Connectome-based reservoir computing with the conn2res toolbox

Abstract

The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses high-dimensional, nonlinear dynamical systems to perform computations and approximate cognitive functions. Here we present : an open-source Python toolbox for implementing biological neural networks as artificial neural networks. is modular, allowing arbitrary network architecture and dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from spiking neurons to memristive dynamics. The versatility of the toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.

Brain Network Modularity and Resilience Signaled by Betweenness Centrality Percolation Spiking

“…It is worth noting that while these terminal layers are interchangeable between input and output layers, the most important finding is the necessity of the transfer module to communicate between the two terminal modules. For example, this network configuration can then input into connectome-based computing software, such as conn2res [65], to simulate reservoir computing based on human fMRI. This neural network configuration thus enables the quantification of the processing power of the connectome network using clearly defined modules through percolation theory.…”