Articles

The following section contains articles related with neurocomputing stuff. This compilation is, by no means complete. If you want me to check an article and add it to the list just contact me.

Neurocomputing

  • Roudi, Y. and Latham, P. E. (2007). A Balanced Memory Network. PLoS Comput Biol. In press. doi:10.1371/journal.pcbi.0030141.eor. June 2007. [Download]
  • Serre, T., Olivia, A., and Poggio, T. (2007). A feedforward architecture accounts for rapid categorization. Proceedings of the National Academy of Sciences, PNAS. doi:10.1073/pnas.0700622104. April 2007. [Download]
  • Serre, T., Wolf L., Bileschi, S., Riesenhuber, M., and Poggio, T. (2007). Robust Object Recognition with Cortex-Like Mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 29, No. 3, March 2007. [Download]
  • Lücke, J. and Von der Malsburg, C. (2006). Rapid Correspondence Finding in Networks of Cortical Columns. Preprint, ICANN 2006, Springer-Verlag, LNCS 4131, pp. 668-677. [Download]
  • Hawkins, J. and George, D. (2005). A Hierarchical Bayesian Model of Invariant Pattern Recognition in the Visual Cortex. IJCNN 05 Proceedings. [Download]
  • Bressloff, C. P. (2005). Lectures in mathematical neuroscience. PCMI 2005 Proceedings. [Lecture 1] [Lecture 2] [Lecture 3] [Lecture 4] [Lecture 5]
  • Lücke, J. (2005). Dynamics of Cortical Columns Sensitive Decision Making. Preprint, ICANN 2005, Springer-Verlag, LNCS 3696, 25-30. [Download]
  • Hecht-Nielsen, R. (2005). Confabulation Theory: A Synopsis. Technical Report #0501, Institute for Neural Computation. University of California, San Diego. [Download]
  • Hecht-Nielsen, R. (2004). Perceptrons. UCSD Institute for Neural Computation Technical Report #0403. July, 2004. [Download]
  • Lücke, J. (2004). Hierarchical Self-Organization of Minicolumnar Receptive Fields. Neural Networks, Special Issue on Self-Organizing Systems,17/8-9:1377-1389. Elsevier. [Download]
  • Lücke, J. (2004). Clustering with Minicolumnar Receptive Field Self-Organization. Proceedings of IJCNN 2004, IEEE/Omnipress, pp. 3113-3118. [Download]
  • Lücke, J. and Von der Malsburg, C. (2004). Rapid Processing and Unsupervised Learning in a Model of the Cortical Macrocolumn. Neural Computation, 16(3), 501 - 533, Massachusetts Institute of Technology. [Download]
  • Jirsa, K. V. and Fuchs, A. (2004). Introduction to Complex System Tools. Mathematics Boot Camp. Center for Complex Systems & Brain Sciences. Florida Atlantic University. [Download]
  • Hawkins, J. and George, D. (2004). Invariant Pattern Recognition using Bayesian Inference on Hierarchical Sequences. RNI Tech Report. [Download]
  • Tijsseling, A. (2003). Overview of CALM (draft). http://www.kung-foo.tv. [Download]
  • Lücke, J., Von der Malsburg, C. and Würtz, R. P. (2002). Macrocolumns as Decision Units. ICANN 2002, Springer-Verlag, LNCS 2415, pp. 57-62. [Download]
  • Sagi, B., Nemat-Nasser, S.C., Kerr, R., Hayek, R. and Hecht-Nielsen, R. (2001). A Biologically Motivated Solution to the Cocktail Party Problem. MIT Press, Neural Computation 13, 1575 - 1602 (2001). [Download]
  • Hawkins, J. (1986). An Investigation of Adaptive Behavior Towards a Theory of Neocortical Function. U.C. Berkeley. Ph.D. Proposal [Download]
  • Albus, J. S. (1975). A New Approach to Manipulator Control: The Cerebe- llar Model Articulation Controller (CMAC). Journal Dyn. Syst. Meas. Control, Trans. ASME 97. 220-227. [Download]

Neuroscience

  • DeFelipe, J. (2005). Cajal y sus dibujos: ciencia y arte. Arte y Neurología. Madrid. Capítulo 18. [Download] [SPANISH]
  • Mountcastle, V. B. (2003). Introduction. Cerebral Cortex. Vol. 13, No. 1, 2-4. Oxford University Press. [Download]
  • DeFelipe, J., Alonso-Nanclares, L., Arrellano, J.I. (2002). Microstructure of the neocortex: Comparative aspects. No. 31, 299-316. Journal of Neurocytology. [Download]
  • Mountcastle, V. B. (1997). The columnar organization of the neocortex. Brain, Vol 120, Issue 4 701-722. Oxford University Press. [Download]
  • Hodgkin, A.L. and Huxley, A.F., and Katz, B (1952). Measurement of current-voltage relations in the membrane of the giant axon of Loligo. Journal of Physiology 116: 424-448. [Download]
  • Hodgkin, A.L. and Huxley, A.F. (1952). Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. Journal of Physiology 116: 449-472. [Download]
  • Hodgkin, A.L. and Huxley, A.F. (1952). The components of membrane conductance in the giant axon of Loligo. Journal of Physiology 116: 473-496. [Download]
  • Hodgkin, A.L. and Huxley, A.F. (1952). The dual effect of membrane potential on sodium conductance in the giant axon of Loligo. Journal of Physiology 116: 497-506. [Download]
  • Hodgkin, A.L. and Huxley, A.F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology 117(4): 500-544. [Download]

