Putting a bug in ML: The moth olfactory network learns to read MNIST

We seek to (i) characterize the learning architectures exploited in biological neural networks for training on very few samples, and (ii) port these algorithmic structures to a machine learning context. The Moth Olfactory Network is among the simplest biological neural systems that can learn, and its architecture includes key structural elements widespread in biological neural nets, such as cascaded networks, competitive inhibition, high intrinsic noise, sparsity, reward mechanisms, and Hebbian plasticity. The interactions of these structural elements play a critical enabling role in rapid learning. We assign a computational model of the Moth Olfactory Network the task of learning to read the MNIST digits. This model, MothNet, is closely aligned with the moth's known biophysics and with in vivo electrode data, including data collected from moths learning new odors. We show that MothNet successfully learns to read given very few training samples (1 to 20 samples per class). In this few-samples regime, it substantially outperforms standard machine learning methods such as nearest-neighbors, support-vector machines, and convolutional neural networks (CNNs). The MothNet architecture illustrates how our proposed algorithmic structures, derived from biological brains, can be used to build alternative deep neural nets (DNNs) that may potentially avoid some of DNNs current learning rate limitations. This novel, bio-inspired neural network architecture offers a valuable complementary approach to DNN design.