Towards A Self-Organizing Neuronal Network Based on Guided Axon-Growth

Axon growth and guidance play a key role in the ontogeny of non-artificial neuronal networks. In particular, they are natural self-organization mechanisms that are often neglected in many electrical realizations of neuronal networks. To compensate the lack of a missing circuit realization for guided axon-growth, we synthesize an electrical circuit implementing this mechanism. Here, the circuit mimics the axon’s ability to grow in dependence of the concentration gradient of so-called guidance cues. Our circuit synthesis is based on the wave digital modeling approach, where we first derive an adequate wave flow diagram and subsequently deduce the corresponding electrical circuit. The resulting circuit exploits memristive Jaumann structures with memsensors for implementing the gradient dependent growth-mechanism. Emulation results of a $10 \times 10$ concentration plane reveal the circuit to exhibit a form of unsupervised decision-making, where it selects the path with the highest concentration gradient.