Brain connectivity meets reservoir computing

The connectivity of Artificial Neural Networks (ANNs) is different from the one observed in Biological Neural Networks (BNNs). Can the wiring of actual brains help improve ANNs architectures? Can we learn from ANNs about what network features support computation in the brain when solving a task? At a meso/macro-scale level of the connectivity, ANNs' architectures are carefully engineered and such those design decisions have crucial importance in many recent performance improvements. On the other hand, BNNs exhibit complex emergent connectivity patterns at all scales. At the individual level, BNNs connectivity results from brain development and plasticity processes, while at the species level, adaptive reconfigurations during evolution also play a major role shaping connectivity. Ubiquitous features of brain connectivity have been identified in recent years, but their role in the brain's ability to perform concrete computations remains poorly understood. Computational neuroscience studies reveal the influence of specific brain connectivity features only on abstract dynamical properties, although the implications of real brain networks topologies on machine learning or cognitive tasks have been barely explored. Here we present a cross-species study with a hybrid approach integrating real brain connectomes and Bio-Echo State Networks, which we use to solve concrete memory tasks, allowing us to probe the potential computational implications of real brain connectivity patterns on task solving. We find results consistent across species and tasks, showing that biologically inspired networks perform as well as classical echo state networks, provided a minimum level of randomness and diversity of connections is allowed. We also present a framework, bio2art, to map and scale up real connectomes that can be integrated into recurrent ANNs. This approach also allows us to show the crucial importance of the diversity of interareal connectivity patterns, stressing the importance of stochastic processes determining neural networks connectivity in general. Author summary Artificial Neural Networks (ANNs) and Biological Neural Networks (BNNs) exhibit different connectivity patterns. ANNs' have tyically carefully hand-crafted architectures that play an important role in their performance. On the other hand, BNNs' wiring shows self-organized emergent patterns resulting from processes such as development and neuronal plasticity. Although ubiquitous properties of brain connectivity have beed identified and associated with abstract dynamical properties of the brain, the implications of real brain networks topologies on concrete machine learning tasks have been barely explored. The goal of this hybrid, cross-species study was to give a step in that direction by probing real brain connectomes on concrete machine learning tasks. Our approach integrates real brain connectomes and Bio-Echo State Networks, which we use to solve concrete memory tasks. To achieve that, we also present here a framework, bio2art, to map and scale up real connectomes that can be integrated into recurrent ANNs. We find results consistent across species and tasks, showing that biologically inspired networks perform as well as classical echo state networks, provided a minimum level of randomness and diversity of connections is allowed. Out findings stress the importance of stochasticity in neural networks connectivity, especially regarding the heterogeneity of interareal connectivity.