  Recurrent networks are a special class of artificial neural systems that use
their internal states to perform computing tasks for machine learning. One of
its state-of-the-art developments, i.e. reservoir computing (RC), uses the
internal structure -- usually a static network with random structure -- to map
an input signal into a nonlinear dynamical system defined in a higher
dimensional space. Reservoirs are characterized by nonlinear interactions among
their units and their ability to store information through recurrent loops,
allowing to train artificial systems to learn task-specific dynamics. However,
it is fundamentally unknown how the random topology of the reservoir affects
the learning performance. Here, we fill this gap by considering a battery of
synthetic networks -- characterized by different topological features -- and 45
empirical connectomes -- sampled from brain regions of organisms belonging to 8
different species -- to build the reservoir and testing the learning
performance against a prediction task with a variety of complex input signals.
We find nontrivial correlations between RC performances and both the number of
nodes and rank of the covariance matrix of activation states, with performance
depending on the nature -- stochastic or deterministic -- of input signals.
Remarkably, the modularity and the link density of the reservoir are found to
affect RC performances: these results cannot be predicted by models only
accounting for simple topological features of the reservoir. Overall, our
findings highlight that the complex topological features characterizing
biophysical computing systems such as connectomes can be used to design
efficient bio-inspired artificial neural networks.
