Dharmendra S. Modha
Rajagopal Ananthanarayanan
Skip Abstract Section
Skip Supplemental Material SectionAbstract
Unite neuroscience, supercomputing, and nanotechnology to discover, demonstrate, and deliver the brain's core algorithms.
Supplemental Material
Available for Download
doc
modha-appdx.doc (132 KB)
Online appendix associated with "Cognitive computing"
- Albus, J.S., Bekey, G.A., Holland, J.H., Kanwisher, N.G., Krichmar, J.L., Mishkin, M., Modha, D.S., Raichle, M.E., Shepherd, G.M., and Tononi, G. A proposal for a decade of the Mind Initiative {Letter}. Science 317, 5843 (Sept. 7, 2007), 1321.Google Scholar
Cross Ref
- Ananthanarayanan, R., Esser S.K., Simon H.D., and Modha, D.S. The cat is out of the bag: Cortical simulations with 10<zref=R9>9<zrefx> neurons and 10<zref=R13>13<zrefx> synapses. Gordon Bell Prize Winner. In Proceedings of the ACM/IEEE Conference on Supercomputing (Portland, OR, Nov. 14--20). ACM, New York, NY, 2009, 1--12. Google ScholarDigital Library
- Ananthanarayanan, R. and Modha, D.S. Anatomy of a cortical simulator. In Proceedings of the ACM/IEEE Conference on Supercomputing (Reno, NV, Nov. 10--16). ACM, New York, NY, 2007, 3--14. Google ScholarDigital Library
- Arbib, M., Ed. The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge, MA, 2002. Google ScholarDigital Library
- Baumgartner, P. and Payr, S. Eds. Speaking Minds: Interviews with 20 Eminent Cognitive Scientists. Princeton University Press, Princeton, NJ, 1995. Google ScholarDigital Library
- Binzegger, T., Douglas, R.J., and Martin, K.A.A. quantitative map of the circuit of cat primary visual cortex. Journal of Neuroscience 24, 39 (Sept. 2004), 8441--8453.Google ScholarCross Ref
- Brette, R. et al. Simulation of networks of spiking neurons: A review of tools and strategies. Journal of Computational Neuroscience 23, 3 (July, 2007), 349--398.Google ScholarCross Ref
- Gazzaniga, M.S. The Cognitive Neurosciences, Fourth Edition. MIT Press, Cambridge, MA, 2009.Google Scholar
- George, D. and Hawkins, J. Towards a mathematical theory of cortical microcircuits. PLoS Computational Biology 5, 10 (Oct. 2009), e1000532.Google ScholarCross Ref
- Goertzel, B. and Pennachin, C. Artificial General Intelligence. Springer, Berlin, Heidelberg, 2009. Google ScholarDigital Library
- Herz, A.V.M., Gollisch, T., Machens, C.K., and Jaeger, D. Modeling single-neuron dynamics and computations: A balance of detail and abstraction. Science 314, 5796 (Oct. 2006), 80.Google Scholar
- Hill, S. and Tononi, G. Modeling sleep and wakefulness in the thalamocortical system. Journal of Neurophysiology 93, 3 (Nov. 2005), 1671--1698.Google ScholarCross Ref
- Hodgkin, A.L. and Huxley, A.F. A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology 117, 4 (Aug. 1952), 500--544.Google ScholarCross Ref
- IEEE Symposium on Information Theory. MIT, Cambridge, MA, 1956.Google Scholar
- Izhikevich, E.M. and Edelman, G.M. Large-scale model of mammalian thalamocortical systems. Proceedings of the National Academy of Sciences of the USA 105, 9 (Mar. 2008), 3593--3598.Google ScholarCross Ref
- Jeffress, L.A. Cerebral Mechanisms in Behavior: The Hixon Symposium. John Wiley & sons, Inc., New York, 1955.Google Scholar
- Johansson, C. and Lansner, A. Towards cortex-sized artificial neural systems. Neural Networks 20, 1 (Jan. 2007), 48--61. Google ScholarDigital Library
- Kandel, E.R., Schwartz, J.H., and Jessell, T.M. Principles of Neural Science, Fourth Edition. McGraw-Hill Medical, New York, 2000.Google Scholar
- Kötter, R. Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database. Neuroinformatics 2, 2 (June 2004), 127--144.Google ScholarCross Ref
- Markram, H. The Blue Brain Project. National Review of Neuroscience 7, 2 (Feb. 2006), 153--160.Google Scholar
- Marr, D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman, Cambridge, MA, 1983. Google ScholarDigital Library
- McCarthy, J. Generality in artificial intelligence. In Formalizing Common Sense, V. Lifschitz, Ed. Ablex, Bristol, U.K., 1990, 226--236.Google Scholar
