Design of Oscillatory Neural Networks by Machine Learning

Understand

We demonstrate the utility of machine learning algorithms for the design of Oscillatory Neural Networks (ONNs).

• After constructing a circuit model of the oscillators in a machine-learning-enabled simulator and performing Backpropagation through time (BPTT) for determining the coupling resistances between the ring oscillators, we show the design of associative memories and multi-layered ONN classifiers.
• The machine-learning-designed ONNs show superior performance compared to other design methods (such as Hebbian learning) and they also enable significant simplifications in the circuit topology.
• We demonstrate the design of multi-layered ONNs that show superior performance compared to single-layer ones.