Artificial Kuramoto Oscillatory Neurons

We build a new neural network architecture that has iterative modules that update N-dimensional oscillatory neurons via a generalization of the well-known non-linear dynamical model called the Kuramoto model (Kuramoto, 1984). The Kuramoto model describes the synchronization of oscillators; each Kuramoto update applies forces to connected oscillators, encouraging them to become aligned or anti-aligned. This process is similar to binding in neuroscience and can be understood as distributed and continuous clustering. Thus, networks with this mechanism tend to compress their representations via synchronization. We incorporate the Kuramoto model into an artificial neural network, by applying the differential equation that describes the Kuramoto model to each individual neuron. The resulting artificial Kuramoto oscillatory neurons (AKOrN) can be combined with layer architectures such as fully connected layers, convolutions, and attention mechanisms. We explore the capabilities of AKOrN and find that its neuronal mechanism drastically changes the behavior of the network. AKOrN strongly binds object features with competitive performance to slot-based models in object discovery, enhances the reasoning capability of self-attention, and increases robustness against random, adversarial, and natural perturbations with surprisingly good calibration.