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1

Information routing, correspondence finding, and object recognition in the brain. Berlin: Springer-Verlag, 2010.

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2

Venkatesan, Ragav, and Baoxin Li. Convolutional Neural Networks in Visual Computing. Boca Raton ; London : Taylor & Francis, CRC Press, 2017.: CRC Press, 2017. http://dx.doi.org/10.4324/9781315154282.

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3

Rosandich, Ryan G. Intelligent visual inspection: Using artificial neural networks. London: Chapman & Hall, 1997.

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4

Rosandich, Ryan G. Intelligent Visual Inspection: Using artificial neural networks. Boston, MA: Springer US, 1996.

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5

Zhang, Xiang-Sun. Neural Networks in Optimization. Boston, MA: Springer US, 2000.

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6

Information theoretic neural computation. New Jersey: World Scientific, 2002.

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7

Govindaraju, R. S. Artificial Neural Networks in Hydrology. Dordrecht: Springer Netherlands, 2000.

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8

Kaynak, Okyay, Ethem Alpaydin, Erkki Oja, and Lei Xu, eds. Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2.

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9

T, Roska, ed. Cellular neural networks and visual computing: Foundation and applications. Cambridge: Cambridge University Press, 2002.

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10

T. V. S. M. olde Scheper. Chaos and information in dynamic neural networks. Oxford: Oxford Brookes University, 2002.

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11

Sheu, Bing Jay. Neural information processing and VLSI. Boston: Kluwer Academic Publishers, 1995.

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12

Deco, Gustavo. An information-theoretic approach to neural computing. New York: Springer, 1996.

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13

Deco, Gustavo. An information-theoretic approach to neural computing. New York: Springer, 1996.

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14

Keller, James. Fuzzy logic and neural networks for computer vision: Visual materials. Piscataway, NJ: Institute of Electrical and Electronics Engineers, 1992.

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15

Berenji, Hamid R. Fuzzy logic and neural networks for control systems: Visual materials. Piscataway, NJ: Institute of Electrical and Electronics Engineers, 1992.

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16

Benny, Lautrup, ed. Neural networks: Computers with intuition. Singapore: World Scientific, 1990.

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17

NATO Advanced Research Workshop on Information Processing of Chemical Sensory Stimuli in Biological and Artificial Systems (1989 Göttingen, Germany). Chemosensory information processing. Berlin: Springer-Verlag, 1989.

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18

Kuzʹmina, M. G. Oscillatory neural networks: In problems of parallel information processing. Berlin: De Gruyter, 2014.

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19

Deco, Gustavo. An Information-Theoretic Approach to Neural Computing. New York, NY: Springer New York, 1996.

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20

Lemmon, Michael. Competitively Inhibited Neural Networks for Adaptive Parameter Estimation. Boston, MA: Springer US, 1991.

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21

Koch, Christof. Biophysics of computation: Information processing in single neurons. New York: Oxford University Press, 1999.

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22

Biophysics of computation: Information processing in single neurons. New York: Oxford University Press, 1999.

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23

Stocker, Alan. Analog networks for the perception of visual motion. Hoboken NJ: John Wiley & Sons, 2006.

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24

Golès, E. Neural and Automata Networks: Dynamical Behavior and Applications. Dordrecht: Springer Netherlands, 1990.

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25

Shunʾichi, Amari, and Asian Pacific Neural Network Assembly., eds. Progress in neural information processing: Proceedings of the International Conference on Neural Information Processing, Hong Kong, 24-27 September 1996. Berlin: Springer, 1996.

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26

Engel, Andreas K. Zeitliche Kodierung in neuronalen Netzen: Evidenz für kohärente Aktivität im Sehsystem. Münster: Lit, 1996.

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27

Iatan, Iuliana F. Issues in the Use of Neural Networks in Information Retrieval. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-43871-9.

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28

Filo, Orna. Information processing by biochemical systems: Neural network-type configurations. Hoboken, N.J: John Wiley & Sons, 2010.

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29

Conference on Advances in Neural Information Processing Systems (20th 2006 Vancouver, B.C.). Advances in neural information processing systems 19: Proceedings of the 2006 conference. Cambridge, Mass: MIT, 2007.

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30

International Conference on Neural Information Processing (3rd 1996 Hong Kong). Progress in neural information processing: ICONIP'96 : proceedings of the International Conference on Neural Information Processing, Hong Kong, 24-27 September 1996. New York: Springer, 1996.

