  This study proposes a novel biologically-motivated learning method for deep
convolutional neural networks (CNNs). The combination of CNNs and back
propagation (BP) learning is the most powerful method in recent machine
learning regimes. However, it requires large labeled data for training, and
this requirement can occasionally become a barrier for real world applications.
To address this problem and utilize unlabeled data, I propose to introduce
unsupervised competitive learning which only requires forward propagating
signals as a pre-training method for CNNs. The method was evaluated by image
discrimination tasks using MNIST, CIFAR-10, and ImageNet datasets, and it
achieved a state-of-the-art performance as a biologically-motivated method in
the ImageNet experiment. The results suggested that the method enables
higher-level learning representations solely from forward propagating signals
without a backward error signal for the learning of convolutional layers. The
proposed method could be useful for a variety of poorly labeled data, for
example, time series or medical data.
