We shall use data from neuroanatomy and neurophysiology as a guide for the fabrication of deterministic and complex self-assembled networks of polymeric non linear elements with adaptive properties. The main objective is the realization of a new technology for the production of functional molecular assemblies, which can perform advanced tasks involving learning and decision making, and which can be tailored down to the nanoscale. The polymer network shall be prepared using molecular deposition and self assembly techniques in two and three dimensions. Electron beams shall be used for microelectrode configurations and for sample modification. Non linear elements will be provided by Schottky junctions, functionalized gold nanoparticles or molecular heterojunctions, which will be statistically dispersed in the matrix, to mimic the synaptic and neuronal distribution in biological systems as obtained from neuroanatomical data. We shall start with polyaniline embedded in ionic polyethyleneoxide, but other polymeric systems will be explored. The polymers will be functionalized to influence the deposition or self-assembly processes. To train the network we shall use mainly electrochemical modification of the polymer conductivity, for which we have already demonstrated the basic functional behaviour. . We shall monitor the network transfer function, for different types of signal input, including signal dependent noise. Artificial Intelligence algorithms and specifically developed statistical correlation techniques shall be used throughout. Upon success, the data shall be compared and connected to electrophysiological data obtained for brain systems of different complexity: first the simpler and more deterministic case of the pond snail and subsequently in the far more complex statistically distributed cases of cognitive processes in the cerebral cortex of the mammalian brain.