1,423 research outputs found

    Development of Maps of Simple and Complex Cells in the Primary Visual Cortex

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    Hubel and Wiesel (1962) classified primary visual cortex (V1) neurons as either simple, with responses modulated by the spatial phase of a sine grating, or complex, i.e., largely phase invariant. Much progress has been made in understanding how simple-cells develop, and there are now detailed computational models establishing how they can form topographic maps ordered by orientation preference. There are also models of how complex cells can develop using outputs from simple cells with different phase preferences, but no model of how a topographic orientation map of complex cells could be formed based on the actual connectivity patterns found in V1. Addressing this question is important, because the majority of existing developmental models of simple-cell maps group neurons selective to similar spatial phases together, which is contrary to experimental evidence, and makes it difficult to construct complex cells. Overcoming this limitation is not trivial, because mechanisms responsible for map development drive receptive fields (RF) of nearby neurons to be highly correlated, while co-oriented RFs of opposite phases are anti-correlated. In this work, we model V1 as two topographically organized sheets representing cortical layer 4 and 2/3. Only layer 4 receives direct thalamic input. Both sheets are connected with narrow feed-forward and feedback connectivity. Only layer 2/3 contains strong long-range lateral connectivity, in line with current anatomical findings. Initially all weights in the model are random, and each is modified via a Hebbian learning rule. The model develops smooth, matching, orientation preference maps in both sheets. Layer 4 units become simple cells, with phase preference arranged randomly, while those in layer 2/3 are primarily complex cells. To our knowledge this model is the first explaining how simple cells can develop with random phase preference, and how maps of complex cells can develop, using only realistic patterns of connectivity

    Visuospatial coding as ubiquitous scaffolding for human cognition

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    For more than 100 years we have known that the visual field is mapped onto the surface of visual cortex, imposing an inherently spatial reference frame on visual information processing. Recent studies highlight visuospatial coding not only throughout visual cortex, but also brain areas not typically considered visual. Such widespread access to visuospatial coding raises important questions about its role in wider cognitive functioning. Here, we synthesise these recent developments and propose that visuospatial coding scaffolds human cognition by providing a reference frame through which neural computations interface with environmental statistics and task demands via perception–action loops

    Establishment of a Scaffold for Orientation Maps in Primary Visual Cortex of Higher Mammals

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    From whole-brain data to functional circuit models: the zebrafish optomotor response

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    Detailed descriptions of brain-scale sensorimotor circuits underlying vertebrate behavior remain elusive. Recent advances in zebrafish neuroscience offer new opportunities to dissect such circuits via whole-brain imaging, behavioral analysis, functional perturbations, and network modeling. Here, we harness these tools to generate a brain-scale circuit model of the optomotor response, an orienting behavior evoked by visual motion. We show that such motion is processed by diverse neural response types distributed across multiple brain regions. To transform sensory input into action, these regions sequentially integrate eye- and direction-specific sensory streams, refine representations via interhemispheric inhibition, and demix locomotor instructions to independently drive turning and forward swimming. While experiments revealed many neural response types throughout the brain, modeling identified the dimensions of functional connectivity most critical for the behavior. We thus reveal how distributed neurons collaborate to generate behavior and illustrate a paradigm for distilling functional circuit models from whole-brain data

    Development of orientation preference maps in ferret visual cortex

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    Coordinated optimization of visual cortical maps (II) Numerical studies

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    It is an attractive hypothesis that the spatial structure of visual cortical architecture can be explained by the coordinated optimization of multiple visual cortical maps representing orientation preference (OP), ocular dominance (OD), spatial frequency, or direction preference. In part (I) of this study we defined a class of analytically tractable coordinated optimization models and solved representative examples in which a spatially complex organization of the orientation preference map is induced by inter-map interactions. We found that attractor solutions near symmetry breaking threshold predict a highly ordered map layout and require a substantial OD bias for OP pinwheel stabilization. Here we examine in numerical simulations whether such models exhibit biologically more realistic spatially irregular solutions at a finite distance from threshold and when transients towards attractor states are considered. We also examine whether model behavior qualitatively changes when the spatial periodicities of the two maps are detuned and when considering more than 2 feature dimensions. Our numerical results support the view that neither minimal energy states nor intermediate transient states of our coordinated optimization models successfully explain the spatially irregular architecture of the visual cortex. We discuss several alternative scenarios and additional factors that may improve the agreement between model solutions and biological observations.Comment: 55 pages, 11 figures. arXiv admin note: substantial text overlap with arXiv:1102.335

    Hebbian Learning of the Statistical and Geometrical Structure of Visual Input

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    Intrinsic structural disorder in cytoskeletal proteins.

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    Cytoskeleton, the internal scaffold of the cell, displays an exceptional combination of stability and dynamics. It is composed of three major filamentous networks, microfilaments (actin filaments), intermediate filaments (neurofilaments), and microtubules. Together, they ensure the physical and structural stability of the cell, whereby also mediating its large-scale structural rearrangements, motility, stress response, division, and internal transport. All three cytoskeletal systems are built upon the same basic design: they have a central repetitive scaffold assembled from folded building elements, surrounded and regulated by accessory regions/proteins that regulate its formation and mediate its countless interactions with its environment, serving to send regulatory signals to and from the cytoskeleton. Here, we elaborate on the idea that the opposing features of stability and dynamics are also manifest in the dichotomy of the structural status of its components, the core being highly structured and the accessory proteins/regions being highly disordered, and are responsible for most of the regulatory (post-translational) input promoting adaptive responses and providing dynamics necessary for each of the cytoskeletal systems. This pattern entails special consequences, in which the manifold functional advantages of structural disorder, most pronounced in regulatory and signaling functions, are all exploited by nature. (c) 2013 Wiley Periodicals, Inc
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