The significance of memory in sensory cortex

Early sensory cortex is typically investigated in response to sensory stimulation, masking the contribution of internal signals. Recently, van Kerkoerle and colleagues reported that attention and memory signals segregate from sensory signals within specific layers of primary visual cortex, providing insight into the role of internal signals in sensory processing

Forecasting faces in the cortex: Comment on ‘High-level prediction signals in a low-level area of the macaque face-processing hierarchy’, by Schwiedrzik and Freiwald, Neuron (2017)

Although theories of predictive coding in the brain abound, we lack key pieces of neuronal data to support these theories. Recently, Schwiedrzik and Freiwald found neurophysiological evidence for predictive codes throughout the face-processing hierarchy in macaque cortex. We highlight how these data enhance our knowledge of cortical information processing, and the impact of this more broadly

The laminar integration of sensory inputs with feedback signals in human cortex

The cortex constitutes the largest area of the human brain. Yet we have only a basic understanding of how the cortex performs one vital function: the integration of sensory signals (carried by feedforward pathways) with internal representations (carried by feedback pathways). A multi-scale, multi-species approach is essential for understanding the site of integration, computational mechanism and functional role of this processing. To improve our knowledge we must rely on brain imaging with improved spatial and temporal resolution and paradigms which can measure internal processes in the human brain, and on the bridging of disciplines in order to characterize this processing at cellular and circuit levels. We highlight apical amplification as one potential mechanism for integrating feedforward and feedback inputs within pyramidal neurons in the rodent brain. We reflect on the challenges and progress in applying this model neuronal process to the study of human cognition. We conclude that cortical-layer specific measures in humans will be an essential contribution for better understanding the landscape of information in cortical feedback, helping to bridge the explanatory gap

Contributions of cortical feedback to sensory processing in primary visual cortex

Closing the structure-function divide is more challenging in the brain than in any other organ (Lichtman and Denk, 2011). For example, in early visual cortex, feedback projections to V1 can be quantified (e.g., Budd, 1998) but the understanding of feedback function is comparatively rudimentary (Muckli and Petro, 2013). Focusing on the function of feedback, we discuss how textbook descriptions mask the complexity of V1 responses, and how feedback and local activity reflects not only sensory processing but internal brain states

Diagnostic information use to understand brain mechanisms of facial expression categorization

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 visual system via fixations reflect diagnostic information use. Inputs to the visual system dictate the information distributed to cognitive systems during the seamless and rapid categorization of expressive faces. How we perform eye-movements during this task informs how task-driven and stimulus-driven mechanisms interact to guide the extraction of information supporting recognition. We recorded eye movements of observers who categorized the six basic categories of facial expressions. We use a measure of task-relevant information (diagnosticity) to discuss oculomotor behaviour, with focus on two findings. Firstly, fixated regions reveal expression differences. Secondly, by examining fixation sequences, the intersection of fixations with diagnostic information increases in a sequence of fixations. This suggests a top-down drive to acquire task-relevant information, with different functional roles for first and final fixations. A combination of psychophysical studies of visual recognition together with the EEG (electroencephalogram) signal is used to infer the dynamics of feature extraction and use during the recognition of facial expressions in Chapter 3. The results reveal a process that integrates visual information over about 50 milliseconds prior to the face-sensitive N170 event-related potential, starting at the eye region, and proceeding gradually towards lower regions. The finding that informative features for recognition are not processed simultaneously but in an orderly progression over a short time period is instructive for understanding the processes involved in visual recognition, and in particular the integration of bottom-up and top-down processes. In Chapter 4 we use fMRI to investigate the task-dependent activation to diagnostic features in early visual areas, suggesting top-down mechanisms as V1 traditionally exhibits only simple response properties. Chapter 3 revealed that diagnostic features modulate the temporal dynamics of brain signals in higher visual areas. Within the hierarchical visual system however, it is not known if an early (V1/V2/V3) sensitivity to diagnostic information contributes to categorical facial judgements, conceivably driven by top-down signals triggered in visual processing. Using retinotopic mapping, we reveal task-dependent information extraction within the earliest cortical representation (V1) of two features known to be differentially necessary for face recognition tasks (eyes and mouth). This strategic encoding of face images is beyond typical V1 properties and suggests a top-down influence of task extending down to the earliest retinotopic stages of visual processing. The significance of these data is discussed in the context of the cortical face network and bidirectional processing in the visual system. The visual cognition of facial expression processing is concerned with the interactive processing of bottom-up sensory-driven information and top-down mechanisms to relate visual input to categorical judgements. The three experiments presented in this thesis are summarized in Chapter 5 in relation to how diagnostic features can be used to explore such processing in the human brain leading to proficient facial expression categorization

