Biosystems and Bioinspired Systems

  • Octavian Iordache
Part of the Understanding Complex Systems book series (UCS)


Artificial genetic codes, neural networks and neural codes are presented as theoretical frames for evolutionary computation and biomimetic devices.

Models for genetic code evolution offer suggestions for chemical and biochemical inspired computations as for instance artificial chemistry or chemical programming.

Neural networks architecture issues require evolvability as outlined by growing neural nets or by protein based neural networks.

The significance of neural coding, symbolic connectionist hybrids, neural binding, temporal synchrony studies for unconventional computing and neurocognitive devices is highlighted.

Evolutionary circuits based on electrochemical filaments are proposed. The perspectives of evolvable circuits based on bio-molecules properties, are evaluated.

Case studies show how technological innovation should find the right moment to free the artificial system designer from the detailed experimental data of real systems.


Neural Network Genetic Code Evolutionary Computation Categorical Product Evolvable Circuit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  • Octavian Iordache

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