12
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Could the prior development of the retinotopic map account for the radial bias in the orientation map in V1?

      abstract
      1 , , 1
      BMC Neuroscience
      BioMed Central
      24th Annual Computational Neuroscience Meeting: CNS*2015
      18-23 July 2015

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The development of the retinotopic map is presumed to precede the development of the orientation map in V1 in primates. Experimental studies demonstrate a radial bias, wherein radial orientations produce higher activity compared to other orientations [1]. However most traditional models assume isometry in the orientation map developed, i.e. the orientation maps throughout V1 are similar in nature, independent of its retinotopy. In this paper, we propose an activity-dependent model which simulates the development of a radially biased orientation map. To that end we simulate the large-scale development of the retinotopic map, followed by the development of the orientation map in a sub-region of this map. The architecture consists of a Laterally Interconnected Synergetically Self Organizing Map (LISSOM) [2] with 2 layers, representing the retina, and the V1 respectively (see Figure 1A). At each time step, each neuron in V1, combines the afferent activation (ζr1,r2 ) along with its lateral excitations and inhibitions (ηkl ) from the previous time step. (1) η i j ( t ) = σ ( ∑ r 1 , r 2 ζ r 1 , r 2 μ i j , r 1 r 2 + γ E ∑ k , l E i j , k l η k l ( t - 1 ) - γ I ∑ k , l I i j , k l η k l ( t - 1 ) ) The afferent (μij,r1r2 ), lateral excitatory (Eij,kl ) and lateral inhibitory (Iij,kl ) weights adapt based on a normalized Hebbian mechanism. In order to develop the retinotopic map, the inputs to the retinal layer consists of centered (assumed to be the point of fixation) rectangular bars of varying dilations and rotations as modelled in [3]. The retinotopic map developed, biases the initial configuration of the orientation map (see Figure 1B) since all the bars given during the initial training are centered. For the subsequent refinement of the orientation map, Gaussians of differing orientation and positions (non-centered) are given as inputs to the retinal layer. After training for 4000 iterations (see Figure 1C,D), it is observed that the developed orientation map prefers those orientations which the retinotopy biases it towards, quantified by their corresponding histograms (See Figure 1E,F). As seen from the histogram the area occupied by the region mapping 1250-1500 is larger in the map developed assuming retinotopic bias, compared to that of the map developed assuming isotropy. Figure 1 (A) Schematic representation of the LISSOM architecture. (B) V1 orientation map developed after initial training to establish retinotopy along with its color map. (C) Orientation sub-maps, biased by initial retinotopy, at 1000, 2000, 3000, 4000 iterations. (D) Orientation sub-maps at 1000, 2000, 3000, 4000 iterations assuming isometry. (E) Histogram of the area covered by each of the orientations (color coded) corresponding to (C). (F) Histogram of the area covered by each of the orientations (color coded) corresponding to (D). Conclusions A neural activity based model for the development of radially biased orientation maps in V1 is demonstrated.

          Related collections

          Most cited references3

          • Record: found
          • Abstract: found
          • Article: not found

          The radial bias: a different slant on visual orientation sensitivity in human and nonhuman primates.

          It is generally assumed that sensitivity to different stimulus orientations is mapped in a globally equivalent fashion across primate visual cortex, at a spatial scale larger than that of orientation columns. However, some evidence predicts instead that radial orientations should produce higher activity than other orientations, throughout visual cortex. Here, this radial orientation bias was robustly confirmed using (1) human psychophysics, plus fMRI in (2) humans and (3) behaving monkeys. In visual cortex, fMRI activity was at least 20% higher in the retinotopic representations of polar angle which corresponded to the radial stimulus orientations (relative to tangential). In a global demonstration of this, we activated complementary retinotopic quadrants of visual cortex by simply changing stimulus orientation, without changing stimulus location in the visual field. This evidence reveals a neural link between orientation sensitivity and the cortical retinotopy, which have previously been considered independent.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Topographica: Building and Analyzing Map-Level Simulations from Python, C/C++, MATLAB, NEST, or NEURON Components

            Many neural regions are arranged into two-dimensional topographic maps, such as the retinotopic maps in mammalian visual cortex. Computational simulations have led to valuable insights about how cortical topography develops and functions, but further progress has been hindered by the lack of appropriate tools. It has been particularly difficult to bridge across levels of detail, because simulators are typically geared to a specific level, while interfacing between simulators has been a major technical challenge. In this paper, we show that the Python-based Topographica simulator makes it straightforward to build systems that cross levels of analysis, as well as providing a common framework for evaluating and comparing models implemented in other simulators. These results rely on the general-purpose abstractions around which Topographica is designed, along with the Python interfaces becoming available for many simulators. In particular, we present a detailed, general-purpose example of how to wrap an external spiking PyNN/NEST simulation as a Topographica component using only a dozen lines of Python code, making it possible to use any of the extensive input presentation, analysis, and plotting tools of Topographica. Additional examples show how to interface easily with models in other types of simulators. Researchers simulating topographic maps externally should consider using Topographica's analysis tools (such as preference map, receptive field, or tuning curve measurement) to compare results consistently, and for connecting models at different levels. This seamless interoperability will help neuroscientists and computational scientists to work together to understand how neurons in topographic maps organize and operate.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              The mapping of eccentricity and meridional angle onto orthogonal axes in the primary visual cortex: an activity-dependent developmental model

              Primate vision research has shown that in the retinotopic map of the primary visual cortex, eccentricity and meridional angle are mapped onto two orthogonal axes: whereas the eccentricity is mapped onto the nasotemporal axis, the meridional angle is mapped onto the dorsoventral axis. Theoretically such a map has been approximated by a complex log map. Neural models with correlational learning have explained the development of other visual maps like orientation maps and ocular-dominance maps. In this paper it is demonstrated that activity based mechanisms can drive a self-organizing map (SOM) into such a configuration that dilations and rotations of a particular image (in this case a rectangular bar) are mapped onto orthogonal axes. We further demonstrate using the Laterally Interconnected Synergetically Self Organizing Map (LISSOM) model, with an appropriate boundary and realistic initial conditions, that a retinotopic map which maps eccentricity and meridional angle to the horizontal and vertical axes respectively can be developed. This developed map bears a strong resemblance to the complex log map. We also simulated lesion studies which indicate that the lateral excitatory connections play a crucial role in development of the retinotopic map.
                Bookmark

                Author and article information

                Contributors
                Conference
                BMC Neurosci
                BMC Neurosci
                BMC Neuroscience
                BioMed Central
                1471-2202
                2015
                4 December 2015
                : 16
                : Suppl 1
                : P28
                Affiliations
                [1 ]Department of Biotechnology, Indian Institute of Technology Madras, Chennai, 600036, Tamil Nadu, India
                Article
                1471-2202-16-S1-P28
                10.1186/1471-2202-16-S1-P28
                4697593
                db254200-c8fe-4a78-95be-0b8d5e41ebb4
                Copyright © 2015 Philips and Chakravarthy

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                24th Annual Computational Neuroscience Meeting: CNS*2015
                Prague, Czech Republic
                18-23 July 2015
                History
                Categories
                Poster Presentation

                Neurosciences
                Neurosciences

                Comments

                Comment on this article