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31

Kong), International Conference on Neural Information Processing (3rd 1996 Hong. Progress in neural information processing: ICONIP'96 : proceedings of the International Conference on Neural Information Processing, Hong Kong, 24-27 September 1996. New York: Springer, 1996.

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32

Saalmann, Yuri B., and Sabine Kastner. Neural Mechanisms of Spatial Attention in the Visual Thalamus. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.013.

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Neural mechanisms of selective attention route behaviourally relevant information through brain networks for detailed processing. These attention mechanisms are classically viewed as being solely implemented in the cortex, relegating the thalamus to a passive relay of sensory information. However, this passive view of the thalamus is being revised in light of recent studies supporting an important role for the thalamus in selective attention. Evidence suggests that the first-order thalamic nucleus, the lateral geniculate nucleus, regulates the visual information transmitted from the retina to visual cortex, while the higher-order thalamic nucleus, the pulvinar, regulates information transmission between visual cortical areas, according to attentional demands. This chapter discusses how modulation of thalamic responses, switching the response mode of thalamic neurons, and changes in neural synchrony across thalamo-cortical networks contribute to selective attention.
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33

P, Mathur Bimal, Koch Christof 1956-, CREOL (Research center), and Society of Photo-optical Instrumentation Engineers., eds. Visual information processing, from neurons to chips: 1-2 April 1991, Orlando, Florida. Bellingham, Wash., USA: SPIE, 1991.

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34

Wolfrum, Philipp. Information Routing, Correspondence Finding, and Object Recognition in the Brain. Springer, 2014.

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35

Wolfrum, Philipp. Information Routing, Correspondence Finding, and Object Recognition in the Brain. Springer, 2011.

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36

Chua, Leon O., and Tamas Roska. Cellular Neural Networks & Visual Computing. Cambridge University Press, 2002.

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37

Fukushima, Kunihiko. Neural Networks and Information Processing. Addison-Wesley, 1992.

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38

Artificial Neural Networks. Springer, 2008.

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39

Luigi Mazzeo, Pier, Srinivasan Ramakrishnan, and Paolo Spagnolo, eds. Visual Object Tracking with Deep Neural Networks. IntechOpen, 2019. http://dx.doi.org/10.5772/intechopen.80142.

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40

Artificial Neural Networks. Springer, 2010.

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41

Taylor, J. G. Neural Networks (UNICOM - Information & Communications Technology). Nelson Thornes Ltd, 1998.

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42

S, Neelakanta Perambur, ed. Information-theoretic aspects of neural networks. Boca Raton, Fla: CRC Press, 1999.

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43

van Gerven, Marcel, and Sander Bohte, eds. Artificial Neural Networks as Models of Neural Information Processing. Frontiers Media SA, 2018. http://dx.doi.org/10.3389/978-2-88945-401-3.

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44

Neural Information Processing. Springer, 2010.

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45

Schild, Detlev. Chemosensory Information Processing ). Springer, 2011.

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46

Coolen, A. C. C., P. Sollich, and R. Kuhn. Theory of Neural Information Processing Systems. Oxford University Press, USA, 2005.

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47

Hsu, Hui-Huang. Neural networks for signal and information processing. 1994.

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48

(Editor), Todd K. Leen, Thomas G. Dietterich (Editor), and Volker Tresp (Editor), eds. Advances in Neural Information Processing Systems 13 (Neural Information Processing). The MIT Press, 2001.

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49

Holscher, Christian, and Matthias Munk. Information Processing by Neuronal Populations. University of Cambridge ESOL Examinations, 2012.

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50

Butz, Martin V., and Esther F. Kutter. Top-Down Predictions Determine Perceptions. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0009.

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While bottom-up visual processing is important, the brain integrates this information with top-down, generative expectations from very early on in the visual processing hierarchy. Indeed, our brain should not be viewed as a classification system, but rather as a generative system, which perceives something by integrating sensory evidence with the available, learned, predictive knowledge about that thing. The involved generative models continuously produce expectations over time, across space, and from abstracted encodings to more concrete encodings. Bayesian information processing is the key to understand how information integration must work computationally – at least in approximation – also in the brain. Bayesian networks in the form of graphical models allow the modularization of information and the factorization of interactions, which can strongly improve the efficiency of generative models. The resulting generative models essentially produce state estimations in the form of probability densities, which are very well-suited to integrate multiple sources of information, including top-down and bottom-up ones. A hierarchical neural visual processing architecture illustrates this point even further. Finally, some well-known visual illusions are shown and the perceptions are explained by means of generative, information integrating, perceptual processes, which in all cases combine top-down prior knowledge and expectations about objects and environments with the available, bottom-up visual information.
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