publication . Preprint . 2016

BioSpaun: A large-scale behaving brain model with complex neurons

Eliasmith, Chris; Gosmann, Jan; Choo, Xuan;
Open Access English
  • Published: 16 Feb 2016
Abstract
Comment: 17 pages 7 figures
Subjects
free text keywords: Quantitative Biology - Neurons and Cognition, Computer Science - Artificial Intelligence
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26 references, page 1 of 2

[1] C. Eliasmith, T. C. Stewart, X. Choo, T. Bekolay, T. DeWolf, Y. Tang, D. Rasmussen, A large-scale model of the functioning brain, Science 338 (2012) 1202{1205. [OpenAIRE]

[2] H. Markram, E. Muller, S. Ramaswamy, M. Reimann, M. Abdellah, C. Sanchez, A. Ailamaki, L. Alonso-Nanclares, N. Antille, S. Arsever, G. Kahou, T. Berger, A. Bilgili, N. Buncic, A. Chalimourda, G. Chindemi, J.-D. Courcol, F. Delalondre, V. Delattre, S. Druckmann, R. Dumusc, J. Dynes, S. Eilemann, E. Gal, M. Gevaert, J.-P. Ghobril, A. Gidon, J. Graham, A. Gupta, V. Haenel, E. Hay, T. Heinis, J. Hernando, M. Hines, L. Kanari, D. Keller, J. Kenyon, G. Khazen, Y. Kim, J. King, Z. Kisvarday, P. Kumbhar, S. Lasserre, J.-V. LeBe, B. Magalha~es, A. Merchan-Perez, J. Meystre, B. Morrice, J. Muller, A. Mun~ozCespedes, S. Muralidhar, K. Muthurasa, D. Nachbaur, T. Newton, M. Nolte, A. Ovcharenko, J. Palacios, L. Pastor, R. Perin, R. Ranjan, I. Riachi, J.-R. Rodr guez, J. Riquelme, C. Rossert, K. Sfyrakis, Y. Shi, J. Shillcock, G. Silberberg, R. Silva, F. Tauheed, M. Telefont, M. Toledo-Rodriguez, T. Trankler, W. VanGeit, J. D az, R. Walker, Y. Wang, S. Zaninetta, J. DeFelipe, S. Hill, I. Segev, F. Schurmann, Reconstruction and Simulation of Neocortical Microcircuitry, Cell 163 (2015) 456{492.

[3] T. Wong, R. Preissl, P. Datta, F. Myron, R. Singh, S. Esser, E. McQuinn, R. Appuswamy, W. Risk, H. Simon, D. Modha, 10^14, IBM Research Report: RJ10502(ALM1211-004) (2012).

[4] P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, Y. Nakamura, B. Brezzo, I. Vo, S. K. Esser, R. Appuswamy, B. Taba, A. Amir, M. D. Flickner, W. P. Risk, R. Manohar, D. S. Modha, Arti cial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface., Science (New York, N.Y.) 345 (2014) 668{73. [OpenAIRE]

[5] L. Sanders, Mind & brain: Model brain mimics human quirks: Computer simulation turns decisions into plans for action, Science News 183 (2013) 13{13.

[6] C. Eliasmith, C. H. Anderson, Neural engineering: Computation, representation and dynamics in neurobiological systems, MIT Press, Cambridge, MA, 2003.

[7] J. Conklin, C. Eliasmith, An attractor network model of path integration in the rat, Journal of Computational Neuroscience 18 (2005) 183{203. [OpenAIRE]

[8] R. Singh, C. Eliasmith, Higher-dimensional neurons explain the tuning and dynamics of working memory cells, Journal of Neuroscience 26 (2006) 3667{3678. [OpenAIRE]

[9] A. Litt, C. Eliasmith, P. Thagard, Neural a ective decision theory: Choices, brains, and emotions, Cognitive Systems Research 9 (2008) 252{273.

[10] T. Bekolay, M. Laubach, C. Eliasmith, A spiking neural integrator model of the adaptive control of action by the medial prefrontal cortex, The Journal of Neuroscience 34 (2014) 1892{1902. [OpenAIRE]

[12] C. Parisien, C. H. Anderson, C. Eliasmith, Solving the problem of negative synaptic weights in cortical models, Neural Computation 20 (2008) 1473{1494.

[13] C. Eliasmith, A uni ed approach to building and controlling spiking attractor networks, Neural computation 17 (2005) 1276{1314. [OpenAIRE]

[14] T. Stewart, C. Eliasmith, Large-Scale Synthesis of Functional Spiking Neural Circuits, Proceedings of the IEEE 102 (2014) 881{898.

[15] C. Eliasmith, How to build a brain: A neural architecture for biological cognition, Oxford University Press, New York, NY, 2013. [OpenAIRE]

[16] E. Crawford, M. Gingerich, C. Eliasmith, Biologically plausible, humanscale knowledge representation, Cognitive Science (2015).

