We propose a novel, brain-inspired deep neural network model known as the
Deep Oscillatory Neural Network (DONN). Deep neural networks like the Recurrent
Neural Networks indeed possess sequence processing capabilities but the
internal states of the network are not designed to exhibit brain-like
oscillatory activity. With this motivation, the DONN is designed to have
oscillatory internal dynamics. Neurons of the DONN are either nonlinear neural
oscillators or traditional neurons with sigmoidal or ReLU activation. The
neural oscillator used in the model is the Hopf oscillator, with the dynamics
described in the complex domain. Input can be presented to the neural
oscillator in three possible modes. The sigmoid and ReLU neurons also use
complex-valued extensions. All the weight stages are also complex-valued.
Training follows the general principle of weight change by minimizing the
output error and therefore has an overall resemblance to complex
backpropagation. A generalization of DONN to convolutional networks known as
the Oscillatory Convolutional Neural Network is also proposed. The two proposed
oscillatory networks are applied to a variety of benchmark problems in signal
and image/video processing. The performance of the proposed models is either
comparable or superior to published results on the same data sets.

我们提出了一种新颖的、受大脑启发的深度神经网络模型，称为深层振荡神经网络 (DONN)。DONN 具有振荡内部动态，可以在信号和图像 / 视频处理的多个基准问题上提供与已发表结果相当或更好的性能。