  Biological neural networks can perform complex computations to predict their
environment, far above the limited predictive capabilities of individual
neurons. While conventional approaches to understanding these computations
often focus on isolating the contributions of single neurons, here we argue
that a deeper understanding requires considering emergent dynamics - dynamics
that make the whole system "more than the sum of its parts". Specifically, we
examine the relationship between prediction performance and emergence by
leveraging recent quantitative metrics of emergence, derived from Partial
Information Decomposition, and by modelling the prediction of environmental
dynamics in a bio-inspired computational framework known as reservoir
computing. Notably, we reveal a bidirectional coupling between prediction
performance and emergence, which generalises across task environments and
reservoir network topologies, and is recapitulated by three key results: 1)
Optimising hyperparameters for performance enhances emergent dynamics, and vice
versa; 2) Emergent dynamics represent a near sufficient criterion for
prediction success in all task environments, and an almost necessary criterion
in most environments; 3) Training reservoir computers on larger datasets
results in stronger emergent dynamics, which contain task-relevant information
crucial for performance. Overall, our study points to a pivotal role of
emergence in facilitating environmental predictions in a bio-inspired
computational architecture.
