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Alessandro E. P. Villa: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Yoshiyuki Asai, Tatyana I. Aksenova, Alessandro E. P. Villa
    Unsupervised Recognition of Neuronal Discharge Waveforms for On-line Real-Time Operation. [Citation Graph (0, 0)][DBLP]
    BVAI, 2005, pp:29-38 [Conf]
  2. Javier Iglesias, Jan Eriksson, Beatriz Pardo, Marco Tomassini, Alessandro E. P. Villa
    Stimulus-Driven Unsupervised Synaptic Pruning in Large Neural Networks. [Citation Graph (0, 0)][DBLP]
    BVAI, 2005, pp:59-68 [Conf]
  3. Luc Jeandenans, Michel Gautero, François Grize, Igor V. Tetko, Alessandro E. P. Villa
    Computer assisted neurophysiology by a distributed JAVA program. [Citation Graph (0, 0)][DBLP]
    HCC, 1998, pp:261-272 [Conf]
  4. Tatyana I. Aksenova, Vladimir Volkovich, Alessandro E. P. Villa
    Robust Structural Modeling and Outlier Detection with GMDH-Type Polynomial Neural Networks. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2005, pp:881-886 [Conf]
  5. Yoshiyuki Asai, Tatyana I. Aksenova, Alessandro E. P. Villa
    On-Line Real-Time Oriented Application for Neuronal Spike Sorting with Unsupervised Learning. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2005, pp:109-114 [Conf]
  6. Yoshiyuki Asai, Takashi Yokoi, Alessandro E. P. Villa
    Detection of a Dynamical System Attractor from Spike Train Analysis. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2006, pp:623-631 [Conf]
  7. Javier Iglesias, Jan Eriksson, Beatriz Pardo, Marco Tomassini, Alessandro E. P. Villa
    Emergence of Oriented Cell Assemblies Associated with Spike-Timing-Dependent Plasticity. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2005, pp:127-132 [Conf]
  8. Javier Iglesias, Alessandro E. P. Villa
    Neuronal Cell Death and Synaptic Pruning Driven by Spike-Timing Dependent Plasticity. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2006, pp:953-962 [Conf]
  9. Juan Manuel Moreno, Javier Iglesias, Jan Eriksson, Alessandro E. P. Villa
    Physical Mapping of Spiking Neural Networks Models on a Bio-inspired Scalable Architecture. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2006, pp:936-943 [Conf]
  10. Igor V. Tetko, Alessandro E. P. Villa
    A Comparative Study of Pattern Detection Algorithm and Dynamical System Approach Using Simulated Spike Trains. [Citation Graph (0, 0)][DBLP]
    ICANN, 1997, pp:37-42 [Conf]
  11. Jan Eriksson, Oriol Torres, Andrew Mitchell, Gayle Tucker, Ken Lindsay, David M. Halliday, Jay Rosenberg, Juan Manuel Moreno, Alessandro E. P. Villa
    Spiking Neural Networks for Reconfigurable POEtic Tissue. [Citation Graph (0, 0)][DBLP]
    ICES, 2003, pp:165-173 [Conf]
  12. Juan Manuel Moreno, Jan Eriksson, Javier Iglesias, Alessandro E. P. Villa
    Implementation of Biologically Plausible Spiking Neural Networks Models on the POEtic Tissue. [Citation Graph (0, 0)][DBLP]
    ICES, 2005, pp:188-197 [Conf]
  13. Andrew M. Tyrrell, Eduardo Sanchez, Dario Floreano, Gianluca Tempesti, Daniel Mange, Juan Manuel Moreno, Jay Rosenberg, Alessandro E. P. Villa
    POEtic Tissue: An Integrated Architecture for Bio-inspired Hardware. [Citation Graph (0, 0)][DBLP]
    ICES, 2003, pp:129-140 [Conf]
  14. Tatyana I. Aksenova, Olga K. Chibirova, Alim-Louis Benabid, Alessandro E. P. Villa
    Nonlinear Oscillation Models for Spike Separation. [Citation Graph (0, 0)][DBLP]
    ISMDA, 2002, pp:61-70 [Conf]
  15. Oriol Torres, Jan Eriksson, Juan Manuel Moreno, Alessandro E. P. Villa
    Hardware Optimization of a Novel Spiking Neuron Model for the POEtic Tissue. [Citation Graph (0, 0)][DBLP]
    IWANN (2), 2003, pp:113-120 [Conf]
  16. Igor V. Tetko, Alessandro E. P. Villa
    Pattern grouping algorithm and de-convolution filtering of non-stationary correlated Poisson processes. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2001, v:38, n:, pp:1709-1714 [Journal]
  17. Alessandro E. P. Villa, Igor V. Tetko, Javier Iglesias
    Computer assisted neurophysiological analysis of cell assemblies activity. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2001, v:38, n:, pp:1025-1030 [Journal]
  18. Vasyl V. Kovalishyn, Igor V. Tetko, Alexander I. Luik, Vladyslav V. Kholodovych, Alessandro E. P. Villa, David J. Livingstone
    Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture. [Citation Graph (0, 0)][DBLP]
    Journal of Chemical Information and Computer Sciences, 1998, v:38, n:4, pp:651-659 [Journal]
  19. Igor V. Tetko, Vsevolod Yu. Tanchuk, Tamara N. Kasheva, Alessandro E. P. Villa
    Internet Software for the Calculation of the Lipophilicity and Aqueous Solubility of Chemical Compounds. [Citation Graph (0, 0)][DBLP]
    Journal of Chemical Information and Computer Sciences, 2001, v:41, n:2, pp:246-252 [Journal]
  20. Igor V. Tetko, Vsevolod Yu. Tanchuk, Tamara N. Kasheva, Alessandro E. P. Villa
    Estimation of Aqueous Solubility of Chemical Compounds Using E-State Indices. [Citation Graph (0, 0)][DBLP]
    Journal of Chemical Information and Computer Sciences, 2001, v:41, n:6, pp:1488-1493 [Journal]
  21. Igor V. Tetko, Vsevolod Yu. Tanchuk, Alessandro E. P. Villa
    Prediction of n-Octanol/Water Partition Coefficients from PHYSPROP Database Using Artificial Neural Networks and E-State Indices. [Citation Graph (0, 0)][DBLP]
    Journal of Chemical Information and Computer Sciences, 2001, v:41, n:5, pp:1407-1421 [Journal]
  22. Igor V. Tetko, Alessandro E. P. Villa, Tatyana I. Aksenova, Walter L. Zielinski, James Brower, Elizabeth R. Collantes, William J. Welsh
    Application of a Pruning Algorithm To Optimize Artificial Neural Networks for Pharmaceutical Fingerprinting. [Citation Graph (0, 0)][DBLP]
    Journal of Chemical Information and Computer Sciences, 1998, v:38, n:4, pp:660-668 [Journal]
  23. Igor V. Tetko, Alessandro E. P. Villa, David J. Livingstone
    Neural Network Studies, 2. Variable Selection. [Citation Graph (0, 0)][DBLP]
    Journal of Chemical Information and Computer Sciences, 1996, v:36, n:4, pp:794-803 [Journal]
  24. Igor V. Tetko, Alessandro E. P. Villa
    Efficient Partition of Learning Data Sets for Neural Network Training. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1997, v:10, n:8, pp:1361-1374 [Journal]
  25. Igor V. Tetko, Alessandro E. P. Villa
    An Enhancement of Generalization Ability in Cascade Correlation Algorithm by Avoidance of Overfitting/Overtraining Problem. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 1997, v:6, n:1-2, pp:43-50 [Journal]
  26. Igor V. Tetko, Alessandro E. P. Villa
    An Efficient Partition of Training Data Set Improves Speed and Accuracy of Cascade-correlation Algorithm. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 1997, v:6, n:1-2, pp:51-59 [Journal]
  27. Yoshiyuki Asai, Takashi Yokoi, Alessandro E. P. Villa
    Deterministic Nonlinear Spike Train Filtered by Spiking Neuron Model. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2007, pp:924-933 [Conf]
  28. Javier Iglesias, Olga K. Chibirova, Alessandro E. P. Villa
    Nonlinear Dynamics Emerging in Large Scale Neural Networks with Ontogenetic and Epigenetic Processes. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2007, pp:579-588 [Conf]
  29. Roberta Sirovich, Laura Sacerdote, Alessandro E. P. Villa
    Effect of Increasing Inhibitory Inputs on Information Processing Within a Small Network of Spiking Neurons. [Citation Graph (0, 0)][DBLP]
    IWANN, 2007, pp:23-30 [Conf]
  30. Alessandro E. P. Villa, Javier Iglesias
    OpenAdap.net: Evolvable Information Processing Environment. [Citation Graph (0, 0)][DBLP]
    WILF, 2007, pp:227-236 [Conf]

