Relevant bibliographies by topics / Primary visual cortex modeling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Primary visual cortex modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Primary visual cortex modeling"

1

Rangan, A., L. Tao, G. Kovacic, and D. Cai. "Multiscale modeling of the primary visual cortex." IEEE Engineering in Medicine and Biology Magazine 28, no. 3 (2009): 19–24. http://dx.doi.org/10.1109/memb.2009.932803.

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Huang, Wentao, Licheng Jiao, and Jianhua Jia. "Modeling contextual modulation in the primary visual cortex." Neural Networks 21, no. 8 (2008): 1182–96. http://dx.doi.org/10.1016/j.neunet.2008.06.001.

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Yang, Qian, Xianglin Qi, and Wang Yunjiu. "Modeling neuronal dynamic coding in primary visual cortex." Biosystems 58, no. 1-3 (2000): 203–9. http://dx.doi.org/10.1016/s0303-2647(00)00124-6.

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Shao, Feng, Wanting Chen, Gangyi Jiang, and Yo-Sung Ho. "Modeling the Perceptual Quality of Stereoscopic Images in the Primary Visual Cortex." IEEE Access 5 (2017): 15706–16. http://dx.doi.org/10.1109/access.2017.2733161.

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Burg, Max F., Santiago A. Cadena, George H. Denfield, et al. "Learning divisive normalization in primary visual cortex." PLOS Computational Biology 17, no. 6 (2021): e1009028. http://dx.doi.org/10.1371/journal.pcbi.1009028.

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Divisive normalization (DN) is a prominent computational building block in the brain that has been proposed as a canonical cortical operation. Numerous experimental studies have verified its importance for capturing nonlinear neural response properties to simple, artificial stimuli, and computational studies suggest that DN is also an important component for processing natural stimuli. However, we lack quantitative models of DN that are directly informed by measurements of spiking responses in the brain and applicable to arbitrary stimuli. Here, we propose a DN model that is applicable to arbi
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Nurminen, Lauri, Markku Kilpeläinen, Pentti Laurinen, and Simo Vanni. "Area Summation in Human Visual System: Psychophysics, fMRI, and Modeling." Journal of Neurophysiology 102, no. 5 (2009): 2900–2909. http://dx.doi.org/10.1152/jn.00201.2009.

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Contextual modulation is a fundamental feature of sensory processing, both on perceptual and on single-neuron level. When the diameter of a visual stimulus is increased, the firing rate of a cell typically first increases (summation field) and then decreases (surround field). Such an area summation function draws a comprehensive profile of the receptive field structure of a neuron, including areas outside the classical receptive field. We investigated area summation in human vision with psychophysics and functional magnetic resonance imaging (fMRI). The stimuli were similar to those used drift
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Abolpour, Nahid, Reza Boostani, Mohammad-Ali Masnadi-Shirazi, Bahman Tahayori, and Ali Almasi. "A CHAOTIC MULTILAYER LIF SCHEME TO MODEL THE PRIMARY VISUAL CORTEX." Biomedical Engineering: Applications, Basis and Communications 33, no. 04 (2021): 2150030. http://dx.doi.org/10.4015/s1016237221500307.

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Precise mathematical modeling of the primary visual cortex (V1) is still a challenging problem. Due to the high similarity of visual system of cat and human, in this paper, we present a hybrid model to track the electrical responses of neurons that are measured by a multi-electrode array implanted in cat V1. The proposed model combines a stochastic phenomenological model with a multilayer leaky integrate-and-fire (LIF) model to predict V1 responses. Since all the existing visual cortex models do not capture the stochastic properties of synaptic changes, the proposed phenomenological model prov
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McLaughlin, David, Robert Shapley, and Michael Shelley. "Large-scale modeling of the primary visual cortex: influence of cortical architecture upon neuronal response." Journal of Physiology-Paris 97, no. 2-3 (2003): 237–52. http://dx.doi.org/10.1016/j.jphysparis.2003.09.019.

