Artificial Neural Networks and Machine Learning -- ICANN 2012

1. Front Matter

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2. Theoretical Neural Computation (A3)

   1. Temporal Patterns in Artificial Reaction Networks

      • Claire Gerrard, John McCall, George M. Coghill, Christopher Macleod

      Pages 1-8

   2. Properties of the Hopfield Model with Weighted Patterns

      • Iakov Karandashev, Boris Kryzhanovsky, Leonid Litinskii

      Pages 9-16

   3. Dynamics and Oscillations of GHNNs with Time-Varying Delay

      • Farouk Chérif

      Pages 17-24

   4. A Dynamic Field Architecture for the Generation of Hierarchically Organized Sequences

      • Boris Durán, Yulia Sandamirskaya, Gregor Schöner

      Pages 25-32

3. Information and Optimization (C1)

   1. Stochastic Techniques in Influence Diagrams for Learning Bayesian Network Structure

      • Michal Matuszak, Jacek Miękisz

      Pages 33-40

   2. The Mix-Matrix Method in the Problem of Binary Quadratic Optimization

      • Iakov Karandashev, Boris Kryzhanovsky

      Pages 41-48

   3. A Rule Chaining Architecture Using a Correlation Matrix Memory

      • James Austin, Stephen Hobson, Nathan Burles, Simon O’Keefe

      Pages 49-56

   4. A Generative Multiset Kernel for Structured Data

      • Davide Bacciu, Alessio Micheli, Alessandro Sperduti

      Pages 57-64

   5. Spectral Signal Unmixing with Interior-Point Nonnegative Matrix Factorization

      • Rafal Zdunek

      Pages 65-72

   6. Hybrid Optimized Polynomial Neural Networks with Polynomial Neurons and Fuzzy Polynomial Neurons

      • Dan Wang, Donghong Ji, Wei Huang

      Pages 73-80

   7. Tikhonov-Type Regularization for Restricted Boltzmann Machines

      • KyungHyun Cho, Alexander Ilin, Tapani Raiko

      Pages 81-88

4. From Neurons to Neuromorphism (A1)

   1. Modeling of Spiking Analog Neural Circuits with Hebbian Learning, Using Amorphous Semiconductor Thin Film Transistors with Silicon Oxide Nitride Semiconductor Split Gates

      • Richard Wood, Ian Bruce, Peter Mascher

      Pages 89-96

   2. Real-Time Simulations of Synchronization in a Conductance-Based Neuronal Network with a Digital FPGA Hardware-Core

      • Marcel Beuler, Aubin Tchaptchet, Werner Bonath, Svetlana Postnova, Hans Albert Braun

      Pages 97-104

   3. Impact of Frequency on the Energetic Efficiency of Action Potentials

      • Anand Singh, Pierre J. Magistretti, Bruno Weber, Renaud Jolivet

      Pages 105-112

   4. A Large-Scale Spiking Neural Network Accelerator for FPGA Systems

      • Kit Cheung, Simon R. Schultz, Wayne Luk

      Pages 113-120

   5. Silicon Neurons That Compute

      • Swadesh Choudhary, Steven Sloan, Sam Fok, Alexander Neckar, Eric Trautmann, Peiran Gao et al.

      Pages 121-128

   6. A Communication Infrastructure for Emulating Large-Scale Neural Networks Models

      • Andres Gaona Barrera, Manuel Moreno Arostegui

      Pages 129-136

5. Spiking Dynamics (B2)

   1. Pair-Associate Learning with Modulated Spike-Time Dependent Plasticity

      • Nooraini Yusoff, André Grüning, Scott Notley

      Pages 137-144

   2. Associative Memory in Neuronal Networks of Spiking Neurons: Architecture and Storage Analysis

      • Everton J. Agnes, Rubem Erichsen Jr., Leonardo G. Brunnet

      Pages 145-152

The two-volume set LNCS 7552 + 7553 constitutes the proceedings of the 22nd International Conference on Artificial Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers included in the proceedings were carefully reviewed and selected from 247 submissions. They are organized in topical sections named: theoretical neural computation; information and optimization; from neurons to neuromorphism; spiking dynamics; from single neurons to networks; complex firing patterns; movement and motion; from sensation to perception; object and face recognition; reinforcement learning; bayesian and echo state networks; recurrent neural networks and reservoir computing; coding architectures; interacting with the brain; swarm intelligence and decision-making; mulitlayer perceptrons and kernel networks; training and learning; inference and recognition; support vector machines; self-organizing maps and clustering; clustering, mining and exploratory analysis; bioinformatics; and time weries and forecasting.

• brain-computer interface
• combinatorial optimization
• evolutionary algorithm
• particle swarm
• self organizing maps
• algorithm analysis and problem complexity

• Neuro Heuristic Research Group, University of Lausanne, Lausanne, Switzerland

  Alessandro E. P. Villa

• Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

  Włodzisław Duch

• Center for Complex Systems Studies, Kalamazoo College, Kalamazoo, USA

  Péter Érdi

• Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, Genoa, Italy

  Francesco Masulli

• Institut für Neuroinformatik, Universität Ulm, Ulm, Germany

  Günther Palm

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