1. Biological Foundations of Neural Computation: Principles of brain organization. Neuroanatomy and Neurophysiology of synapses, dendrodendritic contacts, neurons and neural nets in peripheral and central areas. Plasticity, learning and memory in natural neural nets. Models of development and evolution. The computational perspective in Neuroscience.
   
2. Formal Tools and Computational Models of Neurons and Neural Nets Architectures: Analytic and logic models. Object oriented formulations. Hybrid knowledge representation and inference tools (rules and frames with analytic slots). Probabilistic, bayesian and fuzzy models. Energy related models.
   
3. Plasticity Phenomena (Maturing, Learning and Memory): Biological mechanisms of learning and memory. Computational formulations using correlational, reinforcement and minimization strategies. Conditioned reflex and associative mechanisms. Inductive-deductive and abductive symbolic-subsymbolic formulations. Generalization.
   
4. Complex Systems Dynamics: Self-organization, cooperative processes, autopoiesis, emergent computation, synergetic, evolutive optimization and genetic algorithms. Self-reproducing nets. Self-organizing feature maps. Simulated evolution. Social organization phenomena.
   
5. Cognitive Science and AI: Hybrid knowledge based system. Neural networks for knowledge modeling, acquisition and refinement. Natural language understanding. Concepts formation. Spatial and temporal planning and scheduling. Intentionality.
   
6. Neural Nets Simulation, Emulation and Implementation: Environments and languages. Parallelization, modularity and autonomy. New hardware implementation strategies (FPGA's, VLSI, neurodevices). Evolutive architectures. Real systems validation and evaluation.
   
7. Methodology for Data Analysis, Task Selection and Nets Design.
   
8. Neural Networks for Perception: Biologically inspired preprocessing. Low level processing, source separation, sensor fusion, segmentation, feature extraction, adaptive filtering, noise reduction, texture, stereo correspondence, motion analysis, speech recognition, artificial vision, and hybrid architectures for multisensorial perception.
   
9. Neural Networks for Communications Systems: Modems and codecs, network management, digital communications.
   
10. Neural Networks for Control and Robotics: Systems identification, motion planning and control, adaptive, predictive and model-based control systems, navigation, real time applications, visuo-motor coordination.

Back to IWANN'97 INDEX