  31. Effect of the Background Activity on the Reconstruction of Spike Train by Spike Pattern Detection. [Citation Graph (, )][DBLP]


  32. Effect of Feedback Strength in Coupled Spiking Neural Networks. [Citation Graph (, )][DBLP]


  33. A Framework for Simulation and Analysis of Dynamically Organized Distributed Neural Networks. [Citation Graph (, )][DBLP]


  34. Transmission of Distributed Deterministic Temporal Information through a Diverging/Converging Three-Layers Neural Network. [Citation Graph (, )][DBLP]


  35. Functional Connectivity Driven by External Stimuli in a Network of Hierarchically Organized Neural Modules. [Citation Graph (, )][DBLP]


  36. Dynamics of Firing Patterns in Evolvable Hierarchically Organized Neural Networks. [Citation Graph (, )][DBLP]


  37. OpenAdap.net: a Community-Based Sharing System. [Citation Graph (, )][DBLP]


  38. Artificial Neural Networks simulation of learning of auditory equivalence classes for vowels. [Citation Graph (, )][DBLP]


  39. Changes in quadratic phase coupling of EEG signals during wake and sleep in two chronic insomnia patients, before and after cognitive behavioral therapy. [Citation Graph (, )][DBLP]


  40. PERPLEXUS: Pervasive Computing Framework for Modeling Complex Virtually-Unbounded Systems. [Citation Graph (, )][DBLP]


  41. JubiTool: Unified design flow for the Perplexus SIMD hardware accelerator. [Citation Graph (, )][DBLP]


  42. A Hierarchical Classification of First-Order Recurrent Neural Networks. [Citation Graph (, )][DBLP]


  43. Fast combinatorial methods to estimate the probability of complex temporal patterns of spikes. [Citation Graph (, )][DBLP]


  44. Low-dimensional chaotic attractors in the rat brain. [Citation Graph (, )][DBLP]


  45. Low-dimensional chaotic attractors in the rat brain. [Citation Graph (, )][DBLP]


  46. Low-dimensional chaotic attractors in the rat brain. [Citation Graph (, )][DBLP]


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