Abstract
Transistor-based chaotic oscillators are known to realize highly diverse dynamics despite having elementary circuit topologies. This work investigates, numerically and experimentally using a ring network, a recently-introduced dual-transistor circuit that generates neural-like spike trains. A multitude of non-trivial effects are observed as a function of the supply voltage and coupling strength, including pattern formation under incomplete synchronization and sensitivity to additional long-distance links. Globally-applied noise exerts a synchronizing effect that interacts with the other control parameters. When the network is partitioned in halves at different levels of granularity, their interplay gives rise to adversarial route-to-synchronization phenomena. These results highlight the generative ability of this circuit and motivate its consideration towards the future realization of physical reservoirs.
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