Cross-species connectome comparisons reveal the network attributes of memory capacity and time series prediction

Abstract

The brains connectome provides a powerful blueprint for designing efficient neural networks, yet the impact of incorporating its intricate, non-random architecture into machine learning models remains underexplored. Here, we integrate empirical structural connectomes from four model species--fruit fly, mouse, rat, and macaque--as the recurrent layer in echo state networks (ESNs). We demonstrate that biologically realized networks, particularly the macaque connectome, achieve superior performance in chaotic time series prediction and exhibit higher memory capacity compared to randomly shuffled controls. This computational advantage correlates with small-world topology, which scales with phylogenetic level. Crucially, we identify that weakly connected but highly central nodes are essential for optimal network dynamics; their targeted perturbation significantly degrades performance. Furthermore, functional connectomes from Alzheimers disease patients show computational deficits resembling those induced by weak-tie disruption in healthy networks. Our findings establish that evolved connectome topology is fundamental to efficient information processing, providing key principles for bio-inspired artificial intelligence.