An in-silico Neural Model of Dynamic Routing through Neuronal Coherence

We describe a neurobiologically plausible model to implement dynamic routing using the concept of neuronal communication through neuronal coherence. The model has a three-tier architecture: a raw input tier, a routing control tier, and an invariant output tier. The correct mapping between input and output tiers is re- alized by an appropriate alignment of the phases of their respective background oscillations by the routing control units. We present an example architecture, im- plemented on a neuromorphic chip, that is able to achieve circular-shift invariance. A simple extension to our model can accomplish circular-shift dynamic routing with only O(N) connections, compared to O(N 2) connections required by tradi- tional models.