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BriCA: A Modular Software Platform for Whole Brain Architecture

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9947))

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Abstract

Brain-inspired Computing Architecture (BriCA) is a generic software platform for modular composition of machine learning algorithms. It can combine and schedule an arbitrary number of machine learning components in a brain-inspired fashion to construct higher level structures such as cognitive architectures. We would like to report and discuss the core concepts of BriCA version 1 and prospects toward future development.

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References

  1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015), http://tensorflow.org/, software available from tensorflow.org

  2. Einhorn, E., Langner, T., Stricker, R., Martin, C., Gross, H.M.: Mira - middleware for robotic applications. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2591–2598, October 2012

    Google Scholar 

  3. Ghosh, S., Matsuoka, Y., Asai, Y., Hsin, K.Y., Kitano, H.: Software for systems biology: from tools to integrated platforms. Nat. Rev. Genet. 12(12), 821–832 (2011). http://www.ncbi.nlm.nih.gov/pubmed/22048662

    Google Scholar 

  4. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009). http://doi.acm.org/10.1145/1656274.1656278

    Article  Google Scholar 

  5. Haruno, M., Wolpert, D.M., Kawato, M.: Mosaic model for sensorimotor learning and control. Neural Comput. 13(10), 2201–2220 (2001). http://www.ncbi.nlm.nih.gov/pubmed/11570996

    Article  MATH  Google Scholar 

  6. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006). http://science.sciencemag.org/content/313/5786/504

    Article  MathSciNet  MATH  Google Scholar 

  7. Karpathy, A., Li, F.: Deep visual-semantic alignments for generating image descriptions. CoRR abs/1412.2306 (2014). http://arxiv.org/abs/1412.2306

  8. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). http://dx.doi.org/10.1038/nature14539

    Article  Google Scholar 

  9. Lecun, Y., Cortes, C.: The MNIST database of handwritten digits. http://yann.lecun.com/exdb/mnist/

  10. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015). http://dx.doi.org/10.1038/nature14236

    Article  Google Scholar 

  11. Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3, p. 5

    Google Scholar 

  12. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Enber, D., Chaudhary, V., Young, M.: Machine learning: the high interest credit card of technical debt. In: SE4ML: Software Engineering for Machine Learning (NIPS Workshop) (2014)

    Google Scholar 

  13. Takahashi, K., Kaizu, K., Hu, B., Tomita, M.: A multi-algorithm, multi-timescale method for cell simulation. Bioinformatics 20(4), 538–546 (2004). http://www.ncbi.nlm.nih.gov/pubmed/14990450

    Article  Google Scholar 

  14. Takahashi, K., Itaya, K., Nakamura, M., Koizumi, M., Arakawa, N., Tomita, M., Yamakawa, H.: A generic software platform for brain-inspired cognitive computing. Procedia Comput. Sci. 71, 31–37 (2015). http://www.sciencedirect.com/science/article/pii/S1877050915036467, 6th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2015, 6–8 November Lyon, France

    Article  Google Scholar 

  15. Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. CoRR abs/1411.4555 (2014). http://arxiv.org/abs/1411.4555

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Acknowledgments

We would like to thank Yuji Ichisugi, Makoto Taiji, Shinji Nishimoto, Hidemoto Nakada, and the members of the Whole Brain Architecture Initiative, especially Ryutaro Ichise, Takashi Omori, Hideki Kashioka, Satoshi Kurihara, Takeshi Sakurada, Takeshi Sato, and Yutaka Matsuo, along with the Whole Brain Architecture Future Leaders for their support, comments, and discussion. This research was supported in part by funds from Yamagata Prefectural Government and Tsuruoka City.

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Correspondence to Koichi Takahashi .

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Itaya, K. et al. (2016). BriCA: A Modular Software Platform for Whole Brain Architecture. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_37

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  • DOI: https://doi.org/10.1007/978-3-319-46687-3_37

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