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International Conference on Artificial Neural Networks

ICANN 2016: Artificial Neural Networks and Machine Learning – ICANN 2016 pp 97–104Cite as

A Convolutional Network Model of the Primate Middle Temporal Area

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9887))

Abstract

Convolutional neural networks have many parallels with the primate visual cortex, including deep structures with sparse retinotopic connections, and feature maps with increasing specificity and invariance along feedforward paths. The present study explores the possibility of specifically training convolutional networks to resemble the primate cortex more closely. In particular, in addition to supervised learning to minimize an output error function, a deep layer is directly trained to approximate primate electrophysiology data. This method is used to develop a model of the macaque monkey dorsal stream that estimates heading and speed from visual input.

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References

  1. Felleman, D.J., Van Essen, D.C.: Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991)

    Article  Google Scholar 

  2. Wandell, B.A., Winawer, J.: Imaging retinotopic maps in the human brain. Vis. Res. 51(7), 718–737 (2011)

    Article  Google Scholar 

  3. Krüger, N., Janssen, P., Kalkan, S., Lappe, M., Leonardis, A., Piater, J., Rodríguez-Sánchez, A.J., Wiskott, L.: Deep hierarchies in the primate visual cortex: what can we learn for computer vision? IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1847–1871 (2013)

    Article  Google Scholar 

  4. Goodale, M.A., Milner, A.D.: Separate visual pathways for perception and action. Trends Neurosci. 15(1), 20–25 (1992)

    Article  Google Scholar 

  5. Tanaka, K.: Inferotemporal cortex and object vision. Ann. Rev. Neurosci. 19, 109–139 (1996)

    Article  Google Scholar 

  6. Ungerleider, L.G., Bell, A.H.: Uncovering the visual “alphabet”: advances in our understanding of object perception. Vis. Res. 51(7), 782–799 (2011)

    Article  Google Scholar 

  7. O’Neil, E.B., Protzner, A.B., McCormick, C., McLean, D.A., Poppenk, J., Cate, A.D., Köhler, S.: Distinct patterns of functional and effective connectivity between perirhinal cortex and other cortical regions in recognition memory and perceptual discrimination. Cereb. Cortex 22(1), 74–85 (2012)

    Article  Google Scholar 

  8. Borra, E., Belmalih, A., Calzavara, R., Gerbella, M., Murata, A., Rozzi, S., Luppino, G.: Cortical connections of the macaque anterior intraparietal (AIP) area. Cereb. Cortex 18(5), 1094–1111 (2008)

    Article  Google Scholar 

  9. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Handwritten digit recognition with a back-propagation network. In: NIPS 1989, pp. 396–404 (1990)

    Google Scholar 

  10. Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)

    Article  MATH  Google Scholar 

  11. Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust object recognition with cortex-like mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 411–426 (2007)

    Article  Google Scholar 

  12. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS 2012, pp. 1–9 (2012)

    Google Scholar 

  13. Yamins, D.L.K., Hong, H., Cadieu, C.F., Solomon, E.A., Seibert, D., Dicarlo, J.J.: Performance-optimized hierarchical models predict neural responses in higher visual cortex. PNAS 111(23), 8619–8624 (2014)

    Article  Google Scholar 

  14. Nover, H., Anderson, C.H., DeAngelis, G.C.: A logarithmic, scale-invariant representation of speed in macaque middle temporal area accounts for speed discrimination performance. J. Neurosci. 25(43), 10049–10060 (2005)

    Article  Google Scholar 

  15. DeAngelis, G.C., Uka, T.: Coding of horizontal disparity and velocity by MT neurons in the alert macaque. J. Neurophysiol. 89(2), 1094–1111 (2003)

    Article  Google Scholar 

  16. Pack, C.C., Born, R.T.: Temporal dynamics of a neural solution to the aperture problem in visual area MT of macaque brain. Nature 409(6823), 1040–1042 (2001)

    Article  Google Scholar 

  17. Hubel, D.H., Wiesel, T.N.: Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148, 574–591 (1959)

    Article  Google Scholar 

  18. Markov, N.T., Ercsey-Ravasz, M.M., Ribeiro Gomes, A.R., Lamy, C., Magrou, L., Vezoli, J., Misery, P., Falchier, A., Quilodran, R., Gariel, M.A., Sallet, J., Gamanut, R., Huissoud, C., Clavagnier, S., Giroud, P., Sappey-Marinier, D., Barone, P., Dehay, C., Toroczkai, Z., Knoblauch, K., Van Essen, D.C., Kennedy, H.: A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cereb. Cortex 24(1), 17–36 (2014)

