Neural computation is considered here in the dual perspective of analysis (as science) and synthesis (as engineering). As a science of analysis, neural computation seeks to help neurology, brain theory, and cognitive psychology in the understanding of the functioning of the Nervous System by means of computational models of neurons, neural nets and subcellular processes, with the possibility of using electronics and computers as a "laboratory" in which cognitive processes can be simulated and hypothesis proven without having to act directly upon living beings.

As a synthesis engineering, neural computation seeks to complement the symbolic perspective of Artificial Intelligence (AI), using the biologically inspired models of distributed, self-programming and self-organizing networks, to solve those non-algorithmic problems of function approximation and pattern classification having to do with changing and only partially known environments. Fault tolerance and dynamic reconfiguration are other basic advantages of neural nets.

In the sea of meetings, congresses and workshops on ANN's, IWANN'97 focus on the three subjects that most concern us:

(1) The seeking of biologically inspired new models of local computation architectures and learning along with the organizational principles behind the complexity of intelligent behavior.

(2) The searching for some methodological contributions in the analysis and design of knowledge-based ANN's, instead of "blind nets", and in the reduction of the knowledge level to the sub-symbolic implementation level.

(3) The cooperation with symbolic AI, with the integration of connectionist and symbolic processing in hybrid and multi-strategy approaches for perception, decision and control tasks, as well as for case-based reasoning, concepts formation and learning.

To contribute to the posing and partial solving of these global topics, IWANN'97 offer a brain-storming interdisciplinary forum in advanced Neural Computation for scientists and engineers from biology neuroanatomy, computational neurophysiology, molecular biology, biophysics, linguistics, psychology, mathematics and physics, computer science, artificial intelligence, parallel computing, analog and digital electronics, advanced computer architectures, reverse engineering, cognitive sciences and all the concerned applied domains (sensory systems and signal processing, monitoring, diagnosis, classification and decision making, intelligent control and supervision, perceptual robotics and communication systems).

Contributions on the following and related topics are welcome.