Cortical feedback signals generalise across different spatial frequencies of feedforward inputs

Visual processing in cortex relies on feedback projections contextualising feedforward information flow. Primary visual cortex (V1) has small receptive fields and processes feedforward information at a fine-grained spatial scale, whereas higher visual areas have larger, spatially invariant receptive fields. Therefore, feedback could provide coarse information about the global scene structure or alternatively recover fine-grained structure by targeting small receptive fields in V1. We tested if feedback signals generalise across different spatial frequencies of feedforward inputs, or if they are tuned to the spatial scale of the visual scene. Using a partial occlusion paradigm, functional magnetic resonance imaging (fMRI) and multivoxel pattern analysis (MVPA) we investigated whether feedback to V1 contains coarse or fine-grained information by manipulating the spatial frequency of the scene surround outside an occluded image portion. We show that feedback transmits both coarse and fine-grained information as it carries information about both low (LSF) and high spatial frequencies (HSF). Further, feedback signals containing LSF information are similar to feedback signals containing HSF information, even without a large overlap in spatial frequency bands of the HSF and LSF scenes. Lastly, we found that feedback carries similar information about the spatial frequency band across different scenes. We conclude that cortical feedback signals contain information which generalises across different spatial frequencies of feedforward inputs

A perspective on cortical layering and layer-spanning neuronal elements

This review article addresses the function of the layers of the cerebral cortex. We develop the perspective that cortical layering needs to be understood in terms of its functional anatomy, i.e., the terminations of synaptic inputs on distinct cellular compartments and their effect on cortical activity. The cortex is a hierarchical structure in which feed forward and feedback pathways have a layer-specific termination pattern. We take the view that the influence of synaptic inputs arriving at different cortical layers can only be understood in terms of their complex interaction with cellular biophysics and the subsequent computation that occurs at the cellular level. We use high-resolution fMRI, which can resolve activity across layers, as a case study for implementing this approach by describing how cognitive events arising from the laminar distribution of inputs can be interpreted by taking into account the properties of neurons that span different layers. This perspective is based on recent advances in measuring subcellular activity in distinct feed-forward and feedback axons and in dendrites as they span across layers

Scene representations conveyed by cortical feedback to early visual cortex can be described by line drawings

Human behavior is dependent on the ability of neuronal circuits to predict the outside world. Neuronal circuits in early visual areas make these predictions based on internal models that are delivered via non-feedforward connections. Despite our extensive knowledge of the feedforward sensory features that drive cortical neurons, we have a limited grasp on the structure of the brain's internal models. Progress in neuroscience therefore depends on our ability to replicate the models that the brain creates internally. Here we record human fMRI data while presenting partially occluded visual scenes. Visual occlusion allows us to experimentally control sensory input to subregions of visual cortex while internal models continue to influence activity in these regions. Since the observed activity is dependent on internal models, but not on sensory input, we have the opportunity to map visual features conveyed by the brain's internal models. Our results show that activity related to internal models in early visual cortex are more related to scene-specific features than to categorical or depth features. We further demonstrate that behavioral line drawings provide a good description of internal model structure representing scene-specific features. These findings extend our understanding of internal models, showing that line drawings provide a window into our brains' internal models of vision

Neuronal codes for predictive processing in cortical layers

Predictive processing as a computational motif of the neocortex needs to be elaborated into theories of higher cognitive functions that include simulating future behavioural outcomes. We contribute to the neuroscientific perspective of predictive processing as a foundation for the proposed representational architectures of the mind

Cortical depth profiles in primary visual cortex for illusory and imaginary experiences

Visual illusions and mental imagery are non-physical sensory experiences that involve cortical feedback processing in the primary visual cortex. Using laminar functional magnetic resonance imaging (fMRI) in two studies, we investigate if information about these internal experiences is visible in the activation patterns of different layers of primary visual cortex (V1). We find that imagery content is decodable mainly from deep layers of V1, whereas seemingly ‘real’ illusory content is decodable mainly from superficial layers. Furthermore, illusory content shares information with perceptual content, whilst imagery content does not generalise to illusory or perceptual information. Together, our results suggest that illusions and imagery, which differ immensely in their subjective experiences, also involve partially distinct early visual microcircuits. However, overlapping microcircuit recruitment might emerge based on the nuanced nature of subjective conscious experience