Highly accurate retinotopic maps of the physiological blind spot in human visual cortex

The physiological blind spot is a naturally occurring scotoma corresponding with the optic disc in the retina of each eye. Even during monocular viewing, observers are usually oblivious to the scotoma, in part because the visual system extrapolates information from the surrounding area. Unfortunately, studying this visual field region with neuroimaging has proven difficult, as it occupies only a small part of retinotopic cortex. Here, we used functional magnetic resonance imaging and a novel data-driven method for mapping the retinotopic organization in and around the blind spot representation in V1. Our approach allowed for highly accurate reconstructions of the extent of an observer’s blind spot, and out-performed conventional model-based analyses. This method opens exciting opportunities to study the plasticity of receptive fields after visual field loss, and our data add to evidence suggesting that the neural circuitry responsible for impressions of perceptual completion across the physiological blind spot most likely involves regions of extrastriate cortex—beyond V1

Plasticity of visual field representations

Unravelling the organization of the visual cortex is fundamental to understanding the degree to which the adult visual cortex has the capability to adapt its function and structure. The research in this thesis aimed to: 1) understand how the visual field representations present in the human visual cortex are shaped by visual experience, predictive mechanisms, damage due to visual field defects or developmental disorders, and 2) develop advanced techniques and paradigms to characterize population receptive fields (pRFs) and their connections using neurocomputational models. To do so, I combined the neuroimaging technique functional magnetic resonance imaging (fMRI) with biologically-driven neurocomputational models to investigate whether neurons – at the population or subpopulation level – have the capacity to modify their receptive field properties following damage (artificial and natural) to the human visual system or following changes in the stimulus. The main project outcomes are: 1) the development of a new a versatile brain mapping technique that captures the activity of neuronal subpopulations with minimal prior assumptions and high resolution, which we call micro probing (MP); 2) the design of alternative visual mapping stimuli, with which we have shown that the recruitment of neural resources depends on the task and/or stimulus; 3) the development of a novel approach to map the visual field and that enables the evaluation of vision loss and provides important information about the function of the visual cortex and 4) the finding that in response to an artificial scotoma (mimicking a lesion to the visual system), there is a system-wide reconfiguration of cortical connectivity and RFs which may underlie the predictive masking of scotomas. These novel techniques and findings increase our understanding of the neuroplastic properties of the visual cortex and may be applied in the evaluation of pre- and post-treatment strategies that aim for vision restoration and rehabilitation

Experience-driven formation of parts-based representations in a model of layered visual memory

Growing neuropsychological and neurophysiological evidence suggests that the visual cortex uses parts-based representations to encode, store and retrieve relevant objects. In such a scheme, objects are represented as a set of spatially distributed local features, or parts, arranged in stereotypical fashion. To encode the local appearance and to represent the relations between the constituent parts, there has to be an appropriate memory structure formed by previous experience with visual objects. Here, we propose a model how a hierarchical memory structure supporting efficient storage and rapid recall of parts-based representations can be established by an experience-driven process of self-organization. The process is based on the collaboration of slow bidirectional synaptic plasticity and homeostatic unit activity regulation, both running at the top of fast activity dynamics with winner-take-all character modulated by an oscillatory rhythm. These neural mechanisms lay down the basis for cooperation and competition between the distributed units and their synaptic connections. Choosing human face recognition as a test task, we show that, under the condition of open-ended, unsupervised incremental learning, the system is able to form memory traces for individual faces in a parts-based fashion. On a lower memory layer the synaptic structure is developed to represent local facial features and their interrelations, while the identities of different persons are captured explicitly on a higher layer. An additional property of the resulting representations is the sparseness of both the activity during the recall and the synaptic patterns comprising the memory traces.Comment: 34 pages, 12 Figures, 1 Table, published in Frontiers in Computational Neuroscience (Special Issue on Complex Systems Science and Brain Dynamics), http://www.frontiersin.org/neuroscience/computationalneuroscience/paper/10.3389/neuro.10/015.2009

Organization of the dorsal lateral geniculate nucleus in the mouse

AbstractThe dorsal lateral geniculate nucleus (dLGN) of the thalamus is the principal conduit for visual information from retina to visual cortex. Viewed initially as a simple relay, recent studies in the mouse reveal far greater complexity in the way input from the retina is combined, transmitted, and processed in dLGN. Here we consider the structural and functional organization of the mouse retinogeniculate pathway by examining the patterns of retinal projections to dLGN and how they converge onto thalamocortical neurons to shape the flow of visual information to visual cortex.</jats:p

Seven properties of self-organization in the human brain

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward

Coordinated neuronal ensembles in primary auditory cortical columns.

