Persistent Memory in Single Node Delay-Coupled Reservoir Computing

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Title: Persistent Memory in Single Node Delay-Coupled Reservoir Computing
Authors: Kovac, AndrĂ© David
Koall, Maximilian
Pipa, Gordon
Toutounji, Hazem
Abstract: Delays are ubiquitous in biological systems, ranging from genetic regulatory networks and synaptic conductances, to predator/pray population interactions. The evidence is mounting, not only to the presence of delays as physical constraints in signal propagation speed, but also to their functional role in providing dynamical diversity to the systems that comprise them. The latter observation in biological systems inspired the recent development of a computational architecture that harnesses this dynamical diversity, by delay-coupling a single nonlinear element to itself. This architecture is a particular realization of Reservoir Computing, where stimuli are injected into the system in time rather than in space as is the case with classical recurrent neural network realizations. This architecture also exhibits an internal memory which fades in time, an important prerequisite to the functioning of any reservoir computing device. However, fading memory is also a limitation to any computation that requires persistent storage. In order to overcome this limitation, the current work introduces an extended version to the single node Delay-Coupled Reservoir, that is based on trained linear feedback. We show by numerical simulations that adding task-specific linear feedback to the single node Delay-Coupled Reservoir extends the class of solvable tasks to those that require nonfading memory. We demonstrate, through several case studies, the ability of the extended system to carry out complex nonlinear computations that depend on past information, whereas the computational power of the system with fading memory alone quickly deteriorates. Our findings provide the theoretical basis for future physical realizations of a biologically-inspired ultrafast computing device with extended functionality.
Citations: PLoS ONE, Vol. 11, No. 10: e0165170, S. 1-15
URL: https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2017032715753
Subject Keywords: Memory; Teachers; Long-term memory; Signal filtering; Dynamical systems; Neural networks; Nonlinear dynamics; Signal processing; Neurons
Issue Date: 27-Mar-2017
License name: Namensnennung 4.0 International
License url: http://creativecommons.org/licenses/by/4.0/
Type of publication: Einzelbeitrag in einer wissenschaftlichen Zeitschrift [article]
Appears in Collections:FB08 - Hochschulschriften
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