RT Journal Article
SR Electronic
T1 Computational capability of ecological dynamics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.09.15.460556
DO 10.1101/2021.09.15.460556
A1 Masayuki Ushio
A1 Kazufumi Watanabe
A1 Yasuhiro Fukuda
A1 Yuji Tokudome
A1 Kohei Nakajima
YR 2021
UL http://biorxiv.org/content/early/2021/09/17/2021.09.15.460556.abstract
AB Ecological dynamics is driven by an ecological network consisting of complex interactions. Information processing capability of artificial networks has been exploited as a computational resource, yet whether an ecological network possesses a computational capability and how we can exploit it remain unclear. Here, we show that ecological dynamics can be exploited as a computational resource. We call this approach “Ecological Reservoir Computing” (ERC) and developed two types of ERC. In silico ERC reconstructs ecological dynamics from empirical time series and uses simulated system responses as reservoir states, which predicts near future of chaotic dynamics and emulates nonlinear dynamics. The real-time ERC uses population dynamics of a unicellular organism, Tetrahymena thermophila. Medium temperature is an input signal and changes in population abundance are reservoir states. Intriguingly, the real-time ERC has necessary conditions for reservoir computing and is able to make near future predictions of model and empirical time series.Competing Interest StatementThe authors have declared no competing interest.