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

IWINAC2017: Topics (Extended version)

To address the two questions exposed in the scope of IWINAC2017, we will make use of a wide and comprehensive view of the Computational Paradigm (CP) that first consider three levels of description for each calculus (physical mechanisms, symbols and knowledge) and then distinguish between two domains of description (the level "own" domain and the domain of the external observer).

This wider view of the CP allows us more elbow room to accommodate the results of the interplay between Nature and Computation. The IWINAC forum becomes thus a methodological approximation (set of intentions, questions, experiments, models, algorithms, mechanisms, explanation procedures, and engineering and computational methods) to the natural and artificial perspectives of the mind embodiments problem, both in humans and in artifacts.

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. Nanotechnology.
  • 1.2.2 Non-conventional approaches to Computation
    Biomaterials. DNA, cellular and membrane computing, P. Systems, Chemical and Quantum Computing.
  • 1.2.3 Sensory and motor prostheses
    Signal processing, artificial cochlea, audio-tactile vision substitution. Artificial sensory and motor systems for handicapped people. Inter-sensory transfer and sensory plasticity.

2 Interplay at the Symbol Level
From Artificial to Natural
2.1 Neural Coding and Neuro-informatics
  • 2.1.1 Symbols
    Kinds of Neural Coding. Anatomical Basis (regularities, synchronization, resonance, dynamics binding and other potential mechanisms underlying neural coding). Grounded Symbols and Sensorimotor categories.
  • 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 Bioinspired Programming Strategies
  • 2.2.1 Behavior based computational methods
    Reactive mechanisms. Self-organizing optimization. Collective emergent behavior (ant colonies). Ethology and Artificial Life.
  • 2.2.2 Evolutionary computation
    Genetic algorithms, evolutionary strategies, evolutionary programming and genetic programming. Macro-evolution and the interplay between evolution and learning. Meta-heuristics.
  • 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 Foundations and approaches to the study of Cognition
  • 3.1.1 AI&KE
    Use of AI&KE concepts, tools, and methods in the modeling of cognitive processes, and of individual and social behavior. Contribution to the debate on AI paradigms: symbolic (representational), connectionist, situated, and hybrid (soft computing).
  • 3.1.2 Controversies on the Philosophical Foundations of AI
    Open questions and controversies in AI&Cognition (mechanicist physicalism, emergentist thought...). Minsky, Simon, Newell, Marr, Searle, Maturana, Varela, Dreyfus, Edelman, Clancey, Brooks, Pylyshyn, Fodor, Zubiri and more.
  • 3.1.3 Computational Modeling of Cognitive Tasks
    Learning (associative, reinforcement, insight), Memory (short and long term, Semantic, Episodic...), Perception of different modalities and action (reactive, goal-directed, adaptive and intentional), Attention, Natural Language and Consciousness. Use of AI and KE tools and techniques in cognitive models (rules, frames, logic and causal networks).
From Natural to Artificial
3.2 Bioinspired Engineering AI&KE
  • 3.2.1 Knowledge Modeling and Formalization
    Bioinspired Knowledge representation Methods, Artificial Immune Systems. Reusability of Components. Ontologies. Symbolic, Neuronal and Bayesian Problem Solving Methods. Neural and Probabilistic Graphical Models Methods. Modeling and Formalization languages. Distributed AI and Multi-agent systems.
  • 3.2.2 Applications
    Bioinspired solutions to engineering, computational and social problems in different application domain:
    1. Biology & Medicine: Image understanding. KBS and ANN for diagnoses, therapy planning, and patient follow-up. Telemedicine, Health Economics.
    2. Robotic paradigms: Dynamic vision. Stereoscopic 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, biometrics, distance education, web, data mining and information retrieval, Probabilistic Decision Makinng...