Complex computation from developmental priors

Artificial Intelligence (AI) research has provided key insights into the mechanics of learning complex tasks. However, AI models have long overlooked innateness: how strong pressures for survival lead to the encoding of complex behaviors in the nascent wiring of a brain. Although innate neural solutions have inspired AI approaches from layered architectures to ConvNets, the underlying neuroevolutionary search for novel heuristics has not been successfully systematized. In this manuscript, we examine how neurodevelopmental principles can inform the discovery of useful inductive biases. We begin by considering the weight matrix of a neural network to be emergent from well-studied rules of neuronal compatibility. Rather than updating the network's weights directly, we improve task fitness by updating the neurons' wiring rules, thereby mirroring evolutionary selection on brain development. We find that the resulting framework can not only achieve high performance on standard machine learning (ML) tasks, but does so with a fraction of the full network's parameters. Further, we show that developmentally-inspired techniques have higher and more stable performance on metalearning tasks than the standard models they encode. In summary, by introducing realistic developmental considerations into ML frameworks, we not only model the emergence of innate behaviors, but also define a discovery process for structures that promote complex computations.