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Rangan, A. V., D. Cai, and D. W. McLaughlin. "Modeling the spatiotemporal cortical activity associated with the line-motion illusion in primary visual cortex." Proceedings of the National Academy of Sciences 102, no. 52 (2005): 18793–800. http://dx.doi.org/10.1073/pnas.0509481102.

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Cronin, Beau, Ian H. Stevenson, Mriganka Sur, and Konrad P. Körding. "Hierarchical Bayesian Modeling and Markov Chain Monte Carlo Sampling for Tuning-Curve Analysis." Journal of Neurophysiology 103, no. 1 (2010): 591–602. http://dx.doi.org/10.1152/jn.00379.2009.

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A central theme of systems neuroscience is to characterize the tuning of neural responses to sensory stimuli or the production of movement. Statistically, we often want to estimate the parameters of the tuning curve, such as preferred direction, as well as the associated degree of uncertainty, characterized by error bars. Here we present a new sampling-based, Bayesian method that allows the estimation of tuning-curve parameters, the estimation of error bars, and hypothesis testing. This method also provides a useful way of visualizing which tuning curves are compatible with the recorded data.
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Dissertations / Theses on the topic "Primary visual cortex modeling"

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Law, Judith S. "Modeling the development of organization for orientation preference in primary visual cortex." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3935.

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The cerebral cortex of mammals comprises a series of topographic maps, forming sensory and motor areas such as those in the visual, auditory, and somatosensory systems. Understanding the rules that govern the development of these maps and how this topographic organization relates to information processing is critical for the understanding of cortical processing and whole brain function. Previous computational models have shown that topographic maps can develop through a process of self-organization, if spatially localized patches of cortical neurons are activated by particular stimuli. This th
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Ball, Christopher Edward. "Modeling the emergence of perceptual color space in the primary visual cortex." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/11694.

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Humans’ perceptual experience of color is very different from what one might expect, given the light reaching the eye. Identical patterns of light are often perceived as different colors, and different patterns of light are often perceived as the same color. Even more strikingly, our perceptual experience is that hues are arranged circularly (with red similar to violet), even though single-wavelength lights giving rise to perceptions of red and violet are at opposite ends of the wavelength spectrum. The goal of this thesis is to understand how perceptual color space arises in the brain, focusi
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Vieira, Diogo Porfirio de Castro. "Análises de estabilidade e de sensibilidade de modelos biologicamente plausíveis do córtex visual primário." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-18032009-163830/.

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A neurociência computacional é uma vasta área que tem como objeto de estudo o entendimento ou a emulação da dinâmica cerebral em diversos níveis. Neste trabalho atenta-se ao estudo da dinâmica de neurônios, os quais, no consenso atual, acredita-se serem as unidades fundamentais do processamento cerebral. A importância do estudo sobre o comportamento de neurônios se encontra na diversidade de propriedades que eles podem apresentar. O estudo se torna mais rico quando há interações de sistemas internos ao neurônio em diferentes escalas de tempo, criando propriedades como adaptação, latência e com
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da, Silva Gomes Joao Paulo. "Brain inspired approach to computational face recognition." Thesis, University of Plymouth, 2015. http://hdl.handle.net/10026.1/3544.

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Face recognition that is invariant to pose and illumination is a problem solved effortlessly by the human brain, but the computational details that underlie such efficient recognition are still far from clear. This thesis draws on research from psychology and neuroscience about face and object recognition and the visual system in order to develop a novel computational method for face detection, feature selection and representation, and memory structure for recall. A biologically plausible framework for developing a face recognition system will be presented. This framework can be divided into f
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Schwedhelm, Philipp [Verfasser], Stefan [Akademischer Betreuer] Treue, Hansjörg [Gutachter] Scherberger, and Melanie [Gutachter] Wilke. "Feature-based attention in primate visual cortex : Mechanisms and limitations of color- and motionselection as assessed by neurophysiology, psychophysics and computational modeling / Philipp Schwedhelm. Betreuer: Stefan, Treue. Gutachter: Hansjörg, Scherberger ; Melanie, Wilke." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2016. http://d-nb.info/1105760162/34.

