SynCx outperforms current state-of-the-art unsupervised synchrony-based models on standard multi-object datasets while using between 6-23x fewer model parameters compared to the baseline models. 8/x

SynCx processes complex-valued inputs at every layer using complex-valued weights. It is trained to reconstruct the input image at every iteration using the output magnitudes. Output phases are fed back as input to the next step with input magnitudes clamped to the image. 6/x

We argue for the importance of iterative computation (recurrence) and complex-valued weights to achieve phase synchronization in activations. To build some intuition look at the 3 shapes (T, H, and overlapping squares) made of horizontal and vertical bars. 2/x