IWINAC International Work-conference on the Interplay between Natural and Artificial Computation

IWINAC-2005: Topics

To address the two questions exposed in the scope of IWINAC-2005, we will make use of the ``building of and for knowledge'' concepts that distinguish three levels of description in each calculus: The Physical Level (PL), where the hardware lives, the Symbol Level (SL) where the programs live and a third level, introduced by Newell and Marr, situated above the symbol level and named by Newell ``the Knowledge Level'' (KL) and by Marr the level of ``the theory of calculus''. We seek the interplay between the natural and the artificial at each one of these three levels (PL, SL, KL).

1  Interplay at the Physical Level
From Artificial to Natural
1.1  Computational Neuroscience
  • 1.1.1 Tools  
    Conceptual, formal, and computational tools and methods in the modeling of neuronal processes and neural nets: individual and collective dynamics.
  • 1.1.2 Mechanisms  
    Computational modeling of neural mechanisms at the architectural level: oscillatory/regulatory feedback loops, lateral inhibition, reflex arches, connectivity and signal routing networks, distributed central-patterns generators. Contributions to library of neural circuitry.
  • 1.1.3 Plasticity  
    Models of memory, adaptation, learning and other plasticity phenomena. Mechanisms of reinforcement, self-organization, anatomo-physiological coordination and structural coupling.
From Natural to Artificial
1.2  Bio-inspired Circuits and Mechanisms
  • 1.2.1 Electronics  
    Bio-inspired electronics and computer architectures. Advanced models for ANN. Evolvable hardware (CPLDs, FPGAs, ...). Adaptive cellular automata. Redundancy, parallelism and fault-tolerant computation. Retinotopic organizations.
  • 1.2.2 Non-conventional (Natural) Computation  
    Biomaterials for computational systems. DNA, cellular and membrane computing.
  • 1.2.3 Sensory and motor prostheses  
    Signal processing, artificial cochlea, audio-tactile vision substitution. Artificial sensory and motor systems for handicapped people. Intersensory transfer and sensory plasticity.

2  Interplay at the Symbol Level
From Artificial to Natural
2.1  Neuro-informatics
  • 2.1.1 Symbols  
    From neurons to neurophysiological symbols (regularities, synchronization, resonance, dynamics binding and other potential mechanisms underlying neural coding). Neural data structures and neural "algorithms".
  • 2.1.2 Brain databases  
    Neural data analysis, integration and sharing. Standardization, construction and use of databases in neuroscience and cognition.
  • 2.1.3 Neurosimulators 
    Development and use of biologically oriented Neurosimulators. Contributions to the understanding of the relationships between structure and function in biology.
From Natural to Artificial
2.2  Bio-inspired Programming Strategies
  • 2.2.1 Behavior based computational methods  
    Reactive mechanisms. Self-organizative optimization. Collective emergent behavior (ant colonies). Ethology and Artificial Life.
  • 2.2.2 Evolutionary computation  
    Genetic algorithms, evolutionary strategies, evolutionary programming and genetic programming. Macroevolution and the interplay between evolution and learning.
  • 2.2.3 Hybrid approaches 
    Neuro-symbolic integration. Knowledge-based ANN and connectionist KBS. Neuro-fuzzy systems. Hybrid adaptation and learning at the symbol level.

3  Interplay at the Knowledge Level
From Artificial to Natural
3.1  Computational approach to Cognition
  • 3.1.1 AI&KE  
    Use of AI&KE concepts, tools, and methods in the modeling of mental processes and behavior. Contribution to the AI debate on paradigms for knowledge representation and use: symbolic (representational), connectionist, situated, and hybrid.
  • 3.1.2 Controversies  
    Open questions and controversies in AI&Cognition (semantics versus syntax, knowledge as mechanisms that knows, cognition without computation,...). Minsky, Simon, Newell, Marr, Searly, Maturana, Clancey, Brooks, Pylyshyn, Fodor, and more.
  • 3.1.3 Knowledge Modeling  
    Reusability of components in knowledge modeling (libraries of tasks, methods, inferences and roles). Ontologies (generic, domain specific, object oriented, methods, and tasks). Knowledge representation Methodologies and Knowledge edition tools.
From Natural to Artificial
3.2  Cognitive Inspiration in AI&KE
  • 3.2.1 Synthetic cognition  
    Bio-inspired modeling of cognitive tasks. Perception, decision-making, planning and control. Biologically plausible (user sensitive) man-machine interfaces. Natural language programming attempts. Social organizations, distributed AI, and multi-agent systems.
  • 3.2.2 Applications  
    Bio-inspired solutions to engineering, computational and social problems in different application domain:
    1. Medicine. Image understanding. KBS and ANN for diagnoses, therapy planning, and patient follow-up. Telemedicine.
    2. Robotic paradigms. Dynamic vision. Path planning, map building, and behavior based navigation methods. Anthropomorphic robots.
    3. Health biotechnology. Bio-inspired solutions for sustainable growth and development.
    4. Other domains (surveillance and security systems, distance education, web, data mining and information retrieval, ...).