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Fotheringhame, David K. "Temporal coding in primary visual cortex." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339357.

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Nauhaus, Ian Michael. "Functional connectivity in primary visual cortex." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1692099811&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Thulin, Nilsson Linnea. "The Role of Primary Visual Cortex in Visual Awareness." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11623.

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Despite its great complexity, a great deal is known about the organization and information-processing properties of the visual system. However, the neural correlates of visual awareness are not yet understood. By studying patients with blindsight, the primary visual cortex (V1) has attracted a lot of attention recently. Although this brain area appears to be important for visual awareness, its exact role is still a matter of debate. Interactive models propose a direct role for V1 in generating visual awareness through recurrent processing. Hierarchal models instead propose that awareness is ge
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Krug, Kristine. "Ordering geniculate input into primary visual cortex." Thesis, University of Oxford, 1997. https://ora.ox.ac.uk/objects/uuid:b342ffae-4a31-4171-94a6-83cb516e83fe.

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Precise point-to-point connectivity is the basis of ordered maps of the visual field in the brain. One point in the visual field is represented at one locus in the dLGN and one locus in primary visual cortex. A fundamental problem in the development of most sensory systems is the creation of the topographic projections which underlie these maps. Mechanisms ranging from ordered ingrowth of fibres, through chemical guidance of axons to sculpting of the map from an early exuberant input have been proposed. However, we know little about how ordered maps are created beyond the first relay. What we
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Rudiger, Philipp John Frederic. "Development and encoding of visual statistics in the primary visual cortex." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/25469.

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How do circuits in the mammalian cerebral cortex encode properties of the sensory environment in a way that can drive adaptive behavior? This question is fundamental to neuroscience, but it has been very difficult to approach directly. Various computational and theoretical models can explain a wide range of phenomena observed in the primary visual cortex (V1), including the anatomical organization of its circuits, the development of functional properties like orientation tuning, and behavioral effects like surround modulation. However, so far no model has been able to bridge these levels of de
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Books on the topic "Primary visual cortex modeling"

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1929-, Peters Alan, ed. The cat primary visual cortex. Academic Press, 2002.

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Peters, Alan, and Kathleen S. Rockland, eds. Primary Visual Cortex in Primates. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4757-9628-5.

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Suner, Ivan Jose. Influences of the lateral geniculate nucleus in the specification of primary visual cortex in macaca mulatta. s.n.], 1992.

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service), SpringerLink (Online, ed. Circuits in the Brain: A Model of Shape Processing in the Primary Visual Cortex. Springer-Verlag New York, 2009.

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Neural computation of pattern motion: Modeling stages of motion analysis in the primate visual cortex. MIT Press, 1993.

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(Editor), Bertram Payne, and Alan Peters (Editor), eds. The Cat Primary Visual Cortex. Academic Press, 2001.

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1929-, Peters Alan, and Rockland, Kathleen Linda Skiba, 1947-, eds. Primary visual cortex in primates. Plenum Press, 1994.

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The Cat Primary Visual Cortex. Elsevier, 2002. http://dx.doi.org/10.1016/b978-0-12-552104-8.x5000-7.

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Peters, Alan. Cerebral Cortex: Volume 10 Primary Visual Cortex In Primates. Springer, 2013.

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(Editor), Alan Peters, and Kathleen S. Rockland (Editor), eds. Primary Visual Cortex in Primates (Cerebral Cortex) VOL. 10. Springer, 1994.

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Book chapters on the topic "Primary visual cortex modeling"

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Rangan, Aaditya V., Louis Tao, Gregor Kovačič, and David Cai. "Large-Scale Computational Modeling of the Primary Visual Cortex." In Coherent Behavior in Neuronal Networks. Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0389-1_14.

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Marshall, Jonathan A., and George J. Kalarickal. "Modeling Dynamic Receptive Field Changes in Primary Visual Cortex Using Inhibitory Learning." In Computational Neuroscience. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-9800-5_64.

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Kuljis, Rodrigo O. "The Human Primary Visual Cortex." In Cerebral Cortex. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4757-9628-5_12.

