title: Model for a flexible motor memory based on a self-active recurrent neural network.
abstract: Using recent recurrent network architecture based on the reservoir computing approach, we propose and numerically simulate a model that is focused on the aspects of a flexible motor memory for the storage of elementary movement patterns into the synaptic weights of a neural network, so that the patterns can be retrieved at any time by simple static commands. The resulting motor memory is flexible in that it is capable to continuously modulate the stored patterns. The modulation consists in an approximately linear inter- and extrapolation, generating a large space of possible movements that have not been learned before. A recurrent network of thousand neurons is trained in a manner that corresponds to a realistic exercising scenario, with experimentally measured muscular activations and with kinetic data representing proprioceptive feedback. The network is "self-active" in that it maintains recurrent flow of activation even in the absence of input, a feature that resembles the "resting-state activity" found in the human and animal brain. The model involves the concept of "neural outsourcing" which amounts to the permanent shifting of computational load from higher to lower-level neural structures, which might help to explain why humans are able to execute learned skills in a fluent and flexible manner without the need for attention to the details of the movement.
authors.affiliation: Motion Science, University of Münster, Horstmarer Landweg 62b, 48149 Münster, Germany; Center for Nonlinear Science (CeNoS), 48149 Münster, Germany. Electronic address: mail@kim-bostroem.de.
authors.forename: Kim Joris
authors.initials: KJ
authors.lastname: Boström
authors.forename: Heiko
authors.initials: H
authors.lastname: Wagner
authors.forename: Markus
authors.initials: M
authors.lastname: Prieske
authors.forename: Marc
authors.initials: M
authors.lastname: de Lussanet
mesh_terms: D000818:Animals; D002490:Central Nervous System / Q000502:physiology; D004576:Electromyography; D015444:Exercise / Q000502:physiology; D056228:Feedback, Sensory / Q000502:physiology; D007839:Functional Laterality / Q000502:physiology; D006801:Humans; D008568:Memory / Q000502:physiology*; D009048:Motor Skills / Q000502:physiology; D009068:Movement / Q000502:physiology*; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D011434:Proprioception / Q000502:physiology
keywords: 2330; 2343; 4160; Motor control; Motor learning; Motor memory; Neural networks; Reservoir computing