  Natural intelligences (NIs) thrive in a dynamic world - they learn quickly,
sometimes with only a few samples. In contrast, artificial intelligences (AIs)
typically learn with a prohibitive number of training samples and computational
power. What design principle difference between NI and AI could contribute to
such a discrepancy? Here, we investigate the role of weight polarity:
development processes initialize NIs with advantageous polarity configurations;
as NIs grow and learn, synapse magnitudes update, yet polarities are largely
kept unchanged. We demonstrate with simulation and image classification tasks
that if weight polarities are adequately set a priori, then networks learn with
less time and data. We also explicitly illustrate situations in which a priori
setting the weight polarities is disadvantageous for networks. Our work
illustrates the value of weight polarities from the perspective of statistical
and computational efficiency during learning.
