Bio-inspired computing tissues: towards machines that evolve, grow, and learn.

On the State of the Art of POEtic Machines

Biological systems grow, live, adapt, and reproduce, characteristics that are not truly encompassed by any existing computing system. If one considers life on Earth since its very beginning, three levels of organization can be distinguished: phylogeny, ontogeny, and epigenesis. Machines that combine evolutionary mechanisms (Phylogeny), developmental processes (Ontogeny), and learning algorithms...

Bio-inspired Computing Machines with Self-repair Mechanisms

Developmental biology requires three principles of organization characteristic of living organisms: multicellular architecture, cellular division, and cellular differentiation. Implemented in silicon according to these principles, new computing machines become able to grow, to self-replicate, and to self-repair. The introduction of a new algorithm for cellular division, the so-called Tom Thumb ...

Architectures and design methodologies for bio-inspired computing machines

The evolution of parallel cellular machines: toward evolware.

The historical idea of evolving machines has recently resurfaced as the nascent field of bio-inspired systems and evolvable hardware. This paper describes the cellular programming approach used to evolve parallel cellular machines, presenting its application to six computational problems: density, synchronization, ordering, boundary computation, thinning and random number generation. Our result...

Concept Learning in Neuromorphic Vision Systems: What Can We Learn from Insects?

Vision systems that enable collision avoidance, localization and navigation in complex and uncertain environments are common in biology, but are extremely challenging to mimic in artificial electronic systems, in particular when size and power limitations apply. The development of neuromorphic electronic systems implementing models of biological sensory-motor systems in silicon is one promising...