26 references, page 1 of 2
Abstract
Comment: 17 pages 7 figures
Subjects
free text keywords: Quantitative Biology - Neurons and Cognition, Computer Science - Artificial Intelligence
Download from
26 references, page 1 of 2

[1] C. Eliasmith, T. C. Stewart, X. Choo, T. Bekolay, T. DeWolf, Y. Tang, D. Rasmussen, A large-scale model of the functioning brain, Science 338 (2012) 1202{1205. [OpenAIRE]

[2] H. Markram, E. Muller, S. Ramaswamy, M. Reimann, M. Abdellah, C. Sanchez, A. Ailamaki, L. Alonso-Nanclares, N. Antille, S. Arsever, G. Kahou, T. Berger, A. Bilgili, N. Buncic, A. Chalimourda, G. Chindemi, J.-D. Courcol, F. Delalondre, V. Delattre, S. Druckmann, R. Dumusc, J. Dynes, S. Eilemann, E. Gal, M. Gevaert, J.-P. Ghobril, A. Gidon, J. Graham, A. Gupta, V. Haenel, E. Hay, T. Heinis, J. Hernando, M. Hines, L. Kanari, D. Keller, J. Kenyon, G. Khazen, Y. Kim, J. King, Z. Kisvarday, P. Kumbhar, S. Lasserre, J.-V. LeBe, B. Magalha~es, A. Merchan-Perez, J. Meystre, B. Morrice, J. Muller, A. Mun~ozCespedes, S. Muralidhar, K. Muthurasa, D. Nachbaur, T. Newton, M. Nolte, A. Ovcharenko, J. Palacios, L. Pastor, R. Perin, R. Ranjan, I. Riachi, J.-R. Rodr guez, J. Riquelme, C. Rossert, K. Sfyrakis, Y. Shi, J. Shillcock, G. Silberberg, R. Silva, F. Tauheed, M. Telefont, M. Toledo-Rodriguez, T. Trankler, W. VanGeit, J. D az, R. Walker, Y. Wang, S. Zaninetta, J. DeFelipe, S. Hill, I. Segev, F. Schurmann, Reconstruction and Simulation of Neocortical Microcircuitry, Cell 163 (2015) 456{492.

[3] T. Wong, R. Preissl, P. Datta, F. Myron, R. Singh, S. Esser, E. McQuinn, R. Appuswamy, W. Risk, H. Simon, D. Modha, 10^14, IBM Research Report: RJ10502(ALM1211-004) (2012).

[4] P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, Y. Nakamura, B. Brezzo, I. Vo, S. K. Esser, R. Appuswamy, B. Taba, A. Amir, M. D. Flickner, W. P. Risk, R. Manohar, D. S. Modha, Arti cial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface., Science (New York, N.Y.) 345 (2014) 668{73. [OpenAIRE]

[5] L. Sanders, Mind & brain: Model brain mimics human quirks: Computer simulation turns decisions into plans for action, Science News 183 (2013) 13{13.

[6] C. Eliasmith, C. H. Anderson, Neural engineering: Computation, representation and dynamics in neurobiological systems, MIT Press, Cambridge, MA, 2003.

[7] J. Conklin, C. Eliasmith, An attractor network model of path integration in the rat, Journal of Computational Neuroscience 18 (2005) 183{203. [OpenAIRE]

[8] R. Singh, C. Eliasmith, Higher-dimensional neurons explain the tuning and dynamics of working memory cells, Journal of Neuroscience 26 (2006) 3667{3678. [OpenAIRE]

[9] A. Litt, C. Eliasmith, P. Thagard, Neural a ective decision theory: Choices, brains, and emotions, Cognitive Systems Research 9 (2008) 252{273.

[10] T. Bekolay, M. Laubach, C. Eliasmith, A spiking neural integrator model of the adaptive control of action by the medial prefrontal cortex, The Journal of Neuroscience 34 (2014) 1892{1902. [OpenAIRE]

[12] C. Parisien, C. H. Anderson, C. Eliasmith, Solving the problem of negative synaptic weights in cortical models, Neural Computation 20 (2008) 1473{1494.

[13] C. Eliasmith, A uni ed approach to building and controlling spiking attractor networks, Neural computation 17 (2005) 1276{1314. [OpenAIRE]

[14] T. Stewart, C. Eliasmith, Large-Scale Synthesis of Functional Spiking Neural Circuits, Proceedings of the IEEE 102 (2014) 881{898.

[15] C. Eliasmith, How to build a brain: A neural architecture for biological cognition, Oxford University Press, New York, NY, 2013. [OpenAIRE]

[16] E. Crawford, M. Gingerich, C. Eliasmith, Biologically plausible, humanscale knowledge representation, Cognitive Science (2015).

26 references, page 1 of 2
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