- McClelland, J.L. and Rumelhart, D.E. Parallel Distributed Processing. In Psychological and Biological Models, Volume 2. MIT Press, Cambridge, MA, 1987.Google ScholarCross Ref
- McCulloch, W.S. and Pitts, W. How we know universals: The perception of auditory and visual forms. Bulletin of Mathematical Biology 9, 3 (Sept. 1947), 127--147.Google Scholar
- McCulloch, W.S. and Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5 (1943), 115--133.Google ScholarCross Ref
- Mead, C. Analog VLSI and Neural Systems. Addison Wesley Publishing Co., Boston, 1989. Google ScholarDigital Library
- Minsky, M. The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. Simon & Schuster, New York, 2006. Google ScholarDigital Library
- Modha, D.S. and Singh, R. Network architecture of the long-distance pathways in the macaque brain. Proceedings of the National Academy of Sciences of the USA 107, 30 (June 2010), 13485--13490.Google ScholarCross Ref
- Mountcastle, V.B. Perceptual Neuroscience: The Cerebral Cortex. Harvard University Press, Cambridge, MA, 1998.Google Scholar
- Rosenblatt, F. The Perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review 65, 6 (Nov. 1958), 386--408.Google ScholarCross Ref
- Schultz, W., Dayan, P., and Montague, P.R. A neural substrate of prediction and reward. Science 275, 5306 (Mar. 1997), 1593--1599.Google ScholarCross Ref
- Sherbondy, A.J., Dougherty, RF., Ananthanaraynan, R., Modha, D.S., and Wandell, B.A. Think global, act local: Projectome estimation with BlueMatter. In Proceedings of the Medical Image Computing and Computer Assisted Intervention Society, Lecture Notes in Computer Science (London, Sept. 20--24). Springer, Berlin, 2009, 861--868. Google ScholarDigital Library
- Song, S., Miller, K.D., and Abbott, L.F. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience 3, 9 (Sept. 2000), 919--926.Google ScholarCross Ref
- Sutton, R.S. and Barto, A.G. Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Review 88, 2 (Mar. 1981), 135--140.Google ScholarCross Ref
- Teddington Symposium on the Mechanization of Thought Processes (National Physical Laboratory, Nov. 24--27). Her Majesty's Stationary Office, London, 1958.Google Scholar
- Traub, R.D., Contreras, D., Cunningham, M., Murray, H., LeBeau, F.E.N., Roopun, A., Bibbig, A., Wilent, W.B., Higley, M., and Whittington, M.A. Single-column thalamocortical network model exhibiting gamma oscillations, spindles, and epileptogenic bursts. Journal of Neurophysiology 93, 4 (Nov. 2005), 2194--2232.Google ScholarCross Ref
- Valiant, L.G. Circuits of the Mind. Oxford University Press, New York, 2000. Google ScholarDigital Library
- von Hayek, F.A. The Sensory Order: An Inquiry Into the Foundations of Theoretical Psychology. University of Chicago Press, Chicago, 1952.Google Scholar
- von Neumann, J. The Computer and the Brain, Second Edition. Yale University Press, New Haven, CT, 2000. Google ScholarDigital Library
Index Terms
- Cognitive computing
Recommendations
Interconnected Large-Scale Systems for Three Fundamental Cognitive Tasks Revealed by Functional MRI
The specific brain areas required to execute each of three fundamental cognitive tasks - object naming, same-different discrimination, and integer computation - are determined by whole-brain functional magnetic resonance imaging (fMRI) using a novel ...
The neural correlates of cognitive control: Successful remembering and intentional forgetting
The ability to control how we process information by remembering that which is important and forgetting that which is irrelevant is essential to maintain accurate, up-to-date memories. As such, memory success is predicated on both successful intentional ...
Dynamic adjustments of cognitive control: Oscillatory correlates of the conflict adaptation effect
It is a prominent idea that cognitive control mediates conflict adaptation, in that response conflict in a previous trial triggers control adjustments that reduce conflict in a current trial. In the present EEG study, we investigated the dynamics of ...
Comments