Accessing the topological properties of human brain functional sub-circuits in Echo State Networks
Journal:
arXiv
Published Date:
Dec 19, 2024
Abstract
Recent years have witnessed an emerging trend in neuromorphic computing that
centers around the use of brain connectomics as a blueprint for artificial
neural networks. Connectomics-based neuromorphic computing has primarily
focused on embedding human brain large-scale structural connectomes (SCs), as
estimated from diffusion Magnetic Resonance Imaging (dMRI) modality, to
echo-state networks (ESNs). A critical step in ESN embedding requires
pre-determined read-in and read-out layers constructed by the induced subgraphs
of the embedded reservoir. As \textit{a priori} set of functional sub-circuits
are derived from functional MRI (fMRI) modality, it is unknown, till this
point, whether the embedding of fMRI-induced sub-circuits/networks onto SCs is
well justified from the neuro-physiological perspective and ESN performance
across a variety of tasks. This paper proposes a pipeline to implement and
evaluate ESNs with various embedded topologies and processing/memorization
tasks. To this end, we showed that different performance optimums highly depend
on the neuro-physiological characteristics of these pre-determined fMRI-induced
sub-circuits. In general, fMRI-induced sub-circuit-embedded ESN outperforms
simple bipartite and various null models with feed-forward properties commonly
seen in MLP for different tasks and reservoir criticality conditions. We
provided a thorough analysis of the topological properties of pre-determined
fMRI-induced sub-circuits and highlighted their graph-theoretical properties
that play significant roles in determining ESN performance.