Spiking Neural Networks

  • Ananthanarayanan, R. and Modha, D. (2007). Anatomy of a Cortical Simulator. International Conference for High Performance Computing, Networking, Storage and Analysis. November 13, 2007. [Download]
  • Rao, R.P.N. (2004). Hierarchical Bayesian Inference in Networks of Spiking Neurons. Advances in NIPS 2004. Vol.17. [Download]
  • Bohte, S.M. (2003). Spiking Neural Networks. Leiden University. Thesis. ISBN 90-6734-167-3 [Download]
  • Eurich, C.W. and Wilke, S.D. (2000). Multidimensional Encoding Strategy of Spiking Neurons. Neural Computation, 12:7:1519-1529(11) [Download]
  • Maass, W. (1996). Networks of Spiking Neurons: The Third Generation of Neural Network Models. ECCC TR96-031. [Download]

Recurrent Neural Networks

  • Pearlmutter, B. A. (1990). Dynamic Recurrent Neural Networks. School of Computer Science, Carnegie Mellon University, Dec 1990. [Download]
  • Elman, J.L. (1990). Finding structure in time. Cognitive Science 14: 179-211. [Download]

Bioengineering

  • Lin, J. and Merolla, P. and Arthur, J. and Boahen, K. (2006). Programmable Connections in Neuromorphic Grids. 49th IEEE Midwest Symposium on Circuits and Symtems, 2006. In press. [Download]
  • Arthur, J.V and Boahen K. (2004). Recurrently Connected Silicon Neurons with Active Dendrites for One-Shot Learning. International Joint Conference on Neural Networks, IJCNN'04. 1699-1704. IEEE Press. [Download]
  • Taba, B. and Boahen, K. (2003). Topographic Map Formation by Silicon Growth Cones. Advances in Neural Information Processing Systems 15. MIT Press. [Download]
  • Legenstein, R.A. and Maass, W. (2001). Foundations for a Circuit Complexity Theory of Sensory Processing. Proc. of NIPS 2000, Advances in Neural Information Processing Systems, vol 13, 259-265. Cambridge. MIT Press. [Download]

Cognitive Linguistics and Natural Language Processing

  • Carbonell J. and Klein, S. and Miller, D. and Steinbaum, M. and Grassiany, T. and Frey, J. (2006). Context-Based Machine Translation. Proceedings of the 7th Conference of the Association for Machine Translation in the Americas, 19-28, Cambridge, August 2006. [Download]
  • Solan, Z., Horn, D., Ruppin, E. and Edelman, S. (2005). Unsupervised learning of natural languages. PNAS. Vol. 102. Issue 35. [Download]
  • Cucerzan, S. and Brill, E. (2004). Spelling Correction as an Iterative Process that Exploits the Collective Knowledge of Web Users. Conference on Empirical Methods in Natural Language Processing (EMNLP). Barcelona, 2004. [Download]
  • Narayanan, S. S. (1997). Knowledge-based Action Representations for Metaphor and Aspect (KARMA). UC Berkeley Ph.D dissertation. [Download]
  • McDermott, D. (1976). Artificial Intelligence meets natural stupidity. ACM Sigart Bulletin, Issue 57. April 1976. ACM Press, NY. [Download]

Genetic Algorithms

  • Floreano, D., Mitri, S., Magnenat, S. and Keller, L. (2007). Evolutionary Conditions for the Emergence of Communication in Robots Current Biology 17, 514-519, March. [Download]
  • Manrique, D., Rios, J. and Rodríguez-Patón (2005). Evolutionary system for automatically constructing and adapting radial basis function networks. Neurocomputing. In Press. [Download]
  • Barrios, D., Carrascal, A., Manrique, D., Rios, J. (2003). Cooperative binary-real coded genetic algorithms for generating and adapting artificial neural networks. Neural Computing & Applications, Vol 12, Num 2. Springer London. [Download]

Intrusion Detection Systems (IDS)

  • Cannady, J. (2000). Applying CMAC-Based On-Line Learning to Intrusion Detection. Proceedings of the 2000 IEEE/INNS Joint International Conference on Neural Networks. [Download]
  • Ptacek, T.H. and Newsham, T.N. (1998). Insertion, Evasion, and Denial of Service: Eluding Network Intrusion Detection. SecureNetworks Inc. January 1998. [Download]
  • Cannady, J. and Harrel, J. (1996). A comparative Analysis of Current Intrusion Detection Technologies. Proceedings of Technology in Information Security Conference (TISC). 212-218. [Download]
  • Anderson, J.P. (1980). Computer security thread monitoring and surveillance. Fort Washington, PA. April 1980. [Download]
  • Denning, D.E. (1986). An Intrusion-Detection Model. IEEE Computer Society Symposium on Research in Security and Privacy, pp118-131 [Download]

Security

  • Zhuang, L., Zhou, F., Tygar, J.D. (2005). Keyboard Acoustic Emanations Revisited. Proceedings of the 12th ACM Conference on Computer and Communications Security. [Download]
  • Morris, R. and Thompson, K. (1979). Password Security: A case history. Communications of the ACM, Volume 22, Issue 11. November 1979. ACM Press, NY. [Download]

Last Update: 29/01/2008

Valid CSS!

Valid XHTML 1.1

Atom Feed NEWS SUBSCRIBE