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Skalicky, Simon E. "The Primary Visual Cortex." In Ocular and Visual Physiology. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-846-5_14.

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Thomson, J. R., Wm Cowan, K. R. Elder, et al. "Modelling Pattern Formation on Primate Visual Cortex." In Springer Series in Synergetics. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79290-8_6.

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Peters, Alan. "Number of Neurons and Synapses in Primary Visual Cortex." In Cerebral Cortex. Springer US, 1987. http://dx.doi.org/10.1007/978-1-4615-6616-8_7.

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Peters, Alan. "The Organization of the Primary Visual Cortex in the Macaque." In Cerebral Cortex. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4757-9628-5_1.

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Wörgötter, Florentin. "Comparing Different Modeling Approaches of Visual Cortical Cell Characteristics." In Cerebral Cortex. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4903-1_4.

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Casagrande, Vivien A., and Jon H. Kaas. "The Afferent, Intrinsic, and Efferent Connections of Primary Visual Cortex in Primates." In Cerebral Cortex. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4757-9628-5_5.

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Conway, Bevil R. "Segregated processing streams in primary visual cortex." In neural mechanisms of Color Vision. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-5953-2_4.

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Conference papers on the topic "Primary visual cortex modeling"

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Silver, Michael A. "Modeling the time course of attention signals in human primary visual cortex." In Electronic Imaging 2006, edited by Bernice E. Rogowitz, Thrasyvoulos N. Pappas, and Scott J. Daly. SPIE, 2006. http://dx.doi.org/10.1117/12.674149.

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Farkas, I. "Modeling the self-organization of directional selectivity in the primary visual cortex." In 9th International Conference on Artificial Neural Networks: ICANN '99. IEE, 1999. http://dx.doi.org/10.1049/cp:19991117.

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Günthner, Max F., Santiago A. Cadena, George H. Denfield, et al. "Learning Divisive Normalization in Primary Visual Cortex." In 2019 Conference on Cognitive Computational Neuroscience. Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1211-0.

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Tran, Thi Diem, Mutsumi Kimura, and Yasuhiko Nakashima. "Primary Visual Cortex Inspired Feature Extraction Hardware Model." In 2020 4th International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom). IEEE, 2020. http://dx.doi.org/10.1109/sigtelcom49868.2020.9199057.

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Goto, Yoshinobu, Takao Yamasaki, and Shozo Tobimatsu. "Innovation for visual stimuli: From the retina to primary visual cortex." In 2010 IEEE/ICME International Conference on Complex Medical Engineering - CME 2010. IEEE, 2010. http://dx.doi.org/10.1109/iccme.2010.5558856.

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Bouganis, Christos-savvas, Peter K. Cheung, and Li Zhaoping. "FPGA-Accelerated Pre-Attentive Segmentation in Primary Visual Cortex." In 2006 International Conference on Field Programmable Logic and Applications. IEEE, 2006. http://dx.doi.org/10.1109/fpl.2006.311214.

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Wang, Kuansan, and Shihab A. Shamma. "Modeling the auditory functions in the primary cortex." In SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, edited by Harold H. Szu. SPIE, 1994. http://dx.doi.org/10.1117/12.170068.

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Daniela, Coman Andreea, Ionita Silviu, and Lita Ioan. "A Neuronal Model of the Primary Visual Cortex: Simulation of Visual Evoked Potentials." In 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2021. http://dx.doi.org/10.1109/ecai52376.2021.9515133.

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Rajagopalan, Uma M., Hideyuki Takaoka, Ryota Homma, Hirofumi Kadono, and Manabu Tanifuji. "Functional imaging of cat primary visual cortex with optical coherence tomography." In International Symposium on Biomedical Optics, edited by Valery V. Tuchin, Joseph A. Izatt, and James G. Fujimoto. SPIE, 2002. http://dx.doi.org/10.1117/12.470474.

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Wang, Yi, Ke Chen, and Leanne L. H. Chan. "Information transmission in the primary visual cortex of retinal degenerated rats." In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2017. http://dx.doi.org/10.1109/embc.2017.8037644.

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