, lesion and functional neuroimaging techniques can be used to investigate the underlying brain mechanisms supporting this seemingly effortless process, and the associated arrangement of bilateral networks. These brain areas exhibit consistent and replicable activation patterns, and can be broadly defined to include visual (occipital and temporal), limbic (amygdala) and prefrontal (orbitofrontal) regions. Together, these areas support early perceptual processing, the formation of detailed representations and subsequent recognition of expressive faces. Despite the critical role of facial expressions in social communication and extensive work in this area, it is still not known how the brain decodes nonverbal signals in terms of expression-specific features. For these reasons, this thesis investigates the role of these so-called diagnostic facial features at three significant stages in expression recognition; the spatiotemporal inputs to the visual system, the dynamic integration of features in higher visual (occipitotemporal) areas, and early sensitivity to features in V1. In Chapter 1, the basic emotion categories are presented, along with the brain regions that are activated by these expressions. In line with this, the current cognitive theory of face processing reviews functional and anatomical dissociations within the distributed neural “face network”. Chapter 1 also introduces the way in which we measure and use diagnostic information to derive brain sensitivity to specific facial features, and how this is a useful tool by which to understand spatial and temporal organisation of expression recognition in the brain. In relation to this, hierarchical, bottom-up neural processing is discussed along with high-level, top-down facilitatory mechanisms. Chapter 2 describes an eye-movement study that reveals inputs to the visua

Proficient categorization of facial expressions is crucial for normal social interaction. Neurophysiological, behavioural, event-related potential, lesion and functional neuroimaging techniques can be used to investigate the underlying brain mechanisms supporting this seemingly effortless process, and the associated arrangement of bilateral networks. These brain areas exhibit consistent and replicable activation patterns, and can be broadly defined to include visual (occipital and temporal), limbic (amygdala) and prefrontal (orbitofrontal) regions. Together, these areas support early perceptual processing, the formation of detailed representations and subsequent recognition of expressive faces. Despite the critical role of facial expressions in social communication and extensive work in this area, it is still not known how the brain decodes nonverbal signals in terms of expression-specific features. For these reasons, this thesis investigates the role of these so-called diagnostic facial features at three significant stages in expression recognition; the spatiotemporal inputs to the visual system, the dynamic integration of features in higher visual (occipitotemporal) areas, and early sensitivity to features in V1. In Chapter 1, the basic emotion categories are presented, along with the brain regions that are activated by these expressions. In line with this, the current cognitive theory of face processing reviews functional and anatomical dissociations within the distributed neural “face network”. Chapter 1 also introduces the way in which we measure and use diagnostic information to derive brain sensitivity to specific facial features, and how this is a useful tool by which to understand spatial and temporal organisation of expression recognition in the brain. In relation to this, hierarchical, bottom-up neural processing is discussed along with high-level, top-down facilitatory mechanisms. Chapter 2 describes an eye-movement study that reveals inputs to the visua

Proficient categorization of facial expressions is crucial for normal social interaction. Neurophysiological, behavioural, event-related potential, lesion and functional neuroimaging techniques can be used to investigate the underlying brain mechanisms supporting this seemingly effortless process, and the associated arrangement of bilateral networks. These brain areas exhibit consistent and replicable activation patterns, and can be broadly defined to include visual (occipital and temporal), limbic (amygdala) and prefrontal (orbitofrontal) regions. Together, these areas support early perceptual processing, the formation of detailed representations and subsequent recognition of expressive faces. Despite the critical role of facial expressions in social communication and extensive work in this area, it is still not known how the brain decodes nonverbal signals in terms of expression-specific features. For these reasons, this thesis investigates the role of these so-called diagnostic facial features at three significant stages in expression recognition; the spatiotemporal inputs to the visual system, the dynamic integration of features in higher visual (occipitotemporal) areas, and early sensitivity to features in V1. In Chapter 1, the basic emotion categories are presented, along with the brain regions that are activated by these expressions. In line with this, the current cognitive theory of face processing reviews functional and anatomical dissociations within the distributed neural “face network”. Chapter 1 also introduces the way in which we measure and use diagnostic information to derive brain sensitivity to specific facial features, and how this is a useful tool by which to understand spatial and temporal organisation of expression recognition in the brain. In relation to this, hierarchical, bottom-up neural processing is discussed along with high-level, top-down facilitatory mechanisms. Chapter 2 describes an eye-movement study that reveals inputs to the visua

Proficient categorization of facial expressions is crucial for normal social interaction. Neurophysiological, behavioural, event-related potential, lesion and functional neuroimaging techniques can be used to investigate the underlying brain mechanisms supporting this seemingly effortless process, and the associated arrangement of bilateral networks. These brain areas exhibit consistent and replicable activation patterns, and can be broadly defined to include visual (occipital and temporal), limbic (amygdala) and prefrontal (orbitofrontal) regions. Together, these areas support early perceptual processing, the formation of detailed representations and subsequent recognition of expressive faces. Despite the critical role of facial expressions in social communication and extensive work in this area, it is still not known how the brain decodes nonverbal signals in terms of expression-specific features. For these reasons, this thesis investigates the role of these so-called diagnostic facial features at three significant stages in expression recognition; the spatiotemporal inputs to the visual system, the dynamic integration of features in higher visual (occipitotemporal) areas, and early sensitivity to features in V1. In Chapter 1, the basic emotion categories are presented, along with the brain regions that are activated by these expressions. In line with this, the current cognitive theory of face processing reviews functional and anatomical dissociations within the distributed neural “face network”. Chapter 1 also introduces the way in which we measure and use diagnostic information to derive brain sensitivity to specific facial features, and how this is a useful tool by which to understand spatial and temporal organisation of expression recognition in the brain. In relation to this, hierarchical, bottom-up neural processing is discussed along with high-level, top-down facilitatory mechanisms. Chapter 2 describes an eye-movement study that reveals inputs to the visua