Multifunctionality in a Connectome-Based Reservoir Computer
Jacob Morra Andrew Flynn Andreas Amann Mark Daley
Multifunctionality describes the capacity for a neural network to perform
multiple mutually exclusive tasks without altering its network connections; and
is an emerging area of interest in the reservoir computing machine learning
paradigm. Multifunctionality has been observed in the brains of humans and
other animals: particularly, in the lateral horn of the fruit fly. In this
work, we transplant the connectome of the fruit fly lateral horn to a reservoir
computer (RC), and investigate the extent to which this 'fruit fly RC' (FFRC)
exhibits multifunctionality using the 'seeing double' problem as a benchmark
test. We furthermore explore the dynamics of how this FFRC achieves
multifunctionality while varying the network's spectral radius. Compared to the
widely-used Erd\"os-Renyi Reservoir Computer (ERRC), we report that the FFRC
exhibits a greater capacity for multifunctionality; is multifunctional across a
broader hyperparameter range; and solves the seeing double problem far beyond
the previously observed spectral radius limit, wherein the ERRC's dynamics
become chaotic.