#Neuromorphic computing with linear wave scattering technology revolutionized. #Innovation

Fully nonlinear neuromorphic computing with linear wave scattering

The content discusses a neuromorphic system based on a layered architecture and coupled mode theory. It explains the evolution equations for complex field amplitudes of neuron modes and the scattering matrix. The system is trained on a digit recognition task and achieves high accuracy. The architecture is compared to conventional classifiers like linear classifiers, ANNs, and CNNs on the Fashion-MNIST dataset. Hyperparameters such as the number of neuron modes per layer, the number of layers, the input replication, and the intrinsic decay rate are discussed. The system’s performance is affected by these hyperparameters, and optimal values are determined based on the task complexity. The use of Adam optimization is also explained, with observations on its stability during training. The study provides insights into the design and optimization of neuromorphic systems for image classification tasks, showcasing the potential benefits and challenges associated with these systems.

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