A moth brain learns to read MNIST

Charles B. Delahunt, J. Nathan Kutz

Abstract:

We seek to characterize the learning tools (ie algorithmic components) used in biological neural networks, in order to port them to the machine learning context. In particular we address the regime of very few training samples.
The Moth Olfactory Network is among the simplest biological neural systems that can learn. We assigned a computational model of the Moth Olfactory Network the task of classifying the MNIST digits. The moth brain successfully learned to read given very few training samples (1 to 20 samples per class). In this few-samples regime the moth brain substantially outperformed standard ML methods such as Nearest-neighbors, SVM, and CNN.
Our experiments elucidate biological mechanisms for fast learning that rely on cascaded networks, competitive inhibition, sparsity, and Hebbian plasticity. These biological algorithmic components represent a novel, alternative toolkit for building neural nets that may offer a valuable complement to standard neural nets.

TL;DR: A moth brain model can learn the MNIST digits, and out-performs ML methods in the few-training-samples regime.

Keywords: machine learning, neural nets, neuroscience, sparsity