SCOPE AND TOPICS

To address the two questions, 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	From Natural to Artificial
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.	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, neuromorphic 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. Neurolinguistics. Neural Regeneration. tDCS.

2 Interplay at the Symbol Level
From Artificial to Natural	From Natural to Artificial
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.	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. Soft Computing. Deep Learning. Big Data. Affective Computing & Emotional Technologies.

3 Interplay at the Knowledge Level
From Artificial to Natural	From Natural to Artificial
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).	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. Natural Language processing. Distributed AI and Multi-agent systems. 3.2.2 Applications Bioinspired solutions to engineering, computational and social problems in different application domain: Biology & Medicine: Image understanding. KBS and ANN for diagnoses, therapy planning, and patient follow-up. Telemedicine, Health Economics. Precision Medicine. Medical Images. Robotic paradigms: Dynamic vision. Stereoscopic Vision. Path planning, map building, and behavior based navigation methods. Human-Robot Interaction, Social Robotics. Health biotechnology: Bio-inspired solutions for sustainable growth and development. Gerontechnology. Ambient Assisted Living. Other domains: Surveillance and security systems, Biometrics, Speech Processing & Analysis, distance education, web, data mining and information retrieval, Probabilistic Decision Making...