IWANN2003: Topics

1. Mathematical & Computational Methods in neural modeling.
   Levels of analysis. Brain Theory. Neural coding. Mathematical Biophysics. Population Dynamics and Statistical modeling. Diffusion processes. Dynamical Binding. Synchronization. Resonance. Regulatory Mechanisms. Cellular Automata.
2. Neurophysiological data analysis and modeling.
   Ionic channels. Synapses. Neurons. Circuits. Biophysical simulations.
3. Structural and functional models of neurons.
   Analogue, non-linear, recurrent, RBF, PCA, digital, probabilistic, Bayesian, fuzzy and object oriented formulations.
4. Learning and other plasticity phenomena.
   Supervised, non-supervised, reinforcement and statistical algorithms. Hybrid formulations. Incremental-decremental architectures. Biological mechanisms of adaptation and plasticity. Development and maturing.
5. Complex systems dynamics.
   Statistical-mechanics. Attractors. Optimization, self-organization and cooperative-competitive networks. Evolutionary and genetic algorithms.
6. Cognitive Processes and Artificial Intelligence.
   Perception (visual, auditive, tactile, proprioceptive). Multi-sensory integration. Natural language. Memory. Decision Making. Planning. Motor Control. Neuroethology. Knowledge modeling. Multi-agent systems. Distributed AI. Social systems.
7. Methodology for nets design, simulation and implementation.
   Data analysis, task identification and recursive design. Development environments and editing tools. Implementation. Evolving hardware.
8. Bio-inspired systems and engineering.
   Bio-cybernetics and Bionics. Signal processing, neural prostheses, retinomorphic systems, and other neural adaptive prosthetic devices. Molecular computing.
9. Applications.
   Artificial vision, speech recognition, spatio-temporal planning and scheduling. Data mining. Sources separation. Applications of ANNs in Robotics, Astrophysics, Economy, Internet, Medicine, Education and Industry.