  Backpropagation is the default algorithm for training deep neural networks
due to its simplicity, efficiency and high convergence rate. However, its
requirements make it impossible to be implemented in a human brain. In recent
years, more biologically plausible learning methods have been proposed. Some of
these methods can match backpropagation accuracy, and simultaneously provide
other extra benefits such as faster training on specialized hardware (e.g.,
ASICs) or higher robustness against adversarial attacks. While the interest in
the field is growing, there is a necessity for open-source libraries and
toolkits to foster research and benchmark algorithms. In this paper, we present
BioTorch, a software framework to create, train, and benchmark biologically
motivated neural networks. In addition, we investigate the performance of
several feedback alignment methods proposed in the literature, thereby
unveiling the importance of the forward and backward weight initialization and
optimizer choice. Finally, we provide a novel robustness study of these methods
against state-of-the-art white and black-box adversarial attacks.