    Article  Google Scholar 

  19. Stevenson, I.H., Kording, K.P.: How advances in neural recording affect data analysis. Nat. Neurosci. 14(2), 139–142 (2011)

    Article  Google Scholar 

  20. Chollet, F.: Keras (2015). https://github.com/fchollet/keras

  21. Bergstra, J., Bastien, F., Breuleux, O., Lamblin, P., Pascanu, R., Delalleau, O., Desjardins, G., Warde-Farley, D., Goodfellow, I., Bergeron, A., Bengio, Y.: Theano: deep learning on GPUs with Python. In: NIPS 2011 BigLearning Workshop, pp. 1–4 (2011)

    Google Scholar 

  22. Adelson, E.H., Bergen, J.R.: Spatiotemporal energy models for the perception of motion. J. Opt. Soci. Am. A, Opt. Image Sci. 2(2), 284–299 (1985)

    Article  Google Scholar 

  23. Britten, K.H., Shadlen, M.N., Newsome, W.T., Movshon, J.A.: The analysis of visual motion: a comparison of neuronal and psychophysical performance. J. Neurosci. 12(12), 4745–4765 (1992)

    Google Scholar 

  24. Cook, E.P., Maunsell, J.H.R.: Dynamics of neuronal responses in macaque MT and VIP during motion detection. Nat. Neurosci. 5(10), 985–994 (2002)

    Article  Google Scholar 

  25. Nishimoto, S., Gallant, L.: A three-dimensional spatiotemporal receptive field model explains responses of area MT neurons to naturalistic movies. J. Neurosci. 31(41), 14551–14564 (2011)

    Article  Google Scholar 

  26. Kingma, D., Ba, J.: Adam: A Method for Stochastic Optimization. arxiv:1412.6980 [cs], pp. 1–15 (2014)

  27. Carandini, M., Heeger, D.J.: Normalization as a canonical neural computation. Nat. Rev. Neurosci. 13, 51–62 (2011)

    Google Scholar 

  28. Arai, K., Keller, E.L., Edelman, J.A.: Two-dimensional neural network model of the primate saccadic system. Neural Netw. 7(6–7), 1115–1135 (1994)

    Article  MATH  Google Scholar 

  29. Lehky, S.R., Kiani, R., Esteky, H., Tanaka, K.: Statistics of visual responses in primate inferotemporal cortex to object stimuli. J. Neurophysiol. 106(3), 1097–1117 (2011)

    Article  Google Scholar 

  30. Schaffelhofer, S., Scherberger, H.: From vision to action: a comparative population study of hand grasping areas AIP, F5, and M1. In: Bernstein Conference 2014 (2014)

    Google Scholar 

  31. Rubin, D.B., Van Hooser, S.D., Miller, K.D.: The stabilized supralinear network: a unifying circuit motif underlying multi-input integration in sensory cortex. Neuron 85(1), 1–51 (2015)

    Article  Google Scholar 

  32. Eliasmith, C.: A unified approach to building and controlling spiking attractor networks. Neural Comput. 17(6), 1276–1314 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  33. Pinheiro, P.H.O., Collobert, R.: Recurrent Convolutional Neural Networks for Scene Parsing, June 2013

    Google Scholar 

  34. Liu, F., Lin, G., Shen, C.: CRF learning with CNN features for image segmentation. Pattern Recogn. 48(10), 2983–2992 (2015)

    Article  Google Scholar 

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Acknowledgments

This work was supported by a Discovery Grant from the Natural Sciences and Engineering Reseach Council of Canada.

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Authors and Affiliations

  1. University of Waterloo, Waterloo, Canada

    Bryan P. Tripp

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  1. Bryan P. Tripp
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Correspondence to Bryan P. Tripp .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

  2. University of Lausanne, Lausanne, Switzerland

    Paolo Masulli

  3. Universitat Politécnica de Catalunya, Terrrassa, Spain

    Antonio Javier Pons Rivero

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Tripp, B.P. (2016). A Convolutional Network Model of the Primate Middle Temporal Area. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-44781-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44780-3

  • Online ISBN: 978-3-319-44781-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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