An embodied account of serial order: How instabilities drive sequence generation

• Published in: Neural networks Vol. 23; no. 10; pp. 1164 - 1179
• Main Authors: Sandamirskaya, Yulia, Schöner, Gregor
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.12.2010; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Attractors and instabilities; Cognition - physiology; Computer science; control theory; systems; Connectionism. Neural networks; Dynamic Field Theory; Embodiment; Exact sciences and technology; Humans; Learning - physiology; Nerve Net - physiology; Neural dynamics; Neural Networks (Computer); Sequence generation; Sequence generation; Attractors and instabilities; Dynamic Field Theory; Embodiment; Neural dynamics; Bifurcation; Cognition; Duration; Neural network; Distributed system; Attractor; Robotics; Cognitive theory; Sequencing
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2010.07.012

Learning and generating serially ordered sequences of actions is a core component of cognition both in organisms and in artificial cognitive systems. When these systems are embodied and situated in partially unknown environments, specific constraints arise for any neural mechanism of sequence generation. In particular, sequential action must resist fluctuating sensory information and be capable of generating sequences in which the individual actions may vary unpredictably in duration. We provide a solution to this problem within the framework of Dynamic Field Theory by proposing an architecture in which dynamic neural networks create stable states at each stage of a sequence. These neural attractors are destabilized in a cascade of bifurcations triggered by a neural representation of a condition of satisfaction for each action. We implement the architecture on a robotic vehicle in a color search task, demonstrating both sequence learning and sequence generation on the basis of low-level sensory information.