The synchronous activity of groups of neurons is increasingly thought to be important in cortical information processing and transmission. However, most studies of processing in the primary auditory cortex (AI) have viewed neurons as independent filters; little is known about how coordinated AI neuronal activity is expressed throughout cortical columns and how it might enhance the processing of auditory information. To address this, we recorded from populations of neurons in AI cortical columns of anesthetized rats and, using dimensionality reduction techniques, identified multiple coordinated neuronal ensembles (cNEs), which are groups of neurons with reliable synchronous activity. We show that cNEs reflect local network configurations with enhanced information encoding properties that cannot be accounted for by stimulus-driven synchronization alone. Furthermore, similar cNEs were identified in both spontaneous and evoked activity, indicating that columnar cNEs are stable functional constructs that may represent principal units of information processing in AI

Contextual modulation of primary visual cortex by auditory signals

Early visual cortex receives non-feedforward input from lateral and top-down connections (Muckli &amp; Petro 2013 Curr. Opin. Neurobiol. 23, 195–201. (doi:10.1016/j.conb.2013.01.020)), including long-range projections from auditory areas. Early visual cortex can code for high-level auditory information, with neural patterns representing natural sound stimulation (Vetter et al. 2014 Curr. Biol. 24, 1256–1262. (doi:10.1016/j.cub.2014.04.020)). We discuss a number of questions arising from these findings. What is the adaptive function of bimodal representations in visual cortex? What type of information projects from auditory to visual cortex? What are the anatomical constraints of auditory information in V1, for example, periphery versus fovea, superficial versus deep cortical layers? Is there a putative neural mechanism we can infer from human neuroimaging data and recent theoretical accounts of cortex? We also present data showing we can read out high-level auditory information from the activation patterns of early visual cortex even when visual cortex receives simple visual stimulation, suggesting independent channels for visual and auditory signals in V1. We speculate which cellular mechanisms allow V1 to be contextually modulated by auditory input to facilitate perception, cognition and behaviour. Beyond cortical feedback that facilitates perception, we argue that there is also feedback serving counterfactual processing during imagery, dreaming and mind wandering, which is not relevant for immediate perception but for behaviour and cognition over a longer time frame. This article is part of the themed issue ‘Auditory and visual scene analysis’

Neuron analysis of visual perception

The receptive fields of single cells in the visual system of cat and squirrel monkey were studied investigating the vestibular input affecting the cells, and the cell's responses during visual discrimination learning process. The receptive field characteristics of the rabbit visual system, its normal development, its abnormal development following visual deprivation, and on the structural and functional re-organization of the visual system following neo-natal and prenatal surgery were also studied. The results of each individual part of each investigation are detailed

How Does Our Visual System Achieve Shift and Size Invariance?

The question of shift and size invariance in the primate visual system is discussed. After a short review of the relevant neurobiology and psychophysics, a more detailed analysis of computational models is given. The two main types of networks considered are the dynamic routing circuit model and invariant feature networks, such as the neocognitron. Some specific open questions in context of these models are raised and possible solutions discussed

Review: Do the Different Sensory Areas within the Cat Anterior Ectosylvian Sulcal Cortex Collectively Represent a Network Multisensory Hub?

Current theory supports that the numerous functional areas of the cerebral cortex are organized and function as a network. Using connectional databases and computational approaches, the cerebral network has been demonstrated to exhibit a hierarchical structure composed of areas, clusters and, ultimately, hubs. Hubs are highly connected, higher-order regions that also facilitate communication between different sensory modalities. One region computationally identified network hub is the visual area of the Anterior Ectosylvian Sulcal cortex (AESc) of the cat. The Anterior Ectosylvian Visual area (AEV) is but one component of the AESc that also includes the auditory (Field of the Anterior Ectosylvian Sulcus - FAES) and somatosensory (Fourth somatosensory representation - SIV). To better understand the nature of cortical network hubs, the present report reviews the biological features of the AESc. Within the AESc, each area has extensive external cortical connections as well as among one another. Each of these core representations is separated by a transition zone characterized by bimodal neurons that share sensory properties of both adjoining core areas. Finally, core and transition zones are underlain by a continuous sheet of layer 5 neurons that project to common output structures. Altogether, these shared properties suggest that the collective AESc region represents a multiple sensory/multisensory cortical network hub. Ultimately, such an interconnected, composite structure adds complexity and biological detail to the understanding of cortical network hubs